Boyu Jiang, Haibin Wei, Dongsheng Wei, Zipeng Ma, Fuyu Wang
{"title":"Interlayer soil settlement prediction in the construction of under-crossing existing structures based on multi-parameter time series model","authors":"Boyu Jiang, Haibin Wei, Dongsheng Wei, Zipeng Ma, Fuyu Wang","doi":"10.1016/j.undsp.2025.04.009","DOIUrl":null,"url":null,"abstract":"<div><div>Predicting surface settlement can identify potential risks associated in shield construction. However, in the construction of under-crossing existing structures, the surface settlement is minimal due to the high stiffness of the existing structure, making it unsuitable as a basis for risk assessment. Therefore, interlayer soil settlement was used as an evaluation index in this paper, which was predicted by the developed multi-parameter time series (MPTS) model. This model establishes new dataset, including time, effective stress ratio (ESR), mechanical fluctuation coefficient (MFC), and interlayer soil settlement, where ESR and MFC take into account the changing geological conditions. This study proposes a novel MPTS model, integrating grid search (GS), nonlinear particle swarm optimization (NPSO), and support vector regression (SVR) algorithms to predict interlayer soil settlement during under-crossing construction. It utilizes GS and NPSO to obtain the optimal hyperparameters for SVR. Sensitivity analysis based on MPTS model was used to identify important parameters and propose specific improvement measures. A real under-crossing tunnel project was adopted to verify the effectiveness of the MPTS. The results show that the new input parameters proposed in this paper reduce mean absolute error (MAE) by 20.3% and mean square error (MSE) by 46.7% of prediction results. Compared with the other three algorithms, GS-NPSO-SVR has better prediction performance. Through Sobol sensitivity analysis, previous settlement, ESR and MFC in fully weathered mudstone and moderately weathered mudstone are identified as the primary parameters affecting the interlayer soil settlement. The improvement measures based on analysis results reduce the accumulated settlement by 79.97%. The developed MPTS model can accurately predict the interlayer soil settlement and provide guidance for water stopping or reinforcement construction.</div></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":"24 ","pages":"Pages 335-351"},"PeriodicalIF":8.3000,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Underground Space","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2467967425000741","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
Abstract
Predicting surface settlement can identify potential risks associated in shield construction. However, in the construction of under-crossing existing structures, the surface settlement is minimal due to the high stiffness of the existing structure, making it unsuitable as a basis for risk assessment. Therefore, interlayer soil settlement was used as an evaluation index in this paper, which was predicted by the developed multi-parameter time series (MPTS) model. This model establishes new dataset, including time, effective stress ratio (ESR), mechanical fluctuation coefficient (MFC), and interlayer soil settlement, where ESR and MFC take into account the changing geological conditions. This study proposes a novel MPTS model, integrating grid search (GS), nonlinear particle swarm optimization (NPSO), and support vector regression (SVR) algorithms to predict interlayer soil settlement during under-crossing construction. It utilizes GS and NPSO to obtain the optimal hyperparameters for SVR. Sensitivity analysis based on MPTS model was used to identify important parameters and propose specific improvement measures. A real under-crossing tunnel project was adopted to verify the effectiveness of the MPTS. The results show that the new input parameters proposed in this paper reduce mean absolute error (MAE) by 20.3% and mean square error (MSE) by 46.7% of prediction results. Compared with the other three algorithms, GS-NPSO-SVR has better prediction performance. Through Sobol sensitivity analysis, previous settlement, ESR and MFC in fully weathered mudstone and moderately weathered mudstone are identified as the primary parameters affecting the interlayer soil settlement. The improvement measures based on analysis results reduce the accumulated settlement by 79.97%. The developed MPTS model can accurately predict the interlayer soil settlement and provide guidance for water stopping or reinforcement construction.
期刊介绍:
Underground Space is an open access international journal without article processing charges (APC) committed to serving as a scientific forum for researchers and practitioners in the field of underground engineering. The journal welcomes manuscripts that deal with original theories, methods, technologies, and important applications throughout the life-cycle of underground projects, including planning, design, operation and maintenance, disaster prevention, and demolition. The journal is particularly interested in manuscripts related to the latest development of smart underground engineering from the perspectives of resilience, resources saving, environmental friendliness, humanity, and artificial intelligence. The manuscripts are expected to have significant innovation and potential impact in the field of underground engineering, and should have clear association with or application in underground projects.