Wenyu Yang , Junjie Zheng , Rongjun Zhang , Sijie Liu , Wengang Zhang
{"title":"软土中盾构机姿态调整导致超挖间隙的动态预测","authors":"Wenyu Yang , Junjie Zheng , Rongjun Zhang , Sijie Liu , Wengang Zhang","doi":"10.1016/j.undsp.2023.09.004","DOIUrl":null,"url":null,"abstract":"<div><p>The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling. The gap parameter is regarded as an effective tool to estimate the ground loss of tunnelling in soft soil. More specifically, <em>ω</em>, which is a gap parameter component defined as the over (or insufficient) excavation due to the change in the posture of the shield machine, may contribute more to the uncertainty of the ground loss. However, the existing uncertainty characterization methods for <em>ω</em> have several limitations and cannot explain the uncertain correlations between the relevant parameters. Along these lines, to better characterize the uncertainty of <em>ω</em>, the multivariate probability distribution was developed in this work and a dynamic prediction was proposed for it. To attain this goal, 1 523 rings of the field data coming from the shield tunnel between Longqing Road and Baiyun Road in Kunming Metro Line 5 were utilized and 44 parameters including the construction, stratigraphic, and posture parameters were collected to form the database. According to the variance filter method, the mutual information method, and the value of the correlation coefficients, the original 44 parameters were reduced to 10 main parameters, which were unit weight, the stoke of the jacks (A, B, C, and D groups), the pressure of the pushing jacks (A, C groups), the chamber pressure, the rotation speed, and the total force. The multivariate probability distribution was constructed based on the Johnson system of distributions. Moreover, the distribution was satisfactorily verified in explaining the pairwise correlation between <em>ω</em> and other parameters through 2 million simulation cases. At last, the distribution was used as a prior distribution to update the marginal distribution of <em>ω</em> with any group of the relevant parameters known. The performance of the dynamic prediction was further validated by the field data of 3 shield tunnel cases.</p></div>","PeriodicalId":48505,"journal":{"name":"Underground Space","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2467967423001496/pdfft?md5=807fef71679ca938b131c76ef64f9ebc&pid=1-s2.0-S2467967423001496-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Dynamic prediction of over-excavation gap due to posture adjustment of shield machine in soft soil\",\"authors\":\"Wenyu Yang , Junjie Zheng , Rongjun Zhang , Sijie Liu , Wengang Zhang\",\"doi\":\"10.1016/j.undsp.2023.09.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling. The gap parameter is regarded as an effective tool to estimate the ground loss of tunnelling in soft soil. More specifically, <em>ω</em>, which is a gap parameter component defined as the over (or insufficient) excavation due to the change in the posture of the shield machine, may contribute more to the uncertainty of the ground loss. However, the existing uncertainty characterization methods for <em>ω</em> have several limitations and cannot explain the uncertain correlations between the relevant parameters. Along these lines, to better characterize the uncertainty of <em>ω</em>, the multivariate probability distribution was developed in this work and a dynamic prediction was proposed for it. To attain this goal, 1 523 rings of the field data coming from the shield tunnel between Longqing Road and Baiyun Road in Kunming Metro Line 5 were utilized and 44 parameters including the construction, stratigraphic, and posture parameters were collected to form the database. According to the variance filter method, the mutual information method, and the value of the correlation coefficients, the original 44 parameters were reduced to 10 main parameters, which were unit weight, the stoke of the jacks (A, B, C, and D groups), the pressure of the pushing jacks (A, C groups), the chamber pressure, the rotation speed, and the total force. The multivariate probability distribution was constructed based on the Johnson system of distributions. Moreover, the distribution was satisfactorily verified in explaining the pairwise correlation between <em>ω</em> and other parameters through 2 million simulation cases. At last, the distribution was used as a prior distribution to update the marginal distribution of <em>ω</em> with any group of the relevant parameters known. The performance of the dynamic prediction was further validated by the field data of 3 shield tunnel cases.</p></div>\",\"PeriodicalId\":48505,\"journal\":{\"name\":\"Underground Space\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2023-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2467967423001496/pdfft?md5=807fef71679ca938b131c76ef64f9ebc&pid=1-s2.0-S2467967423001496-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Underground Space\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2467967423001496\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Underground Space","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2467967423001496","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}
Dynamic prediction of over-excavation gap due to posture adjustment of shield machine in soft soil
The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling. The gap parameter is regarded as an effective tool to estimate the ground loss of tunnelling in soft soil. More specifically, ω, which is a gap parameter component defined as the over (or insufficient) excavation due to the change in the posture of the shield machine, may contribute more to the uncertainty of the ground loss. However, the existing uncertainty characterization methods for ω have several limitations and cannot explain the uncertain correlations between the relevant parameters. Along these lines, to better characterize the uncertainty of ω, the multivariate probability distribution was developed in this work and a dynamic prediction was proposed for it. To attain this goal, 1 523 rings of the field data coming from the shield tunnel between Longqing Road and Baiyun Road in Kunming Metro Line 5 were utilized and 44 parameters including the construction, stratigraphic, and posture parameters were collected to form the database. According to the variance filter method, the mutual information method, and the value of the correlation coefficients, the original 44 parameters were reduced to 10 main parameters, which were unit weight, the stoke of the jacks (A, B, C, and D groups), the pressure of the pushing jacks (A, C groups), the chamber pressure, the rotation speed, and the total force. The multivariate probability distribution was constructed based on the Johnson system of distributions. Moreover, the distribution was satisfactorily verified in explaining the pairwise correlation between ω and other parameters through 2 million simulation cases. At last, the distribution was used as a prior distribution to update the marginal distribution of ω with any group of the relevant parameters known. The performance of the dynamic prediction was further validated by the field data of 3 shield tunnel cases.
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.