Literatura académica sobre el tema "Model Updating, Structural Health Monitoring"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Model Updating, Structural Health Monitoring".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Model Updating, Structural Health Monitoring"
Haidarpour, Amirabbas y Kong Fah Tee. "Finite Element Model Updating for Structural Health Monitoring". Structural Durability & Health Monitoring 14, n.º 1 (2020): 1–17. http://dx.doi.org/10.32604/sdhm.2020.08792.
Texto completoRocchetta, Roberto, Matteo Broggi, Quentin Huchet y Edoardo Patelli. "On-line Bayesian model updating for structural health monitoring". Mechanical Systems and Signal Processing 103 (marzo de 2018): 174–95. http://dx.doi.org/10.1016/j.ymssp.2017.10.015.
Texto completoDan, Danhui, Tong Yang y 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.
Texto completoSchommer, Sebastian, Viet Ha Nguyen, Stefan Maas y 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.
Texto completoDey, Palash, V. Akhil y A. I. Laskar. "Application of Smartphone and Model Updating Technique in Structural Health Monitoring". Arabian Journal for Science and Engineering 44, n.º 5 (28 de septiembre de 2018): 4819–28. http://dx.doi.org/10.1007/s13369-018-3565-8.
Texto completoChing, Jianye, Matthew Muto y James L. Beck. "Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler". Computer-Aided Civil and Infrastructure Engineering 21, n.º 4 (mayo de 2006): 242–57. http://dx.doi.org/10.1111/j.1467-8667.2006.00432.x.
Texto completoBetti, Michele, Salvatore Giacomo Morano, Gianni Bartoli, Giacomo Zini y Paolo Spinelli. "Structural health monitoring of a masonry arch bridge: modal identification and model updating". International Journal of Masonry Research and Innovation 1, n.º 1 (2022): 1. http://dx.doi.org/10.1504/ijmri.2022.10052514.
Texto completoYang, J., H. F. Lam y J. Hu. "Ambient Vibration Test, Modal Identification and Structural Model Updating Following Bayesian Framework". International Journal of Structural Stability and Dynamics 15, n.º 07 (31 de agosto de 2015): 1540024. http://dx.doi.org/10.1142/s0219455415400246.
Texto completoMordini, Andrea, Konstantin Savov y Helmut Wenzel. "The Finite Element Model Updating: A Powerful Tool for Structural Health Monitoring". Structural Engineering International 17, n.º 4 (noviembre de 2007): 352–58. http://dx.doi.org/10.2749/101686607782359010.
Texto completoChing, Jianye y James L. Beck. "New Bayesian Model Updating Algorithm Applied to a Structural Health Monitoring Benchmark". Structural Health Monitoring: An International Journal 3, n.º 4 (diciembre de 2004): 313–32. http://dx.doi.org/10.1177/1475921704047499.
Texto completoTesis sobre el tema "Model Updating, Structural Health Monitoring"
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.
Texto completoMoravej, 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.
Texto completoSmith, Chandler B. "Sparsity Constrained Inverse Problems - Application to Vibration-based Structural Health Monitoring". ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1143.
Texto completoZolghadri, Navid. "Short and Long-Term Structural Health Monitoring of Highway Bridges". DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/5626.
Texto completoLee, 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.
Texto completoAREZZO, 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.
Texto completoStructural 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.
Texto completoApproved for entry into archive by Felipe Augusto Arakaki (arakaki@reitoria.unesp.br) on 2016-04-05T14:42:00Z (GMT) No. of bitstreams: 1 shiki_sd_dr_ilha.pdf: 10090180 bytes, checksum: c44d2ebecbed6d011cf61ceabdfd3494 (MD5)
Made available in DSpace on 2016-04-05T14:42:00Z (GMT). No. of bitstreams: 1 shiki_sd_dr_ilha.pdf: 10090180 bytes, checksum: c44d2ebecbed6d011cf61ceabdfd3494 (MD5) Previous issue date: 2016-03-04
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.
Texto completoAbstract: 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.
Texto completoWang, 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.
Texto completoLibros sobre el tema "Model Updating, Structural Health Monitoring"
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.
Buscar texto completoCapítulos de libros sobre el tema "Model Updating, Structural Health Monitoring"
Soyoz, Serdar. "Model Updating Techniques for Structures Under Seismic Excitation". En Seismic Structural Health Monitoring, 199–216. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13976-6_8.
Texto completoSehgal, Shankar y Harmesh Kumar. "Damage Detection Using Derringer’s Function based Weighted Model Updating Method". En Structural Health Monitoring, Volume 5, 241–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04570-2_27.
