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Application of response surface-corrected finite element model and Bayesian neural networks to predict the dynamic response of forth road bridges under strong winds
With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive...
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| Format: | Default Article |
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2024
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| Online Access: | https://hdl.handle.net/2134/26075812.v1 |
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| _version_ | 1823789300566720512 |
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| author | Yan Liu Liangliang Hu Xiaolin Meng Yan Bao Craig Hancock |
| author_facet | Yan Liu Liangliang Hu Xiaolin Meng Yan Bao Craig Hancock |
| author_sort | Yan Liu (20699418) |
| collection | Figshare |
| description | With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive approach, combining the influence of both physical model-driven and data-driven methodologies. In this paper, two methods are proposed to predict the displacement and dynamic response of structures under strong winds, namely, a Bayesian Neural Network (BNN) model based on Bayesian inference and a finite element model (FEM) method modified based on genetic algorithms (GAs) and multi-objective optimization (MOO) using response surface methodology (RSM). The characteristics of these approaches in predicting the dynamic response of large-span bridges are explored, and a comparative analysis is conducted to evaluate their differences in computational accuracy, efficiency, model complexity, interpretability, and comprehensiveness. The characteristics of the two methods were evaluated using data collected on the Forth Road Bridge (FRB) as an example under unusual weather conditions with strong wind action. This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. The research results show that the BNN model achieved higher (Formula presented.) values for predicting the Y and Z displacements (0.9073 and 0.7969, respectively) compared to the FEM model (0.6167 and 0.6283). The BNN model exhibited significantly faster computation, taking only 20 s, while the FEM model required 5 h. However, the physical model provided higher interpretability and the ability to predict the dynamic response of the entire structure. These findings help to promote the further integration of these two approaches to obtain an accurate and comprehensive dual-driven approach for predicting the structural dynamic response of large-span bridge structures affected by strong wind loading. |
| format | Default Article |
| id | rr-article-26075812 |
| institution | Loughborough University |
| publishDate | 2024 |
| record_format | Figshare |
| spelling | rr-article-260758122024-03-25T00:00:00Z Application of response surface-corrected finite element model and Bayesian neural networks to predict the dynamic response of forth road bridges under strong winds Yan Liu (20699418) Liangliang Hu (143036) Xiaolin Meng (7623836) Yan Bao (723293) Craig Hancock (9038162) Ecology Analytical chemistry Engineering Electrical engineering Electronics, sensors and digital hardware Environmental sciences Environmental management Distributed computing and systems software Digital twin Displacement prediction Structural health monitoring Large-span bridge structure Strong winds With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive approach, combining the influence of both physical model-driven and data-driven methodologies. In this paper, two methods are proposed to predict the displacement and dynamic response of structures under strong winds, namely, a Bayesian Neural Network (BNN) model based on Bayesian inference and a finite element model (FEM) method modified based on genetic algorithms (GAs) and multi-objective optimization (MOO) using response surface methodology (RSM). The characteristics of these approaches in predicting the dynamic response of large-span bridges are explored, and a comparative analysis is conducted to evaluate their differences in computational accuracy, efficiency, model complexity, interpretability, and comprehensiveness. The characteristics of the two methods were evaluated using data collected on the Forth Road Bridge (FRB) as an example under unusual weather conditions with strong wind action. This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. The research results show that the BNN model achieved higher (Formula presented.) values for predicting the Y and Z displacements (0.9073 and 0.7969, respectively) compared to the FEM model (0.6167 and 0.6283). The BNN model exhibited significantly faster computation, taking only 20 s, while the FEM model required 5 h. However, the physical model provided higher interpretability and the ability to predict the dynamic response of the entire structure. These findings help to promote the further integration of these two approaches to obtain an accurate and comprehensive dual-driven approach for predicting the structural dynamic response of large-span bridge structures affected by strong wind loading.<p></p> 2024-03-25T00:00:00Z Text Journal contribution 2134/26075812.v1 https://figshare.com/articles/journal_contribution/Application_of_response_surface-corrected_finite_element_model_and_Bayesian_neural_networks_to_predict_the_dynamic_response_of_forth_road_bridges_under_strong_winds/26075812 CC BY 4.0 |
| spellingShingle | Ecology Analytical chemistry Engineering Electrical engineering Electronics, sensors and digital hardware Environmental sciences Environmental management Distributed computing and systems software Digital twin Displacement prediction Structural health monitoring Large-span bridge structure Strong winds Yan Liu Liangliang Hu Xiaolin Meng Yan Bao Craig Hancock Application of response surface-corrected finite element model and Bayesian neural networks to predict the dynamic response of forth road bridges under strong winds |
| title | Application of response surface-corrected finite element model and Bayesian neural networks to predict the dynamic response of forth road bridges under strong winds |
| title_full | Application of response surface-corrected finite element model and Bayesian neural networks to predict the dynamic response of forth road bridges under strong winds |
| title_fullStr | Application of response surface-corrected finite element model and Bayesian neural networks to predict the dynamic response of forth road bridges under strong winds |
| title_full_unstemmed | Application of response surface-corrected finite element model and Bayesian neural networks to predict the dynamic response of forth road bridges under strong winds |
| title_short | Application of response surface-corrected finite element model and Bayesian neural networks to predict the dynamic response of forth road bridges under strong winds |
| title_sort | application of response surface-corrected finite element model and bayesian neural networks to predict the dynamic response of forth road bridges under strong winds |
| topic | Ecology Analytical chemistry Engineering Electrical engineering Electronics, sensors and digital hardware Environmental sciences Environmental management Distributed computing and systems software Digital twin Displacement prediction Structural health monitoring Large-span bridge structure Strong winds |
| url | https://hdl.handle.net/2134/26075812.v1 |