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Computation-effective structural performance assessment using Gaussian process-based finite element model updating and reliability analysis
Structural health monitoring data has been widely acknowledged as a significant source for evaluating the performance and health conditions of structures. However, a holistic framework that efficiently incorporates monitored data into structural identification and, in turn, provides a realistic life...
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| Main Authors: | , , , |
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| Format: | Default Article |
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2020
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| Online Access: | https://hdl.handle.net/2134/19902130.v1 |
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| _version_ | 1823266563831103488 |
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| author | Hans Moravej Tommy HT Chan Andre Jesus Khac-Duy Nguyen |
| author_facet | Hans Moravej Tommy HT Chan Andre Jesus Khac-Duy Nguyen |
| author_sort | Hans Moravej (12655246) |
| collection | Figshare |
| description | Structural health monitoring data has been widely acknowledged as a significant source for evaluating the performance and health conditions of structures. However, a holistic framework that efficiently incorporates monitored data into structural identification and, in turn, provides a realistic life-cycle performance assessment of structures is yet to be established. There are different sources of uncertainty, such as structural parameters, computer model bias and measurement errors. Neglecting to account for these factors results in unreliable structural identifications, consequent financial losses, and a threat to the safety of structures and human lives. This paper proposes a new framework for structural performance assessment that integrates a comprehensive probabilistic finite element model updating approach, which deals with various structural identification uncertainties and structural reliability analysis. In this framework, Gaussian process surrogate models are replaced with a finite element model and its associate discrepancy function to provide a computationally efficient and all-round uncertainty quantification. Herein, the structural parameters that are most sensitive to measured structural dynamic characteristics are investigated and used to update the numerical model. Sequentially, the updated model is applied to compute the structural capacity with respect to loading demand to evaluate its as-is performance. The proposed framework’s feasibility is investigated and validated on a large lab-scale box girder bridge in two different health states, undamaged and damaged, with the latter state representing changes in structural parameters resulted from overloading actions. The results from the box girder bridge indicate a reduced structural performance evidenced by a significant drop in the structural reliability index and an increased probability of failure in the damaged state. The results also demonstrate that the proposed methodology contributes to more reliable judgment about structural safety, which in turn enables more informed maintenance decisions to be made. |
| format | Default Article |
| id | rr-article-19902130 |
| institution | Loughborough University |
| publishDate | 2020 |
| record_format | Figshare |
| spelling | rr-article-199021302020-09-13T00:00:00Z Computation-effective structural performance assessment using Gaussian process-based finite element model updating and reliability analysis Hans Moravej (12655246) Tommy HT Chan (12655249) Andre Jesus (12381421) Khac-Duy Nguyen (12655252) Mechanical engineering not elsewhere classified Finite element model updating Structural dynamic Reliability analysis Gaussian process Box girder bridge & modular Bayesian approach Mechanical Engineering Structural health monitoring data has been widely acknowledged as a significant source for evaluating the performance and health conditions of structures. However, a holistic framework that efficiently incorporates monitored data into structural identification and, in turn, provides a realistic life-cycle performance assessment of structures is yet to be established. There are different sources of uncertainty, such as structural parameters, computer model bias and measurement errors. Neglecting to account for these factors results in unreliable structural identifications, consequent financial losses, and a threat to the safety of structures and human lives. This paper proposes a new framework for structural performance assessment that integrates a comprehensive probabilistic finite element model updating approach, which deals with various structural identification uncertainties and structural reliability analysis. In this framework, Gaussian process surrogate models are replaced with a finite element model and its associate discrepancy function to provide a computationally efficient and all-round uncertainty quantification. Herein, the structural parameters that are most sensitive to measured structural dynamic characteristics are investigated and used to update the numerical model. Sequentially, the updated model is applied to compute the structural capacity with respect to loading demand to evaluate its as-is performance. The proposed framework’s feasibility is investigated and validated on a large lab-scale box girder bridge in two different health states, undamaged and damaged, with the latter state representing changes in structural parameters resulted from overloading actions. The results from the box girder bridge indicate a reduced structural performance evidenced by a significant drop in the structural reliability index and an increased probability of failure in the damaged state. The results also demonstrate that the proposed methodology contributes to more reliable judgment about structural safety, which in turn enables more informed maintenance decisions to be made. 2020-09-13T00:00:00Z Text Journal contribution 2134/19902130.v1 https://figshare.com/articles/journal_contribution/Computation-effective_structural_performance_assessment_using_Gaussian_process-based_finite_element_model_updating_and_reliability_analysis/19902130 CC BY-NC-ND 4.0 |
| spellingShingle | Mechanical engineering not elsewhere classified Finite element model updating Structural dynamic Reliability analysis Gaussian process Box girder bridge & modular Bayesian approach Mechanical Engineering Hans Moravej Tommy HT Chan Andre Jesus Khac-Duy Nguyen Computation-effective structural performance assessment using Gaussian process-based finite element model updating and reliability analysis |
| title | Computation-effective structural performance assessment using Gaussian process-based finite element model updating and reliability analysis |
| title_full | Computation-effective structural performance assessment using Gaussian process-based finite element model updating and reliability analysis |
| title_fullStr | Computation-effective structural performance assessment using Gaussian process-based finite element model updating and reliability analysis |
| title_full_unstemmed | Computation-effective structural performance assessment using Gaussian process-based finite element model updating and reliability analysis |
| title_short | Computation-effective structural performance assessment using Gaussian process-based finite element model updating and reliability analysis |
| title_sort | computation-effective structural performance assessment using gaussian process-based finite element model updating and reliability analysis |
| topic | Mechanical engineering not elsewhere classified Finite element model updating Structural dynamic Reliability analysis Gaussian process Box girder bridge & modular Bayesian approach Mechanical Engineering |
| url | https://hdl.handle.net/2134/19902130.v1 |