Shan Huang, Jinsong Huang, Richard Kelly, Merrick Jonse, Patrick Wong, Stanley Yuen, A.H.M. Kamruzzaman, Shui-Hua Jiang
{"title":"Efficient Class C prediction of PVD-improved soft clay settlements","authors":"Shan Huang, Jinsong Huang, Richard Kelly, Merrick Jonse, Patrick Wong, Stanley Yuen, A.H.M. Kamruzzaman, Shui-Hua Jiang","doi":"10.1016/j.enggeo.2024.107903","DOIUrl":null,"url":null,"abstract":"Soft clays exhibit significant challenges in geotechnical engineering due to their low permeability, high compressibility, and susceptibility to settlement under applied loads. These geological factors pose unique difficulties in predicting long-term settlement accurately and efficiently, particularly through Class C prediction methods that involve iterative processes with complex numerical models. To address these challenges, this study presents an efficient approach for Class C prediction of long-term settlement in soft clays. This approach integrates Bayesian updating with structural reliability methods (BUS) and the general simplified Hypothesis B method which is a semi-analytical method based on one-dimensional elastic visco-plastic (1D EVP) model. Unlike previous research that used Response Surface Model (RSM) with polynomial function for consolidation evaluation, the proposed approach enhances both accuracy and performance consistency under varying conditions. Additionally, by leveraging analytical solutions instead of iterative small-time steps required by Finite Element Method (FEM) or Finite Difference Method (FDM), the computational efficiency is also enhanced. The effectiveness of the proposed approach is demonstrated through its application to an embankment improved with prefabricated vertical drain (PVD) in Ballina, New South Wales, Australia. Comparative analyses demonstrate that the predicted settlements from this study, using only the monitoring settlement data collected prior to the 76th day of the project, align closely with the results from established RSM and FEM-based Bayesian back analysis approaches. The obtained results also indicate that the predicted settlements, based on 76 days of monitoring data, closely match field measurements at various depths, whether relying solely on settlement data or integrating additional pore water pressure data. For the Ballina embankment, over 40,000 consolidation analyses required for a single BUS simulation can be completed within 10 h using the general simplified Hypothesis B method, compared to months it might take with FEM or FDM approaches. This makes the proposed approach a practical tool for geotechnical engineers, enabling reliable settlement predictions early in the project timeline while maintaining low computational costs.","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"88 1","pages":""},"PeriodicalIF":6.9000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Geology","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1016/j.enggeo.2024.107903","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Soft clays exhibit significant challenges in geotechnical engineering due to their low permeability, high compressibility, and susceptibility to settlement under applied loads. These geological factors pose unique difficulties in predicting long-term settlement accurately and efficiently, particularly through Class C prediction methods that involve iterative processes with complex numerical models. To address these challenges, this study presents an efficient approach for Class C prediction of long-term settlement in soft clays. This approach integrates Bayesian updating with structural reliability methods (BUS) and the general simplified Hypothesis B method which is a semi-analytical method based on one-dimensional elastic visco-plastic (1D EVP) model. Unlike previous research that used Response Surface Model (RSM) with polynomial function for consolidation evaluation, the proposed approach enhances both accuracy and performance consistency under varying conditions. Additionally, by leveraging analytical solutions instead of iterative small-time steps required by Finite Element Method (FEM) or Finite Difference Method (FDM), the computational efficiency is also enhanced. The effectiveness of the proposed approach is demonstrated through its application to an embankment improved with prefabricated vertical drain (PVD) in Ballina, New South Wales, Australia. Comparative analyses demonstrate that the predicted settlements from this study, using only the monitoring settlement data collected prior to the 76th day of the project, align closely with the results from established RSM and FEM-based Bayesian back analysis approaches. The obtained results also indicate that the predicted settlements, based on 76 days of monitoring data, closely match field measurements at various depths, whether relying solely on settlement data or integrating additional pore water pressure data. For the Ballina embankment, over 40,000 consolidation analyses required for a single BUS simulation can be completed within 10 h using the general simplified Hypothesis B method, compared to months it might take with FEM or FDM approaches. This makes the proposed approach a practical tool for geotechnical engineers, enabling reliable settlement predictions early in the project timeline while maintaining low computational costs.
期刊介绍:
Engineering Geology, an international interdisciplinary journal, serves as a bridge between earth sciences and engineering, focusing on geological and geotechnical engineering. It welcomes studies with relevance to engineering, environmental concerns, and safety, catering to engineering geologists with backgrounds in geology or civil/mining engineering. Topics include applied geomorphology, structural geology, geophysics, geochemistry, environmental geology, hydrogeology, land use planning, natural hazards, remote sensing, soil and rock mechanics, and applied geotechnical engineering. The journal provides a platform for research at the intersection of geology and engineering disciplines.