Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO
Subway station projects are characterized by complex construction technology, complex site conditions, and being easily influenced by the surrounding environment; thus, construction safety accidents occur frequently. In order to improve the computing performance of the early risk warning system in s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/11/5712 |
| _version_ | 1797494077714857984 |
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| author | Leian Zhang Junwu Wang Han Wu Mengwei Wu Jingyi Guo Shengmin Wang |
| author_facet | Leian Zhang Junwu Wang Han Wu Mengwei Wu Jingyi Guo Shengmin Wang |
| author_sort | Leian Zhang |
| collection | DOAJ |
| description | Subway station projects are characterized by complex construction technology, complex site conditions, and being easily influenced by the surrounding environment; thus, construction safety accidents occur frequently. In order to improve the computing performance of the early risk warning system in subway station construction, a novel model based on least-squares support vector machines (LSSVM) optimized by quantum-behaved particle swarm optimization (QPSO) was proposed. First, early warning factors from five aspects (man, machine, management, material, and the environment) were selected based on accident causation theory and literature research. The data acquisition method of each risk factor was provided in detail. Then, the LSSVM with strong small sample analysis and nonlinear analysis abilities was chosen to give the early warning. To further ameliorate the early warning accuracy of the LSSVM, QPSO with a strong global retrieval ability was used to find the optimal calculation parameters of the LSSVM. Seventeen subway stations of Chengdu Metro Line 11 in China were picked as the empirical objects. The results demonstrated that the best regularization parameter was 1.742, and the best width parameter was 14.167. The number of misjudged samples of the proposed model was 1, and the early warning error rate was only 4.41%, which met the needs of engineering practice. Compared with the classic and latest methods, the proposed model was found to have a faster prediction speed and higher prediction accuracy. |
| first_indexed | 2024-03-10T01:29:08Z |
| format | Article |
| id | doaj.art-d8c3c6c98ad84a898a66d2fba3274e00 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:29:08Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d8c3c6c98ad84a898a66d2fba3274e002023-11-23T13:46:36ZengMDPI AGApplied Sciences2076-34172022-06-011211571210.3390/app12115712Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSOLeian Zhang0Junwu Wang1Han Wu2Mengwei Wu3Jingyi Guo4Shengmin Wang5School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430062, ChinaSchool of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430062, ChinaSchool of Civil Engineering and Architecture, Nanchang University, Nanchang 330047, ChinaSchool of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan 430024, ChinaSchool of Civil Engineering and Architecture, Hubei University of Arts and Science, Xiangyang 441021, ChinaSchool of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430062, ChinaSubway station projects are characterized by complex construction technology, complex site conditions, and being easily influenced by the surrounding environment; thus, construction safety accidents occur frequently. In order to improve the computing performance of the early risk warning system in subway station construction, a novel model based on least-squares support vector machines (LSSVM) optimized by quantum-behaved particle swarm optimization (QPSO) was proposed. First, early warning factors from five aspects (man, machine, management, material, and the environment) were selected based on accident causation theory and literature research. The data acquisition method of each risk factor was provided in detail. Then, the LSSVM with strong small sample analysis and nonlinear analysis abilities was chosen to give the early warning. To further ameliorate the early warning accuracy of the LSSVM, QPSO with a strong global retrieval ability was used to find the optimal calculation parameters of the LSSVM. Seventeen subway stations of Chengdu Metro Line 11 in China were picked as the empirical objects. The results demonstrated that the best regularization parameter was 1.742, and the best width parameter was 14.167. The number of misjudged samples of the proposed model was 1, and the early warning error rate was only 4.41%, which met the needs of engineering practice. Compared with the classic and latest methods, the proposed model was found to have a faster prediction speed and higher prediction accuracy.https://www.mdpi.com/2076-3417/12/11/5712early warningconstruction safety risksubway stationLSSVMQPSOaccident causation theory |
| spellingShingle | Leian Zhang Junwu Wang Han Wu Mengwei Wu Jingyi Guo Shengmin Wang Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO Applied Sciences early warning construction safety risk subway station LSSVM QPSO accident causation theory |
| title | Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO |
| title_full | Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO |
| title_fullStr | Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO |
| title_full_unstemmed | Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO |
| title_short | Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO |
| title_sort | early warning of the construction safety risk of a subway station based on the lssvm optimized by qpso |
| topic | early warning construction safety risk subway station LSSVM QPSO accident causation theory |
| url | https://www.mdpi.com/2076-3417/12/11/5712 |
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