Predicting Soft Soil Settlement with a FAGSO-BP Neural Network Model
Aiming at the problem that it is difficult to consider the prediction of foundation settlement in the case of multi-parameter coupling effect by theoretical formulas and numerical analysis, the fireworks algorithm with gravitational search operator (FAGSO) is introduced into the BP neural network mo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/8/1343 |
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| _version_ | 1849712159087394816 |
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| author | Binhui Ma Yarui Xiao Tian Lan Chao Zhang Zengliang Wang Zeshi Xiang Yuqi Li Zijing Zhao |
| author_facet | Binhui Ma Yarui Xiao Tian Lan Chao Zhang Zengliang Wang Zeshi Xiang Yuqi Li Zijing Zhao |
| author_sort | Binhui Ma |
| collection | DOAJ |
| description | Aiming at the problem that it is difficult to consider the prediction of foundation settlement in the case of multi-parameter coupling effect by theoretical formulas and numerical analysis, the fireworks algorithm with gravitational search operator (FAGSO) is introduced into the BP neural network model, and the FAGSO algorithm aims to enhance the neural network’s weight and threshold adjustment process; so, a new soft ground settlement prediction model was developed which uses a fireworks algorithm integrated with a gravitational search operator to optimize a BP neural network (referred to as FAGSO-BP). The FAGSO-BP neural network forecasting model is used to predict the soft foundation settlement of Hunan Wuyi Expressway Project. In the soft foundation settlement prediction analysis of Hunan Wuyi Expressway Project, the average relative error of the FAGSO-BP neural network test set was 6.06%, with an RMSE of 1.6, an MAE of 1.2, a MAPE of 0.12% and an MSE of 2.56, which compared to the traditional BP, GA-BP and FWA-BP neural models, had smaller error and higher model stability. |
| format | Article |
| id | doaj-art-e074a88d102042b6b5a2cf8f542ca239 |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-e074a88d102042b6b5a2cf8f542ca2392025-08-20T03:14:21ZengMDPI AGBuildings2075-53092025-04-01158134310.3390/buildings15081343Predicting Soft Soil Settlement with a FAGSO-BP Neural Network ModelBinhui Ma0Yarui Xiao1Tian Lan2Chao Zhang3Zengliang Wang4Zeshi Xiang5Yuqi Li6Zijing Zhao7School of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaSchool of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaSchool of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaSchool of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaSchool of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaSchool of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaSchool of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaSchool of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaAiming at the problem that it is difficult to consider the prediction of foundation settlement in the case of multi-parameter coupling effect by theoretical formulas and numerical analysis, the fireworks algorithm with gravitational search operator (FAGSO) is introduced into the BP neural network model, and the FAGSO algorithm aims to enhance the neural network’s weight and threshold adjustment process; so, a new soft ground settlement prediction model was developed which uses a fireworks algorithm integrated with a gravitational search operator to optimize a BP neural network (referred to as FAGSO-BP). The FAGSO-BP neural network forecasting model is used to predict the soft foundation settlement of Hunan Wuyi Expressway Project. In the soft foundation settlement prediction analysis of Hunan Wuyi Expressway Project, the average relative error of the FAGSO-BP neural network test set was 6.06%, with an RMSE of 1.6, an MAE of 1.2, a MAPE of 0.12% and an MSE of 2.56, which compared to the traditional BP, GA-BP and FWA-BP neural models, had smaller error and higher model stability.https://www.mdpi.com/2075-5309/15/8/1343settlement predictionBP neural networksoft soilfireworks algorithmFAGSO algorithm |
| spellingShingle | Binhui Ma Yarui Xiao Tian Lan Chao Zhang Zengliang Wang Zeshi Xiang Yuqi Li Zijing Zhao Predicting Soft Soil Settlement with a FAGSO-BP Neural Network Model Buildings settlement prediction BP neural network soft soil fireworks algorithm FAGSO algorithm |
| title | Predicting Soft Soil Settlement with a FAGSO-BP Neural Network Model |
| title_full | Predicting Soft Soil Settlement with a FAGSO-BP Neural Network Model |
| title_fullStr | Predicting Soft Soil Settlement with a FAGSO-BP Neural Network Model |
| title_full_unstemmed | Predicting Soft Soil Settlement with a FAGSO-BP Neural Network Model |
| title_short | Predicting Soft Soil Settlement with a FAGSO-BP Neural Network Model |
| title_sort | predicting soft soil settlement with a fagso bp neural network model |
| topic | settlement prediction BP neural network soft soil fireworks algorithm FAGSO algorithm |
| url | https://www.mdpi.com/2075-5309/15/8/1343 |
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