Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR models
Predicting the deformation of surrounding rock is an important task to ensure the safety of mountain tunnel construction.This study, set against the backdrop of an actual under-construction tunnel, reconstructed the missing surrounding rock monitoring data using a Particle Swarm Optimization-based L...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024016979 |
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| author | Zhenqian Huang Zhen Huang Pengtao An Jun Liu Chen Gao Juncai Huang |
| author_facet | Zhenqian Huang Zhen Huang Pengtao An Jun Liu Chen Gao Juncai Huang |
| author_sort | Zhenqian Huang |
| collection | DOAJ |
| description | Predicting the deformation of surrounding rock is an important task to ensure the safety of mountain tunnel construction.This study, set against the backdrop of an actual under-construction tunnel, reconstructed the missing surrounding rock monitoring data using a Particle Swarm Optimization-based Least Squares Support Vector Regression model (PSO-LSSVR), and subsequently predicted the tunnel surrounding rock deformation using the constructed Gaussian Process Regression model (PSO-GPR).The research results indicate that the average relative error of the PSO-LSSVR reconstruction model is 1.21 %, lower than the 4.82 % of the LSSVR reconstruction model and the 4.69 % of the BP reconstruction model. The relative errors of the PSO-LSSVR prediction model and the BP prediction model are 0.55 % and 2.9 %, respectively, both higher than the PSO-GPR prediction model. The PSO-GPR model considers three covariance functions: the Squared Exponential function (SE), the Rational Quadratic function (RQ), and the Matern function (Matern), with relative errors of 0.16 %, 0.15 %, and 0.23 % in the test results, respectively. However, PSO-GPR-SE has a computational efficiency advantage.Overall, PSO-GPR-SE is a suitable model for predicting the deformation of surrounding rock during mountain tunnel construction. |
| format | Article |
| id | doaj-art-116fe79def534a5193d3411957475bc0 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-116fe79def534a5193d3411957475bc02024-12-19T10:59:31ZengElsevierResults in Engineering2590-12302024-12-0124103445Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR modelsZhenqian Huang0Zhen Huang1Pengtao An2Jun Liu3Chen Gao4Juncai Huang5School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, PR ChinaSchool of Civil Engineering and Architecture, Guangxi University, Nanning 530004, PR China; Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning 530004, PR ChinaSchool of Civil Engineering and Architecture, Guangxi University, Nanning 530004, PR China; Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning 530004, PR China; Corresponding author.School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, PR ChinaChina Construction Seventh Engineering Division.Corp.Ltd, Zhengzhou 450004, PR ChinaSchool of Civil Engineering and Architecture, Guangxi University, Nanning 530004, PR ChinaPredicting the deformation of surrounding rock is an important task to ensure the safety of mountain tunnel construction.This study, set against the backdrop of an actual under-construction tunnel, reconstructed the missing surrounding rock monitoring data using a Particle Swarm Optimization-based Least Squares Support Vector Regression model (PSO-LSSVR), and subsequently predicted the tunnel surrounding rock deformation using the constructed Gaussian Process Regression model (PSO-GPR).The research results indicate that the average relative error of the PSO-LSSVR reconstruction model is 1.21 %, lower than the 4.82 % of the LSSVR reconstruction model and the 4.69 % of the BP reconstruction model. The relative errors of the PSO-LSSVR prediction model and the BP prediction model are 0.55 % and 2.9 %, respectively, both higher than the PSO-GPR prediction model. The PSO-GPR model considers three covariance functions: the Squared Exponential function (SE), the Rational Quadratic function (RQ), and the Matern function (Matern), with relative errors of 0.16 %, 0.15 %, and 0.23 % in the test results, respectively. However, PSO-GPR-SE has a computational efficiency advantage.Overall, PSO-GPR-SE is a suitable model for predicting the deformation of surrounding rock during mountain tunnel construction.http://www.sciencedirect.com/science/article/pii/S2590123024016979Surrounding rock deformationData reconstructionPrediction modelPSO-LSSVRPSO-GPR |
| spellingShingle | Zhenqian Huang Zhen Huang Pengtao An Jun Liu Chen Gao Juncai Huang Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR models Results in Engineering Surrounding rock deformation Data reconstruction Prediction model PSO-LSSVR PSO-GPR |
| title | Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR models |
| title_full | Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR models |
| title_fullStr | Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR models |
| title_full_unstemmed | Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR models |
| title_short | Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR models |
| title_sort | reconstruction and prediction of tunnel surrounding rock deformation data based on pso optimized lssvr and gpr models |
| topic | Surrounding rock deformation Data reconstruction Prediction model PSO-LSSVR PSO-GPR |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024016979 |
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