Dissertations / Theses on the topic 'Model Updating, Structural Health Monitoring'
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Kodikara, Kodikara Arachchige Tharindu Lakshitha. "Structural health monitoring through advanced model updating incorporating uncertainties." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/110811/1/Kodikara%20Arachchige%20Tharindu%20Lakshitha_Kodikara_Thesis.pdf.
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This research developed comprehensive model updating systems for real structures including a hybrid approach which enhanced existing deterministic model updating techniques by providing measures to incorporate uncertainties in a computationally efficient way compared to probabilistic model updating approaches. Further, utilizing the developed hybrid approach a methodology was developed to assess the deterioration of reinforced concrete buildings under serviceability loading conditions. The developed methodologies in the research were successfully validated utilizing two real benchmark structures at Queensland University of Technology equipped with continuous monitoring systems.
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Moravej, Hans. "Vibration-based probabilistic model updating of civil structures using structural health monitoring techniques." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/203653/1/Hans%20Moravej%20Thesis.pdf.
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Information extracted from monitored data is susceptible to uncertainties and not reliable to be used for structural investigations. Finite element model updating (FEMU) is an accredited framework which aims to improve the accuracy of FEMs of real structures. However, FEMU faces barriers to achieving efficiency and addressing uncertainties. This study aims to develop a probabilistic approach based on Modular Bayesian approach (MBA) to address challenges in the application of FEMU. Moreover, this research proposes an integration between MBA and structural reliability analysis to assess the performance of structures during their lifespan. The feasibility of approach is demonstrated on two structures.
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Smith, Chandler B. "Sparsity Constrained Inverse Problems - Application to Vibration-based Structural Health Monitoring." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1143.
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Vibration-based structural health monitoring (SHM) seeks to detect, quantify, locate, and prognosticate damage by processing vibration signals measured while the structure is operational. The basic premise of vibration-based SHM is that damage will affect the stiffness, mass or energy dissipation properties of the structure and in turn alter its measured dynamic characteristics. In order to make SHM a practical technology it is necessary to perform damage assessment using only a minimum number of permanently installed sensors. Deducing damage at unmeasured regions of the structural domain requires solving an inverse problem that is underdetermined and(or) ill-conditioned. In addition, the effects of local damage on global vibration response may be overshadowed by the effects of modelling error, environmental changes, sensor noise, and unmeasured excitation. These theoretical and practical challenges render the damage identification inverse problem ill-posed, and in some cases unsolvable with conventional inverse methods.
This dissertation proposes and tests a novel interpretation of the damage identification inverse problem. Since damage is inherently local and strictly reduces stiffness and(or) mass, the underdetermined inverse problem can be made uniquely solvable by either imposing sparsity or non-negativity on the solution space. The goal of this research is to leverage this concept in order to prove that damage identification can be performed in practical applications using significantly less measurements than conventional inverse methods require. This dissertation investigates two sparsity inducing methods, L1-norm optimization and the non-negative least squares, in their application to identifying damage from eigenvalues, a minimal sensor-based feature that results in an underdetermined inverse problem. This work presents necessary conditions for solution uniqueness and a method to quantify the bounds on the non-unique solution space. The proposed methods are investigated using a wide range of numerical simulations and validated using a four-story lab-scale frame and a full-scale 17 m long aluminum truss. The findings of this study suggest that leveraging the attributes of both L1-norm optimization and non-negative constrained least squares can provide significant improvement over their standalone applications and over other existing methods of damage detection.
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Zolghadri, Navid. "Short and Long-Term Structural Health Monitoring of Highway Bridges." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/5626.
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Structural Health Monitoring (SHM) is a promising tool for condition assessment of bridge structures. SHM of bridges can be performed for different purposes in long or short-term. A few aspects of short- and long-term monitoring of highway bridges are addressed in this research.
Without quantifying environmental effects, applying vibration-based damage detection techniques may result in false damage identification. As part of a long-term monitoring project, the effect of temperature on vibrational characteristics of two continuously monitored bridges are studied. Natural frequencies of the structures are identified from ambient vibration data using the Natural Excitation Technique (NExT) along with the Eigen System Realization (ERA) algorithm. Variability of identified natural frequencies is investigated based on statistical properties of identified frequencies. Different statistical models are tested and the most accurate model is selected to remove the effect of temperature from the identified frequencies. After removing temperature effects, different damage cases are simulated on calibrated finite-element models. Comparing the effect of simulated damages on natural frequencies showed what levels of damage could be detected with this method.
Evaluating traffic loads can be helpful to different areas including bridge design and assessment, pavement design and maintenance, fatigue analysis, economic studies and enforcement of legal weight limits. In this study, feasibility of using a single-span bridge as a weigh-in-motion tool to quantify the gross vehicle weights (GVW) of trucks is studied. As part of a short-term monitoring project, this bridge was subjected to four sets of high speed, live-load tests. Measured strain data are used to implement bridge weigh-in-motion (B-WIM) algorithms and calculate the corresponding velocities and GVWs. A comparison is made between calculated and static weights, and furthermore, between supposed speeds and estimated speeds of the trucks.
Vibration-based techniques that use finite-element (FE) model updating for SHM of bridges are common for infrastructure applications. This study presents the application of both static and dynamic-based FE model updating of a full scale bridge. Both dynamic and live-load testing were conducted on this bridge and vibration, strain, and deflections were measured at different locations. A FE model is calibrated using different error functions. This model could capture both global and local response of the structure and the performance of the updated model is validated with part of the collected measurements that were not included in the calibration process.
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Lee, Soon Gie. "Hybrid Damage Identification Based on Wavelet Transform and Finite Element Model Updating." University of Akron / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=akron1333676433.
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AREZZO, DAVIDE. "An innovative framework for Vibration Based Structural Health Monitoring of buildings through Artificial Intelligence approaches ." Doctoral thesis, Università Politecnica delle Marche, 2022. http://hdl.handle.net/11566/299822.
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Il monitoraggio della salute strutturale consiste nell'identificare tutti quei processi volti a valutare la sicurezza di una struttura. Questi processi hanno trovato la loro prima applicazione nel campo dell'ingegneria aerospaziale e meccanica al fine di valutare le prestazioni e l'insorgenza di danni in componenti meccanici di veicoli e macchinari industriali rotanti. Col tempo, la necessità di valutare lo stato di salute delle strutture ha portato all'utilizzo di queste tecniche anche nel campo dell'ingegneria civile, in particolare del monitoraggio basato su misure di vibrazione ambientale attraverso l'applicazione di tecniche di Operational Modal Analysis (OMA). Queste tecniche sono ben consolidate, basate su solide basi teoriche, e implementate in numerosi framework per il monitoraggio della salute strutturale. Tuttavia, la definizione e l'implementazione di un monitoraggio dinamico efficace in grado di rilevare i danni richiede un alto grado di multidisciplinarietà e il contributo di specialisti provenienti da diversi campi, vale a dire, misure meccaniche, informatica, ingegneria elettronica, identificazione dinamica, ingegneria strutturale, data science. Durante le attività di dottorato è stato sviluppato un framework per il sistema di monitoraggio della salute strutturale basato sulle misure vibrazionali (VB-SHM) in tutte le sue parti, cercando di raggiungere la replicabilità del sistema e la sua efficacia nel tracciare correttamente le condizioni di salute della struttura nel tempo. La replicabilità è fondamentale per promuovere la più ampia diffusione possibile di questo tipo di monitoraggio. Il framework è stato sviluppato a partire dai risultati ottenuti da tre principali casi studio monitorati durante le attività di dottorato. Il caso studio della Chiesa di Santa Maria in Via a Camerino affronta il problema dell'identificazione dinamica, della calibrazione del modello e del posizionamento ottimale dei sensori. Vista la complessità del modello ad elementi finiti, la sua calibrazione è stata effettuata con l'aiuto dell'algoritmo Particle Swarm Optimization. In seguito, vengono presentati i risultati del monitoraggio di un edificio scolastico a Camerino monitorato durante la sequenza sismica del 2016. Durante tutto il periodo di monitoraggio è stata registrata la risposta dell'edificio a diversi terremoti di bassa e media intensità. L'edificio, nonostante l'assenza di danni, ha mostrato un comportamento dinamico tempo variante rendendo difficile la tracciabilità delle frequenze durante la risposta sismica. Applicando una procedura di linearizzazione, è stato possibile tenere traccia delle frequenze anche durante la risposta sismica dell’edificio. Infine, vengono riportati i risultati del monitoraggio della Torre di Ingegneria dell'Università Politecnica delle Marche. La Torre è stata monitorata dal 2017 e, seppur con alcune interruzioni, ha permesso di osservare una marcata dipendenza delle sue frequenze proprie dai parametri ambientali, in particolare temperatura e vento. Questi effetti sono stati efficacemente depurati attraverso l'implementazione di una rete neurale artificiale.Structural health monitoring consists of identifying all those processes aimed at assessing the safety of a structure. These processes found their first application in the field of aerospace and mechanical engineering in order to assess the performance and occurrence of damage in mechanical components of vehicles and rotating industrial machinery. Over time, the need to assess the health status of structures has also led to the use of these techniques in the field of civil engineering, in particular vibration-based monitoring through the application of Operational Modal Analysis (OMA) techniques. These techniques are well established, based on solid theoretical foundations, and implemented in numerous frameworks for structural health monitoring. However, the definition and implementation of an effective dynamic monitoring capable to detect damage requires a high degree of multi-disciplinary and the contribution of specialists from different fields, i.e., measurement engineering, computer science, electronic engineering, dynamic identification, structural engineering, data science. During the PhD activities an effort have been made for the development of a framework for Vibration-Based Structural Health Monitoring system (VB-SHM) in all its part, attempting to achieve replicability of the system and its effectiveness in correctly tracking the health conditions of the structure over time. Replicability is crucial to promote the widest possible spread of this kind of monitoring. The framework has been developed starting from results obtained by three main case studies monitored during the PhD activities. The case study of the Santa Maria in Via Church in Camerino deal with the problem of dynamic identification, model updating and optimal sensor placement. Due to the complexity of the finite element model, model updating has been carried out with the aid of Particle Swarm Optimization algorithm. Thereafter, monitoring results of the r.c. school building in Camerino monitored during the 2016 seismic sequence are presented. Throughout the monitoring period, the response of the building to several low to medium intensity earthquakes was recorded. The building, despite the absence of damage, showed a time-varying dynamic behaviour making it difficult to track the frequencies during the seismic response. By applying a linearisation procedure, frequencies are tracked even during strong motions. Finally, the monitoring results of the Engineering Tower of the Università Politecnica delle Marche are reported. The Tower has been monitored since 2017 and, although with some interruptions, allowed the observation of a marked dependence of its eigen-frequencies on environmental parameters, especially temperature and wind. These effects have been effectively cleansed through the implementation of an artificial neural network.