Texto completoCarroll, Michael, Austin Downey, Jacob Dodson, Jonathan Hong y James Scheppegrell. "Subsecond Model Updating for High-Rate Structural Health Monitoring". En 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.
Texto completoZhu, Yi-Chen, David Wagg, Elizabeth Cross y Robert Barthorpe. "Real-Time Digital Twin Updating Strategy Based on Structural Health Monitoring Systems". En 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.
Texto completoHeo, Gwang Hee, Joon Ryong Jeon, Chin Ok Lee, Gui Lee y Woo Sang Lee. "FE Model Updating for Health Monitoring of Structures and its Experimental Verification by Damage Detection". En Advanced Nondestructive Evaluation I, 268–72. Stafa: Trans Tech Publications Ltd., 2006. http://dx.doi.org/10.4028/0-87849-412-x.268.
Texto completoDe Roeck, Guido. "Model–Based Methods of Damage Identification of Structures Under Seismic Excitation". En Seismic Structural Health Monitoring, 237–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13976-6_10.
Texto completoXu, Y. F., W. D. Zhu, J. Liu y Y. M. Shao. "Non-Model-Based Crack Identification Using Measured Mode Shapes". En Structural Health Monitoring, Volume 5, 279–97. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04570-2_31.
Texto completoGallina, Alberto, Paweł Paćko y Łukasz Ambroziński. "Model Assisted Probability of Detection in Structural Health Monitoring". En Advanced Structural Damage Detection, 57–72. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118536148.ch3.
Texto completoDonajkowski, Hannah, Salma Leyasi, Gregory Mellos, Chuck R. Farrar, Alex Scheinker, Jin-Song Pei y Nicholas A. J. Lieven. "Comparison of Complexity Measures for Structural Health Monitoring". En 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.
Texto completoFinzi Neto, Roberto Mendes y Jose dos Reis Vieira de Moura. "Fundamental Concepts for Impedance-based Structural Health Monitoring". En 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.
Texto completoActas de conferencias sobre el tema "Model Updating, Structural Health Monitoring"
IGEA, FELIPE, MANOLIS N. CHATZIS y ALICE CICIRELLO. "STRUCTURAL MODEL UPDATING USING VARIATIONAL INFERENCE". En Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36282.
Texto completoZENG, JICE, YOUNG HOON HOON KIM y SHIQIANG QIN. "BAYESIAN MODEL UPDATING FOR A CABLE-STAYED PEDESTRIAN BRIDGE USING DREAM AND KRIGING MODEL". En Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36256.
Texto completoBRUNS, MARLENE, BENEDIKT HOFMEISTER, CLEMENS HÜBLER y RAIMUND ROLFES. "Damage Localization Via Model Updating Using a Damage Distribution Function". En Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32202.
Texto completoSIMPSON, THOMAS, VASILIS DERTIMANIS, COSTAS PAPADIMITRIOU y ELENI CHATZI. "On the Potential of Dynamic Sub-structuring Methods for Model Updating". En Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32500.
Texto completoHUANG, YONG, JAMES L. BECK y HUI LI. "Multi-Task Sparse Bayesian Learning For Model Updating In Structural Health Monitoring". En Structural Health Monitoring 2017. Lancaster, PA: DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/shm2017/14097.
Texto completoKERNICKY, TIMOTHY, MATTHEW WHELAN, USMAN RAUF y EHAB AL-SHAER. "Damage Detection in a Laboratory Model Using a Nonlinear Constraint Satisfaction Processor for Finite Element Model Updating". En Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/175.
Texto completoFANG, ZHIHONG, JINGYU HUANG, XIAONONG WANG, LIANG ZHAO, SHUOWEI WANG, ZIYANG ZHANG y DEXIANG LI. "RESEARCH ON MODEL UPDATING METHOD OF HIGH- SPEED MAGLEV GUIDEWAY BASED ON WAVELET TRANSFORM AND OPTIMIZATION ALGORITHM". En Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36290.
Texto completoSABAMEHR, ARDALAN, CHAEWOON LIM y ASHUTOSH BAGCHI. "Updating the Mathematical Models of Bridges Using Data-driven Techniques". En Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/156.
Texto completoSCHRODER, KARSTEN y RAIMUND ROLFES. "Application of a Finite Element Model Updating Approach to Damage Localization at Offshore Wind Energy Converters". En Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/341.
Texto completoEreiz, Suzana y Ivan Duvnjak. "Hybrid model updating based on structural health monitoring in structural dynamics". En 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.
Texto completoInformes sobre el tema "Model Updating, Structural Health Monitoring"
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), enero de 2013. http://dx.doi.org/10.2172/1059871.
Texto completo