A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
Ground-motion simulations provide input time history data required for designing and assessing structures; however, the simulations conducted by the currently available tools only match the design spectrum without verifying if the statistical characteristics of the spectrum and duration are satisfie...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/14/8232 |
| _version_ | 1797590470387302400 |
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| author | Jinjun Hu Yitian Ding Shibin Lin Hui Zhang Chaoyue Jin |
| author_facet | Jinjun Hu Yitian Ding Shibin Lin Hui Zhang Chaoyue Jin |
| author_sort | Jinjun Hu |
| collection | DOAJ |
| description | Ground-motion simulations provide input time history data required for designing and assessing structures; however, the simulations conducted by the currently available tools only match the design spectrum without verifying if the statistical characteristics of the spectrum and duration are satisfied. A ground-motion simulation software was developed to resolve these issues. The developed software employs machine learning methods to match the amplitude, spectrum, and duration features of the target region. Principal component analysis is employed to extract features from the actual ground-motion database to detect characteristic ground motions and predict the target acceleration amplitude, response spectrum, and duration, based on the response spectrum and duration prediction equations. The results show that the simulated ground motion can match the amplitude, spectrum, and duration characteristics well. Therefore, the simulated ground motion can provide more reasonable input for the structure. Moreover, the developed software provides visualization functions that enable the user to determine the target area and obtain the amplitude field intuitively. |
| first_indexed | 2024-03-11T01:20:57Z |
| format | Article |
| id | doaj.art-bbfd01ad5f844db38c5dcd202cbcb337 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:20:57Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bbfd01ad5f844db38c5dcd202cbcb3372023-11-18T18:10:09ZengMDPI AGApplied Sciences2076-34172023-07-011314823210.3390/app13148232A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground MotionJinjun Hu0Yitian Ding1Shibin Lin2Hui Zhang3Chaoyue Jin4Key Laboratory of Earthquake Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaKey Laboratory of Earthquake Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaHubei (Wuhan) Institute of Explosion Science and Blasting Technology, Jianghan University, Economic and Technological Development Zone, Wuhan 430056, ChinaKey Laboratory of Earthquake Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaKey Laboratory of Earthquake Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaGround-motion simulations provide input time history data required for designing and assessing structures; however, the simulations conducted by the currently available tools only match the design spectrum without verifying if the statistical characteristics of the spectrum and duration are satisfied. A ground-motion simulation software was developed to resolve these issues. The developed software employs machine learning methods to match the amplitude, spectrum, and duration features of the target region. Principal component analysis is employed to extract features from the actual ground-motion database to detect characteristic ground motions and predict the target acceleration amplitude, response spectrum, and duration, based on the response spectrum and duration prediction equations. The results show that the simulated ground motion can match the amplitude, spectrum, and duration characteristics well. Therefore, the simulated ground motion can provide more reasonable input for the structure. Moreover, the developed software provides visualization functions that enable the user to determine the target area and obtain the amplitude field intuitively.https://www.mdpi.com/2076-3417/13/14/8232reginal characteristic ground motionground motionground-motion field simulationprincipal component analysisgenetic algorithmsMATLAB-based simulator |
| spellingShingle | Jinjun Hu Yitian Ding Shibin Lin Hui Zhang Chaoyue Jin A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion Applied Sciences reginal characteristic ground motion ground motion ground-motion field simulation principal component analysis genetic algorithms MATLAB-based simulator |
| title | A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion |
| title_full | A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion |
| title_fullStr | A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion |
| title_full_unstemmed | A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion |
| title_short | A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion |
| title_sort | machine learning based software for the simulation of regional characteristic ground motion |
| topic | reginal characteristic ground motion ground motion ground-motion field simulation principal component analysis genetic algorithms MATLAB-based simulator |
| url | https://www.mdpi.com/2076-3417/13/14/8232 |
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