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Shiki, Sidney Bruce [UNESP]. "Application of Volterra series in nonlinear mechanical system identification and in structural health monitoring problems." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/137761.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Estruturas com comportamento não-linear são frequentes em dinâmica estrutural, principalmente considerando componentes parafusados, com juntas, folgas ou estruturas flexíveis sujeitas à grandes deslocamentos. Desse modo, o monitoramento de estruturas com métodos lineares clássicos, como os baseados em parâmetros modais, podem falhar drasticamente em caracterizar efeitos não-lineares. Neste trabalho foi proposta a utilização de séries de Volterra para identificação de sistemas mecânicos não-lineares em aplicações de detecção de danos e quantificação de parâmetros. A propriedade deste modelo de representar separadamente os componentes de resposta linear e não-linear do sistema foi aplicada para se construir índices de dano que evidenciam a necessidade de modelagem não-linear. Além disso métricas de resíduo linear e não-linear dos termos do modelo de Volterra são empregadas para identificar modelos paramétricos da estrutura. As metodologias propostas são ilustradas em bancadas experimentais de modo a evidenciar a importância de fenômenos não-lineares para o monitoramento de estruturas.
Nonlinear structures are frequent in structural dynamics, specially considering screwed components, with joints, clearance or flexible components presenting large displacements. In this sense the monitoring of systems based on classical linear methods, as the ones based on modal parameters, can drastically fail to characterize nonlinear effects. This thesis proposed the use of Volterra series for nonlinear system identification aiming applications in damage detection and parameter quantification. The property of this model of representing the linear and nonlinear components of the response of a system was used to formulate damage features to make clear the need of nonlinear modeling. Also metrics based on the linear and nonlinear residues of the terms of the Volterra model were employed to identify parametric models of the structure. The proposed methodologies are illustrated in experimental setups to show the relevance of nonlinear phenomena in the structural health monitoring.
FAPESP: 2012/04757-6
FAPESP: 2013/25148-0
FAPESP: 2012/21195-1
FAPESP: 2015/03560-2
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Shiki, Sidney Bruce. "Application of Volterra series in nonlinear mechanical system identification and in structural health monitoring problems /." Ilha Solteira, 2016. http://hdl.handle.net/11449/137761.
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Orientador: Samuel da SilvaAbstract: Nonlinear structures are frequent in structural dynamics, specially considering screwed components, with joints, clearance or flexible components presenting large displacements. In this sense the monitoring of systems based on classical linear methods, as the ones based on modal parameters, can drastically fail to characterize nonlinear effects. This thesis proposed the use of Volterra series for nonlinear system identification aiming applications in damage detection and parameter quantification. The property of this model of representing the linear and nonlinear components of the response of a system was used to formulate damage features to make clear the need of nonlinear modeling. Also metrics based on the linear and nonlinear residues of the terms of the Volterra model were employed to identify parametric models of the structure. The proposed methodologies are illustrated in experimental setups to show the relevance of nonlinear phenomena in the structural health monitoring.
Resumo: Estruturas com comportamento não-linear são frequentes em dinâmica estrutural, principalmente considerando componentes parafusados, com juntas, folgas ou estruturas flexíveis sujeitas à grandes deslocamentos. Desse modo, o monitoramento de estruturas com métodos lineares clássicos, como os baseados em parâmetros modais, podem falhar drasticamente em caracterizar efeitos não-lineares. Neste trabalho foi proposta a utilização de séries de Volterra para identificação de sistemas mecânicos não-lineares em aplicações de detecção de danos e quantificação de parâmetros. A propriedade deste modelo de representar separadamente os componentes de resposta linear e não-linear do sistema foi aplicada para se construir índices de dano que evidenciam a necessidade de modelagem não-linear. Além disso métricas de resíduo linear e não-linear dos termos do modelo de Volterra são empregadas para identificar modelos paramétricos da estrutura. As metodologias propostas são ilustradas em bancadas experimentais de modo a evidenciar a importância de fenômenos não-lineares para o monitoramento de estruturas.
Doutor
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Al, Jailawi Samer Saadi Hussein. "Damage detection using angular velocity." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6539.
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The present work introduces novel methodologies for damage detection and health monitoring of structural and mechanical systems. The new approach uses the angular velocity inside different mathematical forms, via a gyroscope, to detect, locate, and relatively quantify damage. This new approach has been shown to outperform the current state-of-the-art acceleration-based approach in detecting damage on structures. Additionally, the current approach has been shown to be less sensitive to environmental acoustic noises, which present major challenges to the acceleration-based approaches. Furthermore, the current approach has been demonstrated to work effectively on arch structures, which acceleration-based approaches have struggled to deal with. The efficacy of the new approach has been investigated through multiple forms of structural damage indices.
The first methodology proposed a damage index that is based on the changes in the second spatial derivative (curvature) of the power spectral density (PSD) of the angular velocity during vibration. The proposed method is based on the output motion only and does not require information about the input forces/motions. The PSD of the angular velocity signal at different locations on structural beams was used to identify the frequencies where the beams show large magnitude of angular velocity. The curvature of the PSD of the angular velocity at these peak frequencies was then calculated. A damage index is presented that measures the differences between the PSD curvature of the angular velocity of a damaged structure and an artificial healthy baseline structure.
The second methodology proposed a damage index that is used to detect and locate damage on straight and curved beams. The approach introduces the transmissibility and coherence functions of the output angular velocity between two points on a structure where damage may occur to calculate a damage index as a metric of the changes in the dynamic integrity of the structure. The damage index considers limited-frequency bands of the transmissibility function at frequencies where the coherence is high. The efficacy of the proposed angular-velocity damage-detection approach as compared to the traditional linear-acceleration damage-detection approach was tested on straight and curved beams with different chord heights. Numerical results showed the effectiveness of the angular-velocity approach in detecting damage of multiple levels. It was observed that the magnitude of the damage index increased with the magnitude of damage, indicating the sensitivity of the proposed method to damage intensity. The results on straight and curved beams showed that the proposed approach is superior to the linear-acceleration-based approach, especially when dealing with curved beams with increasing chord heights. The experimental results showed that the damage index of the angular-velocity approach outweighed that of the acceleration approach by multiple levels in terms of detecting damage.
A third methodology for health-monitoring and updating of structure supports, which resemble bridges’ bearings, is introduced in this work. The proposed method models the resistance of the supports as rotational springs and uses the transmissibility and coherence functions of the output response of the angular velocity in the neighborhood of the bearings to detect changes in the support conditions. The proposed methodology generates a health-monitoring index that evaluates the level of deterioration in the support and a support-updating scheme to update the stiffness resistance of the supports. Numerical and experimental examples using beams with different support conditions are introduced to demonstrate the effectiveness of the proposed method. The results show that the proposed method detected changes in the state of the bearings and successfully updated the changes in the stiffness of the supports.
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Wang, Liang. "Innovative damage assessment of steel truss bridges using modal strain energy correlation." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/53177/1/Liang_Wang_Thesis.pdf.
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As a part of vital infrastructure and transportation network, bridge structures must function safely at all times. Bridges are designed to have a long life span. At any point in time, however, some bridges are aged. The ageing of bridge structures, given the rapidly growing demand of heavy and fast inter-city passages and continuous increase of freight transportation, would require diligence on bridge owners to ensure that the infrastructure is healthy at reasonable cost. In recent decades, a new technique, structural health monitoring (SHM), has emerged to meet this challenge. In this new engineering discipline, structural modal identification and damage detection have formed a vital component. Witnessed by an increasing number of publications is that the change in vibration characteristics is widely and deeply investigated to assess structural damage. Although a number of publications have addressed the feasibility of various methods through experimental verifications, few of them have focused on steel truss bridges. Finding a feasible vibration-based damage indicator for steel truss bridges and solving the difficulties in practical modal identification to support damage detection motivated this research project.
This research was to derive an innovative method to assess structural damage in steel truss bridges. First, it proposed a new damage indicator that relies on optimising the correlation between theoretical and measured modal strain energy. The optimisation is powered by a newly proposed multilayer genetic algorithm. In addition, a selection criterion for damage-sensitive modes has been studied to achieve more efficient and accurate damage detection results. Second, in order to support the proposed damage indicator, the research studied the applications of two state-of-the-art modal identification techniques by considering some practical difficulties: the limited instrumentation, the influence of environmental noise, the difficulties in finite element model updating, and the data selection problem in the output-only modal identification methods.
The numerical (by a planer truss model) and experimental (by a laboratory through truss bridge) verifications have proved the effectiveness and feasibility of the proposed damage detection scheme. The modal strain energy-based indicator was found to be sensitive to the damage in steel truss bridges with incomplete measurement. It has shown the damage indicator's potential in practical applications of steel truss bridges. Lastly, the achievement and limitation of this study, and lessons learnt from the modal analysis have been summarised.
