Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm
ABSTRACT The research proposes a hybrid algorithm model that combines model‐driven and data‐driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a mu...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.13017 |
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| author | Chengzhong Gui Zhi Duan Zuwei Huang Zhiguo Sun Wei Qiao Yu Cheng |
| author_facet | Chengzhong Gui Zhi Duan Zuwei Huang Zhiguo Sun Wei Qiao Yu Cheng |
| author_sort | Chengzhong Gui |
| collection | DOAJ |
| description | ABSTRACT The research proposes a hybrid algorithm model that combines model‐driven and data‐driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi‐objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi‐source heterogeneous data, selection of key sub‐parameters using Principal Component Analysis (PCA), enhanced K‐means clustering analysis, determination of structural component target thresholds, time‐dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double‐layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub‐parameters that have significant impacts on the performance of structural components. Enhanced K‐means clustering analysis is carried out to effectively group and classify the identified key sub‐parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi‐parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan–Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double‐layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds. |
| format | Article |
| id | doaj-art-8789e81b50d0474c8b29423c43d00f40 |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-8789e81b50d0474c8b29423c43d00f402025-01-31T00:22:48ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.13017Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival AlgorithmChengzhong Gui0Zhi Duan1Zuwei Huang2Zhiguo Sun3Wei Qiao4Yu Cheng5Department of Civil Engineering Institute of Disaster Prevention Sanhe ChinaChina Highway Engineering Consulting Group Co., Ltd. Beijing ChinaHighway Monitoring & Response Center Ministry of Transport of the PRC Beijing ChinaDepartment of Civil Engineering Institute of Disaster Prevention Sanhe ChinaChina Highway Engineering Consulting Group Co., Ltd. Beijing ChinaChina Highway Engineering Consulting Group Co., Ltd. Beijing ChinaABSTRACT The research proposes a hybrid algorithm model that combines model‐driven and data‐driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi‐objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi‐source heterogeneous data, selection of key sub‐parameters using Principal Component Analysis (PCA), enhanced K‐means clustering analysis, determination of structural component target thresholds, time‐dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double‐layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub‐parameters that have significant impacts on the performance of structural components. Enhanced K‐means clustering analysis is carried out to effectively group and classify the identified key sub‐parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi‐parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan–Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double‐layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.https://doi.org/10.1002/eng2.13017data preprocessingKaplan–Meier methodK‐means cluster analysisperformance evaluationregression predictionstructural nonlinear analysis |
| spellingShingle | Chengzhong Gui Zhi Duan Zuwei Huang Zhiguo Sun Wei Qiao Yu Cheng Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm Engineering Reports data preprocessing Kaplan–Meier method K‐means cluster analysis performance evaluation regression prediction structural nonlinear analysis |
| title | Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm |
| title_full | Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm |
| title_fullStr | Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm |
| title_full_unstemmed | Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm |
| title_short | Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm |
| title_sort | enhanced multi objective optimization model for bridge performance assessment and prediction based on improved pca k means clustering and kaplan meier survival algorithm |
| topic | data preprocessing Kaplan–Meier method K‐means cluster analysis performance evaluation regression prediction structural nonlinear analysis |
| url | https://doi.org/10.1002/eng2.13017 |
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