Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms
In the fragility analysis, researchers mostly chose and constructed seismic intensity measures (IMs) according to past experience and personal preference, resulting in large dispersion between the sample of engineering demand parameter (EDP) and the regression function with IM as the independent var...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/8969389 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832557102229356544 |
|---|---|
| author | Hanbo Zhu Changqing Miao |
| author_facet | Hanbo Zhu Changqing Miao |
| author_sort | Hanbo Zhu |
| collection | DOAJ |
| description | In the fragility analysis, researchers mostly chose and constructed seismic intensity measures (IMs) according to past experience and personal preference, resulting in large dispersion between the sample of engineering demand parameter (EDP) and the regression function with IM as the independent variable. This problem needs to be solved urgently. Firstly, the existing 46 types of ground motion intensity measures were taken as a candidate set, and the composite intensity measures (IMs) based on machine learning methods were selected and constructed. Secondly, the modified Park–Ang damage index was taken as EDP, and the symbolic regression method was used to fit the functional relationship between the composite intensity measures (CIMs) and EDP. Finally, the probabilistic seismic demand analysis (PSDA) and seismic fragility analysis were performed by the cloud-stripe method. Taking the pier of a three-span continuous reinforced concrete hollow slab bridge as an example, a nonlinear finite element model was established for vulnerability analysis. And the composite IM was compared with the linear composite IM constructed by Kiani, Lu Dagang, and Liu Tingting. The functions of them were compared. The analysis results indicated that the standard deviation of the composite IM fragility curve proposed in this paper is 60% to 70% smaller than the other composite indicators which verified the efficiency, practicality, proficiency, and sufficiency of the proposed machine learning and symbolic regression fusion algorithms in constructing composite IMs. |
| format | Article |
| id | doaj-art-6e2e705e8f0343bea7e98ce636232859 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-6e2e705e8f0343bea7e98ce6362328592025-02-03T05:43:34ZengWileyShock and Vibration1875-92032021-01-01202110.1155/2021/8969389Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion AlgorithmsHanbo Zhu0Changqing Miao1Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of EducationKey Laboratory of Concrete and Prestressed Concrete Structure of Ministry of EducationIn the fragility analysis, researchers mostly chose and constructed seismic intensity measures (IMs) according to past experience and personal preference, resulting in large dispersion between the sample of engineering demand parameter (EDP) and the regression function with IM as the independent variable. This problem needs to be solved urgently. Firstly, the existing 46 types of ground motion intensity measures were taken as a candidate set, and the composite intensity measures (IMs) based on machine learning methods were selected and constructed. Secondly, the modified Park–Ang damage index was taken as EDP, and the symbolic regression method was used to fit the functional relationship between the composite intensity measures (CIMs) and EDP. Finally, the probabilistic seismic demand analysis (PSDA) and seismic fragility analysis were performed by the cloud-stripe method. Taking the pier of a three-span continuous reinforced concrete hollow slab bridge as an example, a nonlinear finite element model was established for vulnerability analysis. And the composite IM was compared with the linear composite IM constructed by Kiani, Lu Dagang, and Liu Tingting. The functions of them were compared. The analysis results indicated that the standard deviation of the composite IM fragility curve proposed in this paper is 60% to 70% smaller than the other composite indicators which verified the efficiency, practicality, proficiency, and sufficiency of the proposed machine learning and symbolic regression fusion algorithms in constructing composite IMs.http://dx.doi.org/10.1155/2021/8969389 |
| spellingShingle | Hanbo Zhu Changqing Miao Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms Shock and Vibration |
| title | Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms |
| title_full | Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms |
| title_fullStr | Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms |
| title_full_unstemmed | Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms |
| title_short | Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms |
| title_sort | seismic fragility analysis of the reinforced concrete continuous bridge piers based on machine learning and symbolic regression fusion algorithms |
| url | http://dx.doi.org/10.1155/2021/8969389 |
| work_keys_str_mv | AT hanbozhu seismicfragilityanalysisofthereinforcedconcretecontinuousbridgepiersbasedonmachinelearningandsymbolicregressionfusionalgorithms AT changqingmiao seismicfragilityanalysisofthereinforcedconcretecontinuousbridgepiersbasedonmachinelearningandsymbolicregressionfusionalgorithms |