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Academic literature on the topic 'Model Updating, Structural Health Monitoring'

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Published: 14 March 2023

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Journal articles on the topic "Model Updating, Structural Health Monitoring"

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Haidarpour, Amirabbas, and Kong Fah Tee. "Finite Element Model Updating for Structural Health Monitoring." Structural Durability & Health Monitoring 14, no. 1 (2020): 1–17. http://dx.doi.org/10.32604/sdhm.2020.08792.

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Rocchetta, Roberto, Matteo Broggi, Quentin Huchet, and Edoardo Patelli. "On-line Bayesian model updating for structural health monitoring." Mechanical Systems and Signal Processing 103 (March 2018): 174–95. http://dx.doi.org/10.1016/j.ymssp.2017.10.015.

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Dan, Danhui, Tong Yang, and Jiongxin Gong. "Intelligent Platform for Model Updating in a Structural Health Monitoring System." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/628619.

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The main aim of this study is to develop an automated smart software platform to improve the time-consuming and laborious process of model updating. We investigate the key techniques of model updating based on intelligent optimization algorithms, that is, accuracy judgment methods for basic finite element model, parameter choice theory based on sensitivity analysis, commonly used objective functions and their construction methods, particle swarm optimization, and other intelligent optimization algorithms. An intelligent model updating prototype software framework is developed using the commercial software systems ANSYS and MATLAB. A parameterized finite element modeling technique is proposed to suit different bridge types and different model updating requirements. An objective function library is built to fit different updating targets. Finally, two case studies are conducted to verify the feasibility of the techniques used by the proposed software platform.
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Schommer, Sebastian, Viet Ha Nguyen, Stefan Maas, and Arno Zürbes. "Model updating for structural health monitoring using static and dynamic measurements." Procedia Engineering 199 (2017): 2146–53. http://dx.doi.org/10.1016/j.proeng.2017.09.156.

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Dey, Palash, V. Akhil, and A. I. Laskar. "Application of Smartphone and Model Updating Technique in Structural Health Monitoring." Arabian Journal for Science and Engineering 44, no. 5 (September 28, 2018): 4819–28. http://dx.doi.org/10.1007/s13369-018-3565-8.

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Ching, Jianye, Matthew Muto, and James L. Beck. "Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler." Computer-Aided Civil and Infrastructure Engineering 21, no. 4 (May 2006): 242–57. http://dx.doi.org/10.1111/j.1467-8667.2006.00432.x.

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Betti, Michele, Salvatore Giacomo Morano, Gianni Bartoli, Giacomo Zini, and Paolo Spinelli. "Structural health monitoring of a masonry arch bridge: modal identification and model updating." International Journal of Masonry Research and Innovation 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijmri.2022.10052514.

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Yang, J., H. F. Lam, and J. Hu. "Ambient Vibration Test, Modal Identification and Structural Model Updating Following Bayesian Framework." International Journal of Structural Stability and Dynamics 15, no. 07 (August 31, 2015): 1540024. http://dx.doi.org/10.1142/s0219455415400246.

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Structural health monitoring (SHM) of civil engineering structures based on vibration data includes three main components: ambient vibration test, modal identification and model updating. This paper discussed these three components in detail and proposes a general framework of SHM for practical application. First, a fast Bayesian modal identification method based on Fast Fourier Transform (FFT) is introduced for efficiently extracting modal parameters together with the corresponding uncertainties from ambient vibration data. A recently developed Bayesian model updating method using Markov chain Monte Carlo simulation (MCMCS) is then discussed. To illustrate the performance of the proposed modal identification and model updating methods, a scale-down transmission tower is investigated. Ambient vibration test is conducted on the target structure to obtain modal parameters. By using the measured modal parameters, model updating is carried out. The MCMC-based Bayesian model updating method can efficiently evaluate the posterior marginal PDFs of the uncertain parameters without calculating high-dimension numerical integration, which provides posterior uncertainties for the target systems.
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Mordini, Andrea, Konstantin Savov, and Helmut Wenzel. "The Finite Element Model Updating: A Powerful Tool for Structural Health Monitoring." Structural Engineering International 17, no. 4 (November 2007): 352–58. http://dx.doi.org/10.2749/101686607782359010.

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Ching, Jianye, and James L. Beck. "New Bayesian Model Updating Algorithm Applied to a Structural Health Monitoring Benchmark." Structural Health Monitoring: An International Journal 3, no. 4 (December 2004): 313–32. http://dx.doi.org/10.1177/1475921704047499.

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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 Silva
Abstract: 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.
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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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Books on the topic "Model Updating, Structural Health Monitoring"

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Feng, Maria Q. Long-term structural performance monitoring of bridges: Development of baseline model and methodology for health monitoring and damage assessment. Sacramento, Calif: California Dept. of Transportation, Division of Research and Innovation, 2008.