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Barthorpe, Robert James. "On model- and data-based approaches to structural health monitoring." Thesis, University of Sheffield, 2010. http://etheses.whiterose.ac.uk/1175/.
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Structural Heath Monitoring (SHM) is the term applied to the process of periodically monitoring the state of a structural system with the aim of diagnosing damage in the structure. Over the course of the past several decades there has been ongoing interest in approaches to the problem of SHM. This attention has been sustained by the belief that SHM will allow substantial economic and life-safety benefits to be realised across a wide range of applications. Several numerical and laboratory implementations have been successfully demonstrated. However, despite this research effort, real-world applications of SHM as originally envisaged are somewhat rare. Numerous technical barriers to the broader application of SHM methods have been identified, namely: severe restrictions on the availability of damaged-state data in real-world scenarios; difficulties associated with the numerical modelling of physical systems; and limited understanding of the physical effect of system inputs (including environmental and operational loads). This thesis focuses on the roles of law-based and data-based modelling in current applications of. First, established approaches to model-based SHM are introduced, with the aid of an exemplar ‘wingbox' structure. The study highlights the degree of difficulty associated with applying model-updating-based methods and with producing numerical models capable of accurately predicting changes in structural response due to damage. These difficulties motivate the investigation of non-deterministic, predictive modelling of structural responses taking into account both experimental and modelling uncertainties. Secondly, a data-based approach to multiple-site damage location is introduced, which may allow the quantity of experimental data required for classifier training to be drastically reduced. A conclusion of the above research is the identification of hybrid approaches, in which a forward-mode law-based model informs a data-based damage identification scheme, as an area for future work
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Melvin, Dyan, and Dyan Melvin. "Model Based Structural Monitoring of Plates using Kalman Filter." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/621369.
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Structural health monitoring (SHM) is a quickly advancing field of study in civil engineering and recent advances in the field are in stark contrast to where the field started. For example modern technology of wireless sensing systems allowed for easier monitoring of structures, but the challenge of limiting the number of instrumented locations has not been overcome with traditional methods. The potential of alternative methods has only been realized in recent years with the increase of model based approaches. In particular, the use of limited measurements to estimate structural response at all locations is appealing. To accomplish this goal, this work approaches SHM by using a numerical model combined with a linear recursive state estimation algorithm, known as the Kalman Filter, to update the model-based prediction with a limited number of real time measurements taken on the structure. A thorough overview of the contents is given here. The first section introduces the topic of SHM and the goal of SHM. Then the challenges and limitation that face SHM are discussed along with the recent advances that can be used to overcome them. In Section 2, the proposed framework, a Kalman filter approach, is established. First, a finite element model is formulated for plate structures using the Mindlin-Reissner plate theory and then this finite element code is verified by a comparison with a commercial FEA software. Then the state space model of the system is defined for use with the Augmented Kalman Filter (AKF); the AKF approach overcomes the intrinsic challenge of unknown excitations for civil structures. The AKF is then formulated and discussed. For Section 3, using the AKF in numerical simulations are conducted for 5 different cases. The first three cases study the advantages of multi-metric measurements, i.e. strain and acceleration measurements combined, versus single metric measurement, i.e. strain measurement only or acceleration measurement only. Following that, the next two cases explore the question of whether multi-metric measurements will always provide the best results. Based on the conclusions from the previous section, Section 4 investigates the application of a genetic algorithm, a search algorithm based of Darwinian principles, to find the optimal sensor placement to use as the input to the AKF. Here the developed search algorithm is used in two cases, the first is to find the optimal placement for the strain measurement only case. Next, the improvements in accuracy that are gained by placing taking more measurements is investigated to determine if the gain in accuracy per added measurement decreases for large numbers of measurements. Section 5 contains the final conclusions about the use of the AKF for SHM of plate structures then the potential opportunities of future work regarding plate structures are discussed.
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Kim, Jina. "Low-Power System Design for Impedance-Based Structural Health Monitoring." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/40400.
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Maintenance of the structural integrity and damage detection are critical for all massive and complicated new and aging structures. A structural health monitoring (SHM) system intends to identify damage on the structure under monitoring, so that necessary action can be taken in advance to avoid catastrophic results. Impedance-based SHM utilizes a piezoelectric ceramic as a collocated actuator and sensor, which measures the electrical impedance of the piezoelectric ceramic over a certain frequency range. The impedance profile of a structure under monitoring is compared against a reference profile obtained from the healthy structure. An existing approach called the sinc method adopts a sinc wave excitation and performs traditional discrete Fourier transform (DFT) based structural condition assessment. The sinc method requires rather intensive computing and a digital-to-analog converter (DAC) to generate a sinc excitation signal. It also needs an analog-to-digital converter (ADC) to measure the response voltage, from which impedance profile is obtained through a DFT. This dissertation investigates system design approaches for impedance-based structural health monitoring (SHM), in which a primary goal is low power dissipation.
First, we investigated behaviors of piezoelectric ceramics and proposed an electrical model in order to enable us to conduct system level analysis and evaluation of an SHM system. Unloaded and loaded piezoelectric ceramics were electrically modeled with lumped linear circuit components, which allowed us to perform system level simulations for various environmental conditions. Next, we explored a signaling method called the wideband method, which uses a pseudorandom noise (PN) sequence for excitation of the structure rather than a signal with a particular waveform. The wideband method simplifies generation of the excitation signal and eliminates a digital-to-analog converter (DAC). The system form factor and power dissipation is decreased compared to the previously existing system based on a sinc signal. A prototype system was implemented on a digital signal processor (DSP) board to validate its approach. Third, we studied another low-power design approach which employs binary signals for structural excitation and structural response measurement was proposed. The binary method measures only the polarity of a response signal to acquire the admittance phase, and compares the measured phase against that of a healthy structure. The binary method eliminates the need for a DAC and an ADC. Two prototypes were developed: one with a DSP board and the other with a microcontroller board. Both prototypes demonstrated reduction of power dissipation compared with those for the sinc method and for the wideband method. The microcontroller based prototype achieved an on-board SHM system. Finally, we proposed an analytical method to assess the quality of the damage detection for the binary method. Using our method, one can obtain the confidence level of a damage detection for a given damage distance.Ph. D.
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Burkett, Jason Lee. "BENCHMARK STUDIES FOR STRUCTURAL HEALTH MONITORING USING ANALYTICAL AND EXPERIMENTAL MODELS." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2660.
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The latest bridge inventory report for the United States indicates that 25% of the highway bridges are structurally deficient or functionally obsolete. With such a large number of bridges in this condition, safety and serviceability concerns become increasingly relevant along with the associated increase in user costs and delays. Biennial inspections have proven subjective and need to be coupled with standardized non-destructive testing methods to accurately assess a bridge's condition for decision making purposes. Structural health monitoring is typically used to track and evaluate performance, symptoms of operational incidents, anomalies due to deterioration and damage during regular operation as well as after an extreme event. Dynamic testing and analysis are concepts widely used for health monitoring of existing structures. Successful health monitoring applications on real structures can be achieved by integrating experimental, analytical and information technologies on real life, operating structures. Real-life investigations must be backed up by laboratory benchmark studies. In addition, laboratory benchmark studies are critical for validating theory, concepts, and new technologies as well as creating a collaborative environment between different researchers. To implement structural health monitoring methods and technologies, a physical bridge model was developed in the UCF structures laboratory as part of this thesis research. In this study, the development and testing of the bridge model are discussed after a literature review of physical models. Different aspects of model development, with respect to the physical bridge model are outlined in terms of design considerations, instrumentation, finite element modeling, and simulating damage scenarios. Examples of promising damage detection methods were evaluated for common damage scenarios simulated on the numerical and physical models. These promising damage indices were applied and directly compared for the same experimental and numerical tests. To assess the simulated damage, indices such as modal flexibility and curvature were applied using mechanics and structural dynamics theory. Damage indices based on modal flexibility were observed to be promising as one of the primary indicators of damage that can be monitored over the service life of a structure. Finally, this thesis study will serve an international effort that has been initiated to explore bridge health monitoring methodologies under the auspices of International Association for Bridge Maintenance and Safety (IABMAS). The data generated in this thesis research will be made available to researchers as well as practitioners in the broad field of structural health monitoring through several national and international societies, associations and committees such as American Society of Civil Engineers (ASCE) Dynamics Committee, and the newly formed ASCE Structural Health Monitoring and Control Committee.M.S.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering
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Taddei, Tommaso. "Model order reduction methods for data assimilation : state estimation and structural health monitoring." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108942.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 243-258).
