Дисертації з теми "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.
Повний текст джерелаMoravej, 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.
Повний текст джерелаSmith, Chandler B. "Sparsity Constrained Inverse Problems - Application to Vibration-based Structural Health Monitoring." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1143.
Повний текст джерелаZolghadri, Navid. "Short and Long-Term Structural Health Monitoring of Highway Bridges." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/5626.
Повний текст джерелаLee, 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.
Повний текст джерелаAREZZO, 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.
Повний текст джерелаStructural 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.
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
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.
Повний текст джерелаAbstract: 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
Al, Jailawi Samer Saadi Hussein. "Damage detection using angular velocity." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6539.
Повний текст джерелаWang, 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.
Повний текст джерелаBarthorpe, Robert James. "On model- and data-based approaches to structural health monitoring." Thesis, University of Sheffield, 2010. http://etheses.whiterose.ac.uk/1175/.
Повний текст джерелаMelvin, 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.
Повний текст джерелаKim, Jina. "Low-Power System Design for Impedance-Based Structural Health Monitoring." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/40400.
Повний текст джерелаPh. D.
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.
Повний текст джерелаM.S.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering
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.
Повний текст джерелаCataloged 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.
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.
Повний текст джерелаQC 20170420
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.
Повний текст джерелаPh. D.
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.
Повний текст джерелаDürager, 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.
Повний текст джерелаTobe, 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.
Повний текст джерелаIsidori, 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.
Повний текст джерелаdetection 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.
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.
Повний текст джерелаEbrahimian, 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.
Повний текст джерелаThis 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.
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.
Повний текст джерелаID: 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
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.
Повний текст джерелаRuffels, 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.
Повний текст джерелаMonavari, Benyamin. "SHM-based structural deterioration assessment." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132660/1/Benyamin%20Monavari%20Thesis.pdf.
Повний текст джерелаShmerling, 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.
Повний текст джерелаM.S.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering
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.
Повний текст джерелаChang, Minwoo. "Investigating and Improving Bridge Management System Methodologies Under Uncertainty." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/5039.
Повний текст джерелаVeta, 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.
Повний текст джерелаYano, Marcus Omori. "Extrapolation of autoregressive model for damage progression analysis /." Ilha Solteira, 2019. http://hdl.handle.net/11449/182287.
Повний текст джерелаResumo: 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
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.
Повний текст джерелаMountassir, Mahjoub El. "Surveillance d'intégrité des structures par apprentissage statistique : application aux structures tubulaires." Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0047.
Повний текст джерелаTo 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
"Feature and Statistical Model Development in Structural Health Monitoring." Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.38657.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Mechanical Engineering 2016
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.
Повний текст джерела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.
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/.
Повний текст джерелаThesis directed by Panos J. Antsaklis for the Department of Electrical Engineering. "April 2006." Includes bibliographical references (leaves 77-78).
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.
Повний текст джерела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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