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Book chapters on the topic "Model Updating, Structural Health Monitoring"

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Soyoz, Serdar. "Model Updating Techniques for Structures Under Seismic Excitation." In Seismic Structural Health Monitoring, 199–216. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13976-6_8.

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Sehgal, Shankar, and Harmesh Kumar. "Damage Detection Using Derringer’s Function based Weighted Model Updating Method." In Structural Health Monitoring, Volume 5, 241–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04570-2_27.

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Carroll, Michael, Austin Downey, Jacob Dodson, Jonathan Hong, and James Scheppegrell. "Subsecond Model Updating for High-Rate Structural Health Monitoring." In Topics in Modal Analysis & Testing, Volume 8, 201–6. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47717-2_19.

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Zhu, Yi-Chen, David Wagg, Elizabeth Cross, and Robert Barthorpe. "Real-Time Digital Twin Updating Strategy Based on Structural Health Monitoring Systems." In Model Validation and Uncertainty Quantification, Volume 3, 55–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0_6.

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Heo, Gwang Hee, Joon Ryong Jeon, Chin Ok Lee, Gui Lee, and Woo Sang Lee. "FE Model Updating for Health Monitoring of Structures and its Experimental Verification by Damage Detection." In Advanced Nondestructive Evaluation I, 268–72. Stafa: Trans Tech Publications Ltd., 2006. http://dx.doi.org/10.4028/0-87849-412-x.268.

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De Roeck, Guido. "Model–Based Methods of Damage Identification of Structures Under Seismic Excitation." In Seismic Structural Health Monitoring, 237–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13976-6_10.

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Xu, Y. F., W. D. Zhu, J. Liu, and Y. M. Shao. "Non-Model-Based Crack Identification Using Measured Mode Shapes." In Structural Health Monitoring, Volume 5, 279–97. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04570-2_31.

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Gallina, Alberto, Paweł Paćko, and Łukasz Ambroziński. "Model Assisted Probability of Detection in Structural Health Monitoring." In Advanced Structural Damage Detection, 57–72. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118536148.ch3.

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Donajkowski, Hannah, Salma Leyasi, Gregory Mellos, Chuck R. Farrar, Alex Scheinker, Jin-Song Pei, and Nicholas A. J. Lieven. "Comparison of Complexity Measures for Structural Health Monitoring." In Model Validation and Uncertainty Quantification, Volume 3, 27–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0_3.

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Finzi Neto, Roberto Mendes, and Jose dos Reis Vieira de Moura. "Fundamental Concepts for Impedance-based Structural Health Monitoring." In Model-based and Signal-Based Inverse Methods, 443–71. Brasilia: Biblioteca Central da Universidade de Brasilia, 2022. http://dx.doi.org/10.4322/978-65-86503-71-5.c12.

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Conference papers on the topic "Model Updating, Structural Health Monitoring"

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IGEA, FELIPE, MANOLIS N. CHATZIS, and ALICE CICIRELLO. "STRUCTURAL MODEL UPDATING USING VARIATIONAL INFERENCE." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36282.

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Monte Carlo sampling approaches are frequently used for probabilistic model updating of physics-based models under parametric uncertainty due to their high accuracy. The model updating framework produces a model that represents the real system more accurately than the prior knowledge or assumptions. This statistically updated model may prove useful if Structural Health Monitoring (SHM) techniques are to be applied. However, the updating of the models requires the use of a high number of samples, implying a high computational cost. Another additional disadvantage of these methods is that most of them require the calibration of a high number of parameters for their algorithm to become sampling efficient. Variational inference (VI) is an alternative approach for inference often used by the machine learning community. An optimization algorithm is employed to choose from a family of distributions the member that best approximates the posterior. In the method described in this paper the variational posterior that maximises the evidence lower bound (ELBO) is chosen. An approach based on VI is proposed and implemented on two different numerical examples to infer the uncertain parameters by postulating a variational posterior distribution given by a multivariate Gaussian approximation. It has been found that the number of samples required for the calculation of the posterior is reduced compared with Monte Carlo sampling approaches, however this occurs at the cost of some accuracy. The methodology will be helpful for the development of enhanced SHM strategies that require fast inference under a limited computational budget.
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ZENG, JICE, YOUNG HOON HOON KIM, and SHIQIANG QIN. "BAYESIAN MODEL UPDATING FOR A CABLE-STAYED PEDESTRIAN BRIDGE USING DREAM AND KRIGING MODEL." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36256.