The objective of this thesis is to develop and analyze model order reduction approaches for the efficient integration of parametrized mathematical models and experimental measurements. Model Order Reduction (MOR) techniques for parameterized Partial Differential Equations (PDEs) offer new opportunities for the integration of models and experimental data. First, MOR techniques speed up computations allowing better explorations of the parameter space. Second, MOR provides actionable tools to compress our prior knowledge about the system coming from the parameterized best-knowledge model into low-dimensional and more manageable forms. In this thesis, we demonstrate how to take advantage of MOR to design computational methods for two classes of problems in data assimilation. In the first part of the thesis, we discuss and extend the Parametrized-Background Data-Weak (PBDW) approach for state estimation. PBDW combines a parameterized best knowledge mathematical model and experimental data to rapidly estimate the system state over the domain of interest using a small number of local measurements. The approach relies on projection-by-data, and exploits model reduction techniques to encode the knowledge of the parametrized model into a linear space appropriate for real-time evaluation. In this work, we extend the PBDW formulation in three ways. First, we develop an experimental a posteriori estimator for the error in the state. Second, we develop computational procedures to construct local approximation spaces in subregions of the computational domain in which the best-knowledge model is defined. Third, we present an adaptive strategy to handle experimental noise in the observations. We apply our approach to a companioni heat transfer experiment to prove the effectiveness of our technique. In the second part of the thesis, we present a model-order reduction approach to simulation based classification, with particular application to Structural Health Monitoring (SHM). The approach exploits (i) synthetic results obtained by repeated solution of a parametrized PDE for different values of the parameters, (ii) machine-learning algorithms to generate a classifier that monitors the state of damage of the system, and (iii) a reduced basis method to reduce the computational burden associated with the model evaluations. The approach is based on an offline/online computational decomposition. In the offline stage, the fields associated with many different system configurations, corresponding to different states of damage, are computed and then employed to teach a classifier. Model reduction techniques, ideal for this many-query context, are employed to reduce the computational burden associated with the parameter exploration. In the online stage, the classifier is used to associate measured data to the relevant diagnostic class. In developing our approach for SHM, we focus on two specific aspects. First, we develop a mathematical formulation which properly integrates the parameterized PDE model within the classification problem. Second, we present a sensitivity analysis to take into account the error in the model. We illustrate our method and we demonstrate its effectiveness through the vehicle of a particular companion experiment, a harmonically excited microtruss.
by Tommaso Taddei.
Ph. D.
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Neves, Cláudia. "Structural Health Monitoring of Bridges : Model-free damage detection method using Machine Learning." Licentiate thesis, KTH, Bro- och stålbyggnad, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205616.
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This is probably the most appropriate time for the development of robust and reliable structural damage detection systems as aging civil engineering structures, such as bridges, are being used past their life expectancy and beyond their original design loads. Often, when a significant damage to the structure is discovered, the deterioration has already progressed far and required repair is substantial. This is both expensive and has negative impact on the environment and traffic during replacement. For the exposed reasons the demand for efficient Structural Health Monitoring techniques is currently extremely high. This licentiate thesis presents a two-stage model-free damage detection approach based on Machine Learning. The method is applied to data gathered in a numerical experiment using a three-dimensional finite element model of a railway bridge. The initial step in this study consists in collecting the structural dynamic response that is simulated during the passage of a train, considering the bridge in both healthy and damaged conditions. The first stage of the proposed algorithm consists in the design and unsupervised training of Artificial Neural Networks that, provided with input composed of measured accelerations in previous instants, are capable of predicting future output acceleration. In the second stage the prediction errors are used to fit a Gaussian Process that enables to perform a statistical analysis of the distribution of errors. Subsequently, the concept of Damage Index is introduced and the probabilities associated with false diagnosis are studied. Following the former steps Receiver Operating Characteristic curves are generated and the threshold of the detection system can be adjusted according to the trade-off between errors. Lastly, using the Bayes’ Theorem, a simplified method for the calculation of the expected cost of the strategy is proposed and exemplified.QC 20170420
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Sunny, Mohammed Rabius. "Towards Structural Health Monitoring of Gossamer Structures Using Conductive Polymer Nanocomposite Sensors." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28797.
Full textAbstract:
The aim of this research is to calibrate conductive polymer nanocomposite materials for
large strain sensing and develop a structural health monitoring algorithm for gossamer
structures by using nanocomposites as strain sensors. Any health monitoring system works
on the principle of sensing the response (strain, acceleration etc.) of the structure to an
external excitation and analyzing the response to find out the location and the extent of
the damage in the structure. A sensor network, a mathematical model of the structure, and
a damage detection algorithm are necessary components of a structural health monitoring
system. In normal operating conditions, a gossamer structure can experience normal strain
as high as 50%. But presently available sensors can measure strain up to 10% only, as
traditional strain sensor materials do not show low elastic modulus and high electrical
conductivity simultaneously. Conductive polymer nanocomposite which can be stretched
like rubber (up to 200%) and has high electrical conductivity (sheet resistance 100 Ohm/sq.)
can be a possible large strain sensor material. But these materials show hysteresis and
relaxation in the variation of electrical properties with mechanical strain. It makes the
calibration of these materials difficult. We have carried out experiments on conductive
polymer nanocomposite sensors to study the variation of electrical resistance with time
dependent strain. Two mathematical models, based on the modified fractional calculus and
the Preisach approaches, have been developed to model the variation of electrical resistance
with strain in a conductive polymer. After that, a compensator based on a modified Preisach
model has been developed. The compensator removes the effect of hysteresis and relaxation
from the output (electrical resistance) obtained from the conductive polymer nanocomposite
sensor. This helps in calibrating the material for its use in large strain sensing. Efficiency of
both the mathematical models and the compensator has been shown by comparison of their
results with the experimental data. A prestressed square membrane has been considered
as an example structure for structural health monitoring. Finite element analysis using
ABAQUS has been carried out to determine the response of the membrane to an uniform
transverse dynamic pressure for different damage conditions. A neuro-fuzzy system has been
designed to solve the inverse problem of detecting damages in the structure from the strain
history sensed at different points of the structure by a sensor that may have a significant
hysteresis. Damage feature index vector determined by wavelet analysis of the strain history at different points of the structure are taken by the neuro-fuzzy system as input. The neuro-fuzzy system detects the location and extent of the damage from the damage feature index
vector by using some fuzzy rules. Rules associated with the fuzzy system are determined
by a neural network training algorithm using a training dataset, containing a set of known
input and output (damage feature index vectors, location and extent of damage for different
damage conditions). This model is validated by using the sets of input-output other than
those which were used to train the neural network.Ph. D.
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Essegbey, John W. "Piece-wise Linear Approximation for Improved Detection in Structural Health Monitoring." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342729241.
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Dürager, Christian [Verfasser], and Christian [Akademischer Betreuer] Boller. "Model-based damage feature extraction for structural-health monitoring applications / Christian Dürager ; Betreuer: Christian Boller." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2018. http://d-nb.info/1183673507/34.
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Tobe, Randy Joseph. "Structural Health Monitoring of a Thermal Protection System for Fastener Failure with a Validated Model." Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1290114035.
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Isidori, Daniela. "A low-cost structural health monitoring system for residential buildings: experimental tests on a scale model." Doctoral thesis, Università Politecnica delle Marche, 2013. http://hdl.handle.net/11566/242728.
Full textAbstract:
Una delle questioni più importanti riguardante l’Ingegneria civile e meccanica
è il rilevamento di danni strutturali. Una struttura civile, durante la sua
vita utile, oltre all’ordinaria esposizione ai carichi di servizio e ambientali, può
essere sottoposta episodicamente anche carichi più rilevanti come ad esempio
i terremoti. Questi eventi possono avere un profondo impatto sulla sicurezza
degli edifici e diventa opportuno, o in molti casi necessario, un continuo monitoraggio
delle condizioni di salute della struttura. Le tecniche più utilizzate
sono quelle di monitoraggio strutturale o Structural Health Monitoring (SHM).
Queste consentono di fornire una preziosa conoscenza del comportamento dinamico
delle strutture monitorate, della loro risposta in condizioni di servizio
sotto carichi ambientali, o sottoposte a situazioni di sollecitazione più rilevanti.
Tali sistemi sono largamente impiegati nelle applicazioni di ingegneria meccanica,
aeronautica e civile (soprattutto per strutture rilevanti), e in genere si
basano sulla misura e sullo studio delle vibrazioni di risposta a diversi input.
Lo sviluppo di dispositivi di misura a basso costo e a basso consumo energetico,
la disponibilità di sistemi di acquisizione di ultima generazione e di software
avanzati per l’analisi dinamica delle strutture rendono possibile l’applicazione
di tecniche di monitoraggio strutturale non solo a strutture strategicamente
significative (grandi infrastrutture, etc.), ma anche ad edifici ordinari. In tale
contesto, lo scopo di questa ricerca è quello di proporre una nuova metodologia
sperimentale e numerica per eseguire il monitoraggio di strutture civili, utilizzando
un prototipo di sistema SHM a basso costo, caratterizzato da sensori
MEMS e da strumenti di acquisizione di nuova generazione. Per questo motivo
è stato realizzato un modello in scala di un edificio a tre piani. Il modello è stato
strumentato e sottoposto a prove dinamiche cicliche. Sul modello sono stati
inseriti insieme ai tradizionali accelerometri piezoelettrici dei sensori MEMS,
in modo da poter confrontare i risultati e valutare le prestazioni di questi ultimi.
Sono state eseguite diverse prove dinamiche utilizzando due diversi tipi di
sensori per fare un’analisi comparativa del rumore di fondo, della risposta dinamica
e dello sfasamento in differenti condizioni operative. I risultati ottenuti
dai sensori a basso costo hanno evidenziato delle buone prestazioni paragonabili
a quelle degli accelerometri piezoelettrici. I dati acquisiti dal sistema sono applicati
a un modello numerico agli elementi finiti (FE) per rilevare l’esistenza, la
distribuzione e l’entità di danni locali e valutare quindi la vita utile rimasta. Il modello numerico (FE) della struttura è stato sviluppato e verificato sulla base
dei risultati ottenuti dall’ identificazione sperimentale dei parametri modali
della struttura, eseguita utilizzando le tecniche di analisi modale sperimentale
EMA (input-output) e di analisi modale operazionale OMA (output-only).
I parametri modali stimati sono stati utilizzati per controllare il modello FE.
L’analisi e il confronto dei risultati teorici e sperimentali ottenuti permettono di
affermare che il sistema proposto rappresenta una soluzione adeguata in termini
di costi, di affidabilità della misura dei sensori che registrano gli input dinamici
e di previsione della vita residua della struttura a seguito di un fenomeno
rilevante. La vita utile stimata dal modello in scala ottenuta mediante indici
danno locale e globale è coerente con i risultati sperimentali.detection of structural damages. Throughout its service life, a civil structure besides the exposure to operational and environmental forces can be subjected episodically to earthquakes. These events may have a deep impact on building safety and a continuous monitoring of the structure health conditions becomes desirable or necessary in many cases. Structural Health Monitoring (SHM) provides a valuable knowledge of the dynamic behavior of monitored structures of their response to service environmental loadings, and of rise and distribution of the deterioration conditions. These techniques are widely employed in mechanical, aeronautical, and civil engineering, generally rely on vibration response measurements. The development of low cost and low energy measuring devices, the new generation of data acquisition systems, together with the increasing availability of software for advanced dynamic analysis, have extended SHM to several areas where up to now the high cost of traditional equipment was not justified by the value of structure itself. In civil engineering, SHM is moving from big infrastructures like bridges, dams and skyscrapers to historical heritage and residential buildings. Within this a background, the purpose of this work is to propose a new combined experimental and numerical methodology to perform the SHM of civil structures lying in seismic hazard zones. A relatively low-cost SHM prototype system based on this approach has been developed and the issues related to the usage of low-cost sensors and new generation data acquisition tools for non-destructive structural testing are discussed. A scale frame model of a three-story building has been build up and instrumented in order to simulate the vibration response of a multi-story building subjected to cyclic loads. Dynamic tests have been carried out by using two different types of sensors in order to make a comparative analysis of floor noise, dynamic response and phase shift in different operating conditions: (i) low cost MEMSbased accelerometers and (ii) classical piezo-electric transducers. The usage of low-cost sensors has allowed to get enough comparable performance, in terms of measured quantities, with respect to piezoelectric accelerometers. The data acquired by the system are provided to a finite element numerical model (FE) to detect the appearing, rise and distribution of local damages and to estimate a global damage level. The numerical finite element (FE) model of the structure has been developed and tuned up by means of the outcome of a structural iden-tification performed by using an Experimental and Operational Modal Analysis approaches. In particular, the modal parameters estimated have been utilised to update the FE model. A damage level estimation methodology is proposed and calibrated comparing the experimental results with the FE model prediction during cyclic failure tests of the scale frame. The life prediction of the scale model obtained by local and global damage indexes is consistent with the experimental results.
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Fang, Qichen. "Development of Conductive Silver Nanocomposite-based Sensors for Structural and Corrosion Health Monitoring." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton162738212502004.
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Ebrahimian, Mahdi. "Structural system identification and health monitoring of buildings by the wave method based on the Timoshenko beam model." Thesis, University of Southern California, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3722860.
Full textAbstract:
This dissertation presents a new development of the wave method for structural health monitoring (SHM) of buildings. Robust and reliable SHM methods help save lives and reduce economic losses caused by earthquakes and other extreme events. Previously, in system identification and health monitoring, it was assumed that waves of different frequency propagate with constant velocity and the identification was based on the non-dispersive shear beam model of the structure. This study presents the first effort to consider dispersive wave propagation in system identification and health monitoring by the wave method. To consider dispersion due to bending deformation in buildings a Timoshenko beam model is used. Although buildings as a whole deform primarily in shear, bending deformation is always present to some degree especially for shear wall buildings. To identify allowable ranges of important parameters of the model parametric studies are performed. The model is further generalized to a non-uniform Timoshenko beam model which can take into account variation of properties with height and be used for higher resolution structural health monitoring. The models together with the suggested method to estimate initial values were validated on three full scale buildings. They were used to identify two full scale building from earthquake records and also to monitor the changes in a full-scale 7-story slice of shear wall building which was progressively damaged on UCSD-NEES shake table. It was shown that the model is robust for structural identification and health monitoring of a wide range of building systems and can successfully model dispersion due to bending deformation.
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Gökçe, Hasan Burak. "Structural identification through monitoring, modeling and predictive analysis under uncertainty." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5222.
Full textAbstract:
Bridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior based on structural health monitoring (SHM) data. In a recent study by the ASCE St-Id Committee, St-Id framework is given in six steps, including modeling, experimentation and ultimately decision making for estimating the performance and vulnerability of structural systems reliably through the improved simulations using monitoring data. In some St-Id applications, there can be challenges and considerations related to this six-step framework. For instance not all of the steps can be employed; thereby a subset of the six steps can be adapted for some cases based on the various limitations. In addition, each step has its own characteristics, challenges, and uncertainties due to the considerations such as time varying nature of civil structures, modeling and measurements. It is often discussed that even a calibrated model has limitations in fully representing an existing structure; therefore, a family of models may be well suited to represent the structure's response and performance in a probabilistic manner. The principle objective of this dissertation is to investigate nonparametric and parametric St-Id approaches by considering uncertainties coming from different sources to better assess the structural condition for decision making. In the first part of the dissertation, a nonparametric St-Id approach is employed without the use of an analytical model.; It is recommended that a family-of-models approach is suitable for structures that have less redundancy, high operational importance, are deteriorated, and are performing under close capacity and demand levels.; The new methodology, which is successfully demonstrated on both lab and real-life structures, can identify and locate the damage by tracking correlation coefficients between strain time histories and can locate the damage from the generated correlation matrices of different strain time histories. This methodology is found to be load independent, computationally efficient, easy to use, especially for handling large amounts of monitoring data, and capable of identifying the effectiveness of the maintenance. In the second part, a parametric St-Id approach is introduced by developing a family of models using Monte Carlo simulations and finite element analyses to explore the uncertainty effects on performance predictions in terms of load rating and structural reliability. The family of models is developed from a parent model, which is calibrated using monitoring data. In this dissertation, the calibration is carried out using artificial neural networks (ANNs) and the approach and results are demonstrated on a laboratory structure and a real-life movable bridge, where predictive analyses are carried out for performance decrease due to deterioration, damage, and traffic increase over time. In addition, a long-span bridge is investigated using the same approach when the bridge is retrofitted. The family of models for these structures is employed to determine the component and system reliability, as well as the load rating, with a distribution that incorporates various uncertainties that were defined and characterized. It is observed that the uncertainties play a considerable role even when compared to calibrated model-based predictions for reliability and load rating, especially when the structure is complex, deteriorated and aged, and subjected to variable environmental and operational conditions.ID: 031001436; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Adviser: F. Necati ?çatba?ƒ.; Title from PDF title page (viewed June 24, 2013).; Thesis (Ph.D.)--University of Central Florida, 2012.; Includes bibliographical references (p. 173-187).
Ph.D.
Doctorate
Civil, Environmental, and Construction Engineering
Engineering and Computer Science
Civil Engineering
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Marsh, Phillip Scott. "Reliability model for lifetime multi-objective optimization of a structural health monitoring system embedded in a deteriorating reinforced concrete bridge deck." Diss., Connect to online resource, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1435223.
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Ruffels, Aaron. "Model-Free Damage Detection for a Small-Scale Steel Bridge." Thesis, KTH, Bro- och stålbyggnad, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232363.
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Around the world bridges are ageing. In Europe approximately two thirds of all railway bridges are over 50 years old. As these structures age, it becomes increasingly important that they are properly maintained. If damage remains undetected this can lead to premature replacement which can have major financial and environmental costs. It is also imperative that bridges are kept safe for the people using them. Thus, it is necessary for damage to be detected as early as possible. This research investigates an unsupervised, model-free damage detection method which could be implemented for continuous structural health monitoring. The method was based on past research by Gonzalez and Karoumi (2015), Neves et al. (2017) and Chalouhi et al. (2017). An artificial neural network (ANN) was trained on accelerations from the healthy structural state. Damage sensitive features were defined as the root mean squared errors between the measured data and the ANN predictions. A baseline healthy state could then be established by presenting the trained ANN with more healthy data. Thereafter, new data could be compared with this reference state. Outliers from the reference data were taken as an indication of damage. Two outlier detection methods were used: Mahalanobis distance and the Kolmogorov-Smirnov test. A model steel bridge with a span of 5 m, width of 1 m and height of approximately 1.7 m was used to study the damage detection method. The use of an experimental model allowed damaged to be freely introduced to the structure. The structure was excited with a 12.7 kg rolling mass at a speed of approximately 2.1 m/s (corresponding to a 20.4 ton axle load moving at 47.8 km/h in full scale). Seven accelerometers were placed on the structure and their locations were determined using an optimal sensor placement algorithm. The objectives of the research were to: identify a number of single damage cases, distinguish between gradual damage cases and identify the location of damage. The proposed method showed promising results and most damage cases were detected by the algorithm. Sensor density and the method of excitation were found to impact the detection of damage. By training the ANN to predict correlations between accelerometers the sensor closest to the damage could be detected, thus successfully localising the damage. Finally, a gradual damage case was investigated. There was a general increase in the damage index for greater damage however, this did not progress smoothly and one case of ‘greater’ damage showed a decrease in the damage index.
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Monavari, Benyamin. "SHM-based structural deterioration assessment." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132660/1/Benyamin%20Monavari%20Thesis.pdf.
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This research has successfully developed an effective methodology to detect and locate deterioration as well as estimate its severity in the presence of environmental and operational (E&O) variations and high level of measurement noise. It developed a novel data normalization procedure to diminish the E&O variations and high level of noise content; and developed thirteen time-series based deterioration indicators to detect deterioration. The proposed methods were verified utilising measured data from different numerically simulated case studies and laboratory tests, and their efficiency is demonstrated using data acquired from a real-world instrumented building.
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Shmerling, Robert Zachary. "STRUCTURAL CONDITION ASSESSMENT OF PRESTRESSED CONCRETE TRANSIT GUIDEWAYS." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3529.