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Modeling error and measurement noise are inevitable and lead to a significant discrepancy between Finite element model (FEM) and a real structure. Finite element model updating (FEMU) is, therefore, necessary to match the measured data with a predicted response from FEM for advancing structural health monitoring (SHM). Bayesian approach has been proposed to identify the most probable values (MPVs) of physical parameters and provide parameters’ uncertainties. However, the current Bayesian approach has challenges in high-dimensional problems and requires high computational costs in the complex structure. In this study, a new Bayesian updating framework is proposed using Differential Evolution Adaptive Metropolis (DREAM) sampling method with a variance-based global sensitivity analysis (GSA) and Kriging model to enhance the Bayesian approach’s performance and computational efficiency. Firstly, variance-based GSA is used to eliminate insignificant parameters to measured responses and reduce model dimensionality. Secondly, a Kriging model is employed as a surrogate of the time-consuming FE model for reducing the computational burden. DREAM is essentially a multi-chain sampling method, which parallelly runs different paths for all possible solutions and accurately approximates the posterior distribution density function (PDF) for the Bayesian approach. The demonstration of the proposed updating framework of a real-world cable-stayed pedestrian bridge is presented.
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BRUNS, MARLENE, BENEDIKT HOFMEISTER, CLEMENS HÜBLER, and RAIMUND ROLFES. "Damage Localization Via Model Updating Using a Damage Distribution Function." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32202.

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SIMPSON, THOMAS, VASILIS DERTIMANIS, COSTAS PAPADIMITRIOU, and ELENI CHATZI. "On the Potential of Dynamic Sub-structuring Methods for Model Updating." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32500.

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HUANG, YONG, JAMES L. BECK, and HUI LI. "Multi-Task Sparse Bayesian Learning For Model Updating In Structural Health Monitoring." In Structural Health Monitoring 2017. Lancaster, PA: DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/shm2017/14097.

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KERNICKY, TIMOTHY, MATTHEW WHELAN, USMAN RAUF, and EHAB AL-SHAER. "Damage Detection in a Laboratory Model Using a Nonlinear Constraint Satisfaction Processor for Finite Element Model Updating." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/175.

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FANG, ZHIHONG, JINGYU HUANG, XIAONONG WANG, LIANG ZHAO, SHUOWEI WANG, ZIYANG ZHANG, and DEXIANG LI. "RESEARCH ON MODEL UPDATING METHOD OF HIGH- SPEED MAGLEV GUIDEWAY BASED ON WAVELET TRANSFORM AND OPTIMIZATION ALGORITHM." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36290.

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Because the maglev guideway is a vital infrastructure of high-speed maglev train, it is of great significance for the guideway structure to analyze dynamic response and assess structural health of the guideway in research. This paper proposed a model updating method of the high-speed maglev guideway based on wavelet transform(WT) and optimization algorithm, which are used to analyze the dynamic response and assess health. Taking the 600 km/h high-speed maglev test vehicle as the excitation, the field test was carried out on the High-speed Maglev Test Line. The measured dynamic responses of the guideway were obtained, and the measured modal parameters were identified by WT. The finite element(FE) model of guideway was established, considering the elastic boundary conditions and material properties, and the initial modal parameters of the guideway were obtained. Based on the FE model, the response surface model of the guideway was constructed and the objective function of the simulated modal parameters and the measured modal parameters was established. Considering the constraint conditions, the optimal solution of the objective function was found by the optimization algorithm, and the elastic boundary conditions and material properties of the FE model were optimized and updated. The research results indicated that the dynamic response of the updated FE model was highly correlated with the measured dynamic response and proved strongly the effectiveness of the proposed method. A more accurate model updating method for the dynamic response analysis and health assessment of the high-speed maglev guideway was provided by this paper.
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SABAMEHR, ARDALAN, CHAEWOON LIM, and ASHUTOSH BAGCHI. "Updating the Mathematical Models of Bridges Using Data-driven Techniques." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/156.

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SCHRODER, KARSTEN, and RAIMUND ROLFES. "Application of a Finite Element Model Updating Approach to Damage Localization at Offshore Wind Energy Converters." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/341.

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Ereiz, Suzana, and Ivan Duvnjak. "Hybrid model updating based on structural health monitoring in structural dynamics." In 6th Symposium on Doctoral Studies in Civil Engineering. University of Zagreb Faculty of Civil Engineering, 2019. http://dx.doi.org/10.5592/co/phdsym.2020.10.

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Reports on the topic "Model Updating, Structural Health Monitoring"

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Taylor, Stuart G. A Multi-scale Approach to Statistical and Model-based Structural Health Monitoring with Application to Embedded Sensing for Wind Energy. Office of Scientific and Technical Information (OSTI), January 2013. http://dx.doi.org/10.2172/1059871.

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