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Objective condition assessment is essential to make better decisions for safety and serviceability of existing civil infrastructure systems. This study explores the condition of an existing transit guideway system that has been in service for thirty-five years. The structural system is composed of six-span continuous prestressed concrete bridge segments. The overall transit system incorporates a number of continuous bridges which share common design details, geometries, and loading conditions. The original analysis is based on certain simplifying assumptions such as rigid behavior over supports and simplified tendon/concrete/steel plate interaction. The current objective is to conduct a representative study for a more accurate understanding of the structural system and its behavior. The scope of the study is to generate finite element models (FEMs) to be used in static and dynamic parameter sensitivity studies, as well load rating and reliability analysis of the structure. The FEMs are used for eigenvalue analysis and simulations. Parameter sensitivity studies consider the effect of changing critical parameters, including material properties, prestress loss, and boundary and continuity conditions, on the static and dynamic structural response. Load ratings are developed using an American Association for State Highway Transportation Officials Load and Resistance Factor Rating (AASHTO LRFR) approach. The reliability of the structural system is evaluated based on the data obtained from various finite element models. Recommendations for experimental validation of the FEM are presented. This study is expected to provide information to make better decisions for operations, maintenance and safety requirements; to be a benchmark for future studies, to establish a procedure and methodology for structural condition assessment, and to contribute to the general research body of knowledge in condition assessment and structural health monitoring.M.S.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering
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Hou, Chuanchuan. "Vibration-based damage identification with enhanced frequency dataset and a cracked beam element model." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20434.
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Damage identification is an important topic in structural assessment and structural health monitoring (SHM). Vibration-based identification techniques use modal data to identify the existence, location and severity of possible damages in structures, often via a numerical model updating procedure. Among other factors influencing the practicality and reliability of a damage identification approach, two are of primary interest to this study. The first one concerns the amount and quality of modal data that can be used as ‘response’ data for the model updating. It is generally recognised that natural frequencies can be measured with relatively high accuracy; however, their number is limited. Mode shapes, on the other hand, are susceptible to larger measurement errors. Seeking additional modal frequency data is therefore of significant value. The second one concerns the errors at the numerical (finite element) model level, particularly in the representation of the effect of damage on the dynamic properties of the structure. An inadequate damage model can lead to inaccurate and even false damage identification. The first part of the thesis is devoted to enhancing the modal dataset by extracting the so called ‘artificial boundary condition’ (ABC) frequencies in a real measurement environment. The ABC frequencies correspond to the natural frequencies of the structure with a perturbed boundary condition, but can be generated without the need of actually altering the physical support condition. A comprehensive experimental study on the extraction of such frequencies has been conducted. The test specimens included steel beams of relatively flexible nature, as well as thick and stiffer beams made from metal material and reinforced concrete, to cover the typical variation of the dynamic characteristics of real-life structures in a laboratory condition. The extracted ABC frequencies are subsequently applied in the damage identification in beams. Results demonstrate that it is possible to extract the first few ABC frequencies from the modal testing in different beam settings for a variety of ABC incorporating one or two virtual pin supports. The inclusion of ABC frequencies enables the identification of structural damages satisfactorily without the necessity to involve the mode shape information. The second part of the thesis is devoted to developing a robust model updating and damage identification approach for beam cracks, with a special focus on thick beams which present a more challenging problem in terms of the effect of a crack than slender beams. The priority task has been to establish a crack model which comprehensively describes the effect of a crack to reduce the modelling errors. A cracked Timoshenko beam element model is introduced for explicit beam crack identification. The cracked beam element model is formulated by incorporating an additional flexibility due to a crack using the fracture mechanics principles. Complex effects in cracked thick beams, including shear deformation and coupling between transverse and longitudinal vibrations, are represented in the model. The accuracy of the cracked beam element model for predicting modal data of cracked thick beams is first verified against numerically simulated examples. The consistency of predictions across different modes is examined in comparison with the conventional stiffness reduction approach. Upon satisfactory verification, a tailored model updating procedure incorporating an adaptive discretisation approach is developed for the implementation of the cracked beam element model for crack identification. The updating procedure is robust in that it has no restriction on the location, severity and number of cracks to be identified. Example updating results demonstrate that satisfactory identification can be achieved for practically any configurations of cracks in a beam. Experimental study with five solid beam specimens is then carried out to further verify the developed cracked beam element model. Both forward verification and crack damage identification with the tested beams show similar level of accuracy to that with the numerically simulated examples. The cracked beam element model can be extended to crack identification of beams with complex cross sections. To do so the additional flexibility matrix for a specific cross-section type needs to be re-formulated. In the present study this is done for box sections. The stress intensity factors (SIF) for a box section as required for the establishment of the additional flexibility matrix are formulated with an empirical approach combining FE simulation, parametric analysis and regression analysis. The extended cracked beam element model is verified against both FE simulated and experimentally measured modal data. The model is subsequently incorporated in the crack identification for box beams. The successful extension of the cracked beam element model to the box beams paves the way for similar extension to the crack identification of other types of sections in real-life engineering applications.
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Chang, Minwoo. "Investigating and Improving Bridge Management System Methodologies Under Uncertainty." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/5039.
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This dissertation presents a novel procedure to select explanatory variables, without the influence of human bias, for deterioration model development using National Bridge Inventory (NBI) data. Using NBI information, including geometric data and climate information, candidate explanatory variables can be converted into normalized numeric values and analyzed prior to the development of deterministic or stochastic deterioration models. The prevailing approach for explanatory variable selection is to use expert opinion solicited from experienced engineers. This may introduce human influenced biases into the deterioration modeling process. A framework using Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression and covariance analysis are combined to compensate for this potential bias. Additionally, the cross validation analysis and solution path is used as a standard for the selection of minimum number of explanatory variables.
The proposed method is demonstrated through the creation of deterministic deterioration models for deck, superstructure, and substructure for Wyoming bridges and compared to explanatory variables using the expert selection method. The comparison shows a significant decrease in error using the presented framework based on the L2 relative error norm.
The final chapter presents a new method to develop stochastic deterioration models using logistic regression. The relative importance amongst explanatory variables is used to develop a classification tree for Wyoming bridges. The bridges in a subset are commonly associated with several explanatory variables, so that the deterioration models can be more representative and accurate than using a single explanatory variable. The logistic regression is used to introduce the stochastic contribution into the deterioration models. In order to avoid missing data problems, the binary categories condition rating, either remaining the same or decreased, are considered for logistic regression. The probability of changes in bridges’ condition rating is obtained and the averages for same condition ratings are used to create transition probability matrix for each age group.
The deterioration model based on Markov chain are developed for Wyoming bridges and compared with the previous model based on percentage prediction and optimization approach. The prediction error is analyzed, which demonstrates the considerable performance of the proposed method and is suitable for relatively small data samples.
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Veta, Jacob E. "Analysis and Development of a Lower Extremity Osteological Monitoring Tool Based on Vibration Data." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1595879294258019.
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Yano, Marcus Omori. "Extrapolation of autoregressive model for damage progression analysis /." Ilha Solteira, 2019. http://hdl.handle.net/11449/182287.
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Orientador: Samuel da SilvaResumo: O principal objetivo deste trabalho é usar métodos de extrapolação em coeficientes de modelos autorregressivos (AR), para fornecer informações futuras de condições de estruturas na existência de mecanismo de danos pré-definidos. Os modelos AR são estimados considerando a predição de um passo à frente, verificados e validados a partir de dados de vibração de uma estrutura na condição não danificada. Os erros de predição são usados para extrair um indicador para classificar a condição do sistema. Então, um novo modelo é identificado se qualquer variação de índices de dano ocorrer, e seus coeficientes são comparados com os do modelo de referência. A extrapolação dos coeficientes de AR é realizada através das splines cúbicas por partes que evitam possíveis instabilidades e alterações indesejáveis dos polinômios, obtendo aproximações adequadas através de polinômios de baixa ordem. Uma curva de tendência para o indicador capaz de predizer o comportamento futuro pode ser obtida a partir da extrapolação direta dos coeficientes. Uma estrutura de três andares com um para-choque e uma coluna de alumínio colocada no centro do último andar são analisados com diferentes cenários de dano para ilustrar a abordagem. Os resultados indicam a possibilidade de estimar a condição futura do sistema a partir dos dados de vibração nas condições de danos iniciais.
Abstract: The main purpose of this work is to apply extrapolation methods upon coefficients of autoregressive models (AR), to provide future condition information of structures in the existence of predefined damage mechanism. The AR models are estimated considering one-step-ahead prediction, verified and validated from vibration data of a structure in the undamaged condition. The prediction errors are used to extract an indicator to classify the system state condition. Then, a new model is identified if any variation of damage indices occurs, and its coefficients are compared to the ones from the reference model. The extrapolation of the AR coefficients is performed through the piecewise cubic splines that avoid possible instabilities and undesirable changes of the polynomials, obtaining suitable approximations through low-order polynomials. A trending curve for the indicator capable of predicting future behavior can be obtained from direct coefficient extrapolation. A benchmark of a three-story building structure with a bumper and an aluminum column placed on the center of the top floor is analyzed with different damage scenarios to illustrate the approach. The results indicate the feasibility of estimating the future system state from the vibration data in the initial damage conditions.
Mestre
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Wahalathantri, Buddhi Lankananda. "Damage assessment in reinforced concrete flexural members using modal strain energy based method." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/59509/1/Buddhi_Wahalathantri_Thesis.pdf.
Full textAbstract:
Damage assessment (damage detection, localization and quantification) in structures and appropriate retrofitting will enable the safe and efficient function of the structures. In this context, many Vibration Based Damage Identification Techniques (VBDIT) have emerged with potential for accurate damage assessment. VBDITs have achieved significant research interest in recent years, mainly due to their non-destructive nature and ability to assess inaccessible and invisible damage locations. Damage Index (DI) methods are also vibration based, but they are not based on the structural model. DI methods are fast and inexpensive compared to the model-based methods and have the ability to automate the damage detection process. DI method analyses the change in vibration response of the structure between two states so that the damage can be identified. Extensive research has been carried out to apply the DI method to assess damage in steel structures. Comparatively, there has been very little research interest in the use of DI methods to assess damage in Reinforced Concrete (RC) structures due to the complexity of simulating the predominant damage type, the flexural crack. Flexural cracks in RC beams distribute non- linearly and propagate along all directions. Secondary cracks extend more rapidly along the longitudinal and transverse directions of a RC structure than propagation of existing cracks in the depth direction due to stress distribution caused by the tensile reinforcement. Simplified damage simulation techniques (such as reductions in the modulus or section depth or use of rotational spring elements) that have been extensively used with research on steel structures, cannot be applied to simulate flexural cracks in RC elements. This highlights a big gap in knowledge and as a consequence VBDITs have not been successfully applied to damage assessment in RC structures. This research will address the above gap in knowledge and will develop and apply a modal strain energy based DI method to assess damage in RC flexural members.
Firstly, this research evaluated different damage simulation techniques and recommended an appropriate technique to simulate the post cracking behaviour of RC structures. The ABAQUS finite element package was used throughout the study with properly validated material models. The damaged plasticity model was recommended as the method which can correctly simulate the post cracking behaviour of RC structures and was used in the rest of this study. Four different forms of Modal Strain Energy based Damage Indices (MSEDIs) were proposed to improve the damage assessment capability by minimising the numbers and intensities of false alarms. The developed MSEDIs were then used to automate the damage detection process by incorporating programmable algorithms. The developed algorithms have the ability to identify common issues associated with the vibration properties such as mode shifting and phase change. To minimise the effect of noise on the DI calculation process, this research proposed a sequential order of curve fitting technique. Finally, a statistical based damage assessment scheme was proposed to enhance the reliability of the damage assessment results. The proposed techniques were applied to locate damage in RC beams and slabs on girder bridge model to demonstrate their accuracy and efficiency.
The outcomes of this research will make a significant contribution to the technical knowledge of VBDIT and will enhance the accuracy of damage assessment in RC structures. The application of the research findings to RC flexural members will enable their safe and efficient performance.
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Mountassir, Mahjoub El. "Surveillance d'intégrité des structures par apprentissage statistique : application aux structures tubulaires." Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0047.
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Les approches de surveillance de l’intégrité des structures ont été proposées pour permettre un contrôle continu de l’état des structures en intégrant à celle-ci des capteurs intelligents. En effet, ce contrôle continu doit être effectué pour s’assurer du bon fonctionnement de celles-ci car la présence d’un défaut dans la structure peut aboutir à un accident catastrophique. Cependant, la variation des conditions environnementales et opérationnelles (CEO) dans lesquelles la structure évolue, impacte sévèrement les signaux collectés ce qui induit parfois une mauvaise interprétation de la présence du défaut dans la structure. Dans ce travail de thèse, l’application des méthodes d’apprentissage statistiques classiques a été envisagée dans le cas des structures tubulaires. Ici, les effets des paramètres de mesures sur la robustesse de ces méthodes ont été investiguées. Ensuite, deux approches ont été proposées pour remédier aux effets des CEO. La première approche suppose que la base de données des signaux de référence est suffisamment riche en variation des CEO. Dans ce cas, une estimation parcimonieuse du signal mesuré est calculée. Puis, l’erreur d’estimation est utilisée comme indicateur de défaut. Tandis que la deuxième approche est utilisée dans le cas où la base de données des signaux des références contient une variation limitée des CEO mais on suppose que celles-ci varient lentement. Dans ce cas, une mise à jour du modèle de l’état sain est effectuée en appliquant l’analyse en composante principale (PCA) par fenêtre mobile. Dans les deux approches, la localisation du défaut a été assurée en utilisant une fenêtre glissante sur le signal provenant de l’état endommagéTo ensure better working conditions of civil and engineering structures, inspections must be made on a regular basis. However, these inspections could be labor-intensive and cost-consuming. In this context, structural health monitoring (SHM) systems using permanently attached transducers were proposed to ensure continuous damage diagnostic of these structures. In SHM, damage detection is generally based on comparison between the healthy state signals and the current signals. Nevertheless, the environmental and operational conditions will have an effect on the healthy state signals. If these effects are not taken into account they would result in false indication of damage (false alarm). In this thesis, classical machine learning methods used for damage detection have been applied in the case of pipelines. The effects of some measurements parameters on the robustness of these methods have been investigated. Afterthat, two approaches were proposed for damage diagnostic depending on the database of reference signals. If this database contains large variation of these EOCs, a sparse estimation of the current signal is calculated. Then, the estimation error is used as an indication of the presence of damage. Otherwise, if this database is acquired at limited range of EOCs, moving window PCA can be applied to update the model of the healthy state provided that the EOCs show slow and continuous variation. In both approaches, damage localization was ensured using a sliding window over the damaged pipe signal
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"Feature and Statistical Model Development in Structural Health Monitoring." Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.38657.
Full textAbstract:
abstract: All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development.
The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally.
Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering.
The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns.
Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.Dissertation/Thesis
Doctoral Dissertation Mechanical Engineering 2016
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Huang, Chih-Wei, and 黃志偉. "Health monitoring of structural systems using a repetitive model refinement approach." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/15408010209374492193.
Full textAbstract:
碩士逢甲大學
土木工程所
95
This paper presents a statistical confidence-interval based model refinement approach for the health monitoring of structural systems under earthquake-induced ground excitations. In a multiple regression setting, the proposed model refinement approach uses the 95% confidence interval of the estimated structural parameter to determine the statistical significance of such a parameter. If the parameter’s confidence interval contains the “null” value, it is statistically significant to remove such a parameter while maintaining the parameters whose confidence intervals do not cover the zero value. Repeat this process by rerunning the multiple regression algorithm for the sifted parameters until all of them are statistically sustainable—all confidence intervals of the estimated parameters do not contain the zero value. Other confidence intervals, such as the 90% and 99%, of structural parameters are also tested for comparison and validation purposes. For stochastic modeling and model updating where no a priori information on the type of the structural model is available, this model refinement approach is implemented for the developed series models of multivariable polynomial expansions: the linear, the Taylor series, and the power series model. After the analysis of variance, the statistically refined power series model provides the least relative error in stiffness evaluation when compared to the model using the nonlinear stress analysis technique.
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Abittan, Erez. "A model-based approach for bridge structural health monitoring using wireless sensor networks." 2006. http://etd.nd.edu/ETD-db/theses/available/etd-04212006-164249/.
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Thesis (M.S.E.E)--University of Notre Dame, 2006.Thesis directed by Panos J. Antsaklis for the Department of Electrical Engineering. "April 2006." Includes bibliographical references (leaves 77-78).
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Chiu, Chun-Hsiang, and 邱群翔. "Applying Finite Element Model to Hilbert-Huang Transform Structural Health Monitoring Method with Different Damping." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9m6rc8.
Full textAbstract:
碩士國立中央大學
土木工程學系
107
In the past, Fourier Transform (FT) was usually used to investigate structural health condition. It transforms signals from time domain functions into frequency domain functions. However, Fourier Transform expands the signals by using pre-determined and time-invariant bases. Therefore, it is only suitable for dealing linear and steady signals. Instantaneous properties cannot be obtained by this method. For analyzing nonlinear and unsteady signals such as earthquake waveforms, better method should be applied. Hilbert-Huang Transform (HHT) is an effective algorithm to deal with time-frequency domain signals. It possesses two characteristics, posteriori base and adaptive base. Thus, it is suitable for dealing nonlinear and unsteady signals. Hilbert-Huang Transform expands the signals into energy distribution in both time domain and frequency domain, which makes it possible to interpret the properties of structural dynamic signals by introducing the concept of instantaneous frequency and determine the structural safety as well. A recently developed analytical method called HHT SHM takes Hilbert-Huang Transform as its core, integrating other two numerical steps, time-frequency domain amplification function (T.F.AF) and modal temporal variation curve (MTVC). The method defines modal parameters which quantify the dynamic characteristics with statistical means. This research utilizes a finite element software, ABAQUS, to establish steel structure models with different damping. Apply earthquake forces on the base of the model and obtain the acceleration responses from various floors. HHT SHM method is adopted for analysis to convert acceleration signals into time-frequency spectrum, and the modal vibration characteristics can be extracted from the spectrum. Finally, compare the analysis results from different models and study the influences of damping ratio on the modal parameters.
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Sevieri, Giacomo. "The seismic assessment of existing concrete gravity dams: FE model uncertainty quantification and reduction." Doctoral thesis, 2019. http://hdl.handle.net/2158/1171930.
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The implementation of resilience-enhancing strategies on existing concrete gravity dams is a task of primary importance for the society. This aim can be achieved by estimating the risk of concrete dams against multi-hazards and by improving the structural control. Focusing the attention only on the seismic hazard, numerical models assume great importance due to the lack of case studies. However, for the same reason, numerical models are characterised by a high level of uncertainty which must be reduced by exploiting all available information. In this way reliable predictive models of the structural behaviour can be built, thus improving the seismic fragility estimation and the dam control. In this context, the observations recorded by the monitoring systems are a powerful source of information.
In this thesis two Bayesian frameworks for Structural Health Monitoring (SHM) of existing concrete gravity dams are proposed. On the one hand, the first proposed framework is defined for static SHM, so the dam displacements are considered as Quantity of Interest (QI). On the other hand, a dynamic SHM framework is defined by assuming the modal characteristics of the system as QI. In this second case an innovative numerical algorithm is proposed to solve the well-known mode matching problem without using the concept of system mode shapes or objective functions. Finally, a procedure based on the Optimal Bayesian Experimental Design is proposed in order to design the devices layout by optimizing the probability of damage detection. In all the three procedures the general Polynomial Chaos Expansion (gPCE) is widely used in order to strongly reduce the computational burden, thus making possible the application of the proposed procedure even without High Performance Computing (HPC).
Two real large concrete gravity dams are analysed in order to show the effectiveness of the proposed procedures in the real world.
In the first part of the thesis an extended literature review on the fragility assessment of concrete gravity dams and the application of SHM is presented. Afterwards, the statistical tools used for the definition of the proposed procedures are introduced. Finally, before the presentation of SHM frameworks, the main sources of uncertainties in the numerical analysis of concrete gravity dams are discussed in order to quantify their effects on the model outputs.
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(10702392), Alana K. Lund. "Bayesian Identification of Nonlinear Structural Systems: Innovations to Address Practical Uncertainty." Thesis, 2021.
Find full textAbstract:
The ability to rapidly assess the condition of a structure in a manner which enables the accurate prediction of its remaining capacity has long been viewed as a crucial step in allowing communities to make safe and efficient use of their public infrastructure. This objective has become even more relevant in recent years as both the interdependency and state of deterioration in infrastructure systems throughout the world have increased. Current practice for structural condition assessment emphasizes visual inspection, in which trained professionals will routinely survey a structure to estimate its remaining capacity. Though these methods have the ability to monitor gross structural changes, their ability to rapidly and cost-effectively assess the detailed condition of the structure with respect to its future behavior is limited.Vibration-based monitoring techniques offer a promising alternative to this approach. As opposed to visually observing the surface of the structure, these methods judge its condition and infer its future performance by generating and updating models calibrated to its dynamic behavior. Bayesian inference approaches are particularly well suited to this model updating problem as they are able to identify the structure using sparse observations while simultaneously assessing the uncertainty in the identified parameters. However, a lack of consensus on efficient methods for their implementation to full-scale structural systems has led to a diverse set of Bayesian approaches, from which no clear method can be selected for full-scale implementation. The objective of this work is therefore to assess and enhance those techniques currently used for structural identification and make strides toward developing unified strategies for robustly implementing them on full-scale structures. This is accomplished by addressing several key research questions regarding the ability of these methods to overcome issues in identifiability, sensitivity to uncertain experimental conditions, and scalability. These questions are investigated by applying novel adaptations of several prominent Bayesian identification strategies to small-scale experimental systems equipped with nonlinear devices. Through these illustrative examples I explore the robustness and practicality of these algorithms, while also considering their extensibility to higher-dimensional systems. Addressing these core concerns underlying full-scale structural identification will enable the practical application of Bayesian inference techniques and thereby enhance the ability of communities to detect and respond to the condition of their infrastructure.
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Bosche, Frederic. "Automated Recognition of 3D CAD Model Objects in Dense Laser Range Point Clouds." Thesis, 2008. http://hdl.handle.net/10012/3849.
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There is shift in the Architectural / Engineering / Construction and Facility Management (AEC&FM) industry toward performance-driven projects. Assuring
good performance requires efficient and reliable performance control processes.
However, the current state of the AEC&FM industry is that control processes are
inefficient because they generally rely on manually intensive, inefficient, and often
inaccurate data collection techniques.
Critical performance control processes include progress tracking and dimensional
quality control. These particularly rely on the accurate and efficient collection
of the as-built three-dimensional (3D) status of project objects. However, currently available
techniques for as-built 3D data collection are extremely inefficient, and provide
partial and often inaccurate information. These limitations have a negative impact
on the quality of decisions made by project managers and consequently on project
success.
This thesis presents an innovative approach for Automated 3D Data Collection
(A3dDC). This approach takes advantage of Laser Detection and Ranging
(LADAR), 3D Computer-Aided-Design (CAD) modeling and registration technologies.
The performance of this approach is investigated with a first set of experimental
results obtained with real-life data. A second set of experiments then
analyzes the feasibility of implementing, based on the developed approach, automated
project performance control (APPC) applications such as automated project
progress tracking and automated dimensional quality control. Finally, other applications
are identified including planning for scanning and strategic scanning.
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Ajith, V. "Wave Propagation in Healthy and Defective Composite Structures under Deterministic and Non-Deterministic Framework." Thesis, 2012. http://hdl.handle.net/2005/3253.
Full textAbstract:
Composite structures provide opportunities for weight reduction, material tailoring and integrating control surfaces with embedded transducers, which are not possible in conventional metallic structures. As a result there is a substantial increase in the use of composite materials in aerospace and other major industries, which has necessitated the need for structural health monitoring(SHM) of aerospace structures. In the context of SHM of aircraft structures, there are many areas, which are still not explored and need deep investigation. Among these, one of the major areas is the development of efficient damage models for complex composite structures, like stiffened structures, box-type structures, which are the building blocks of an aircraft wing structure. Quantification of the defect due to porosity and especially the methods for identifying the porous regions in a composite structure is another such area, which demands extensive research. In aircraft structures, it is not advisable for the structures, to have high porosity content, since it can initiate common defects in composites such as, delamination, matrix cracks etc.. In fact, there is need for a high frequency analysis to detect defects in such complex structures and also to detect damages, where the change in the stiffness due to the damage is very small. Lamb wave propagation based method is one of the efficient high frequency wave based method for damage detection and are extensively used for detecting small damages, which is essentially needed in aircraft industry. However, in order, to develop an efficient Lamb wave based SHM system, we also need an efficient computational wave propagation model. Developing an efficient computational wave propagation model for complex structures is still a challenging area. One of the major difficulty is its computational expense, when the analysis is performed using conventional FEM. However, for 1D And 2D composite structures, frequency domain spectral finite element method (SFEM), which are very effective in sensing small stiffness changes due to a defect in a structure, is one of the efficient tool for developing computationally efficient and accurate wave based damage models. In this work, we extend the efficiency of SFEM in developing damage models, for detecting damages in built-up composite structures and porous composite structure. Finally, in reality, the nature of variability of the material properties in a composite structure, created a variety of structural problems, in which the uncertainties in different parameters play a major part. Uncertainties can be due to the lack of good knowledge of material properties or due to the change in the load and support condition with the change in environmental variables such as temperature, humidity and pressure. The modeling technique is also one of the major sources of uncertainty, in the analysis of composites. In fact, when the variations are large, we can find in the literatures available that the probabilistic models are advantageous than the deterministic ones. Further, without performing a proper uncertain wave propagation analysis, to characterize the effect of uncertainty in different parameters, it is difficult to maintain the reliability of the results predicted by SFEM based damage models. Hence, in this work, we also study the effect of uncertainty in different structural parameters on the performance of the damage models, based on the models developed in the present work.
First, two SFEM based models, one based on the method of assembling 2D spectral elements and the other based on the concept of coupling 2D and 1D spectral elements, are developed to perform high frequency wave propagation analysis of some of the commonly used built-up composite structures. The SFEM model developed using the plate-beam coupling approach is then used to model wave propagation in a multiple stiffened structure and also to model the stiffened structures with different cross sections such as T-section, I-section and hat section.
Next, the wave propagation in a porous laminated composite beam is modeled using SFEM, based on the modified rule of mixture approach. Here, the material properties of the composite is obtained from the modified rule of mixture model, which are then used in SFEM to develop a new model for solving wave propagation problems in porous laminated composite beam. The influence of the porosity content on the parameters such as wave number, group speed and also the effect of variation in theses parameters on the time responses are studied first. Next, the effect of the length of the porous region (in the propagation direction) and the frequency of loading, on the time responses, is studied. The change in the time responses with the change in the porosity of the structure is used as a parameter to find the porosity content in a composite beam.
The SFEM models developed in this study is then used in the context of wave based damage detection, in the next study. First ,the actual measured response from a structure and the numerically obtained response from a SFEM model for porous laminated composite beam are used for the estimation of porosity, by solving a nonlinear optimization problem. The damage force indicator (DFI) technique is used to locate the porous region in a beam and also to find its length, using the measured wave propagation responses. DFI is derived from the dynamic stiffness matrix of the healthy structure along with the nodal displacements of the damaged structure. Next, a wave propagation based method is developed for modeling damage in stiffened composite structures, using SFEM, to locate and quantify the damage due to a crack and skin-stiffener debonding. The method of wave scattering and DFI technique are used to quantify the damage in the stiffened structure.
In the uncertain wave propagation analysis, a study on the uncertainty in material parameters on the wave propagation responses in a healthy metallic beam structure is performed first. Both modulus of elasticity and density are considered uncertain and the analysis is performed using Monte-Carlo simulation (MCS) under the environment of SFEM. The randomness in the material properties are characterized by three different distributions namely normal, Weibul and extreme value distribution and their effect on wave propagation, in beam is investigated. Even a study is performed on the usage of different beam theories and their uncertain responses due to dynamic impulse load.
A study is also conducted to analyze the wave propagation response In a composite structure in an uncertain environment using Neumann expansion blended with Monte-Carlo simulation (NE-MCS) under the environment of SFEM. Neumann expansion method accelerates the MCS, which is required for composites as there are many number of uncertain variables. The effect of the parameters like, fiber orientation, lay-up sequence, number of layers and the layer thickness on the uncertain responses due to dynamic impulse load, is thoroughly analyzed.
Finally, a probabilistic sensitivity analysis is performed to estimate the sensitivity of uncertain material and fabrication parameters, on the SFEM based damage models for a porous laminated composite beam. MCS is coupled with SFEM, for the uncertain wave propagation analysis and the Kullback-Leibler relative entropy is used as the measure of sensitivity. The sensitivity of different input variables on the wave number, group speed and the values of DFI, are mainly considered in this study.
The thesis, written in nine chapters, presents a unified document on wave propagation in healthy and defective composite structure subjected to both deterministic and highly uncertain environment.