Influence of Settlement on Base Resistance of Long Piles in Soft Soil-Field and Machine Learning Assessments
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nguyen, Thanh T. | - |
dc.contributor.author | Le, Viet D. | - |
dc.contributor.author | Huynh, Thien Q. | - |
dc.contributor.author | Nguyen, Nhu H. T. | - |
dc.date.accessioned | 2024-07-19T05:30:22Z | - |
dc.date.available | 2024-07-19T05:30:22Z | - |
dc.date.issued | 2024-06 | - |
dc.identifier.issn | 2673-7094 | - |
dc.identifier.issn | 2673-7094 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28808 | - |
dc.description.abstract | Understanding the role that settlement can have on the base resistance of piles is a crucial matter in the design and safety control of deep foundations under various buildings and infrastructure, especially for long to super-long piles (60-90 m length) in soft soil. This paper presents a novel assessment of this issue by applying explainable machine learning (ML) techniques to a robust database (1131 datapoints) of fully instrumented pile tests across 37 real-life projects in the Mekong Delta. The analysis of data based on conventional methods shows distinct responses of long piles to rising settlement, as compared to short piles. The base resistance can rapidly develop at a small settlement threshold (0.015-0.03% of pile's length) and contribute up to 50-55% of the total bearing capacity in short piles, but it slowly rises over a wide range of settlement to only 20-25% in long piles due to considerable loss of settlement impact over the depth. Furthermore, by leveraging the advantages of ML methods, the results significantly enhance our understanding of the settlement-base resistance relationship through explainable computations. The ML-based prediction method is compared with popular practice codes for pile foundations, further attesting to the high accuracy and reliability of the newly established model. | - |
dc.format.extent | 23 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Influence of Settlement on Base Resistance of Long Piles in Soft Soil-Field and Machine Learning Assessments | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/geotechnics4020025 | - |
dc.identifier.wosid | 001256550200001 | - |
dc.identifier.bibliographicCitation | GEOTECHNICS, v.4, no.2, pp 447 - 469 | - |
dc.citation.title | GEOTECHNICS | - |
dc.citation.volume | 4 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 447 | - |
dc.citation.endPage | 469 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | esci | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Geology | - |
dc.relation.journalWebOfScienceCategory | Engineering, Geological | - |
dc.relation.journalWebOfScienceCategory | Geosciences, Multidisciplinary | - |
dc.subject.keywordPlus | LOAD | - |
dc.subject.keywordPlus | BEHAVIOR | - |
dc.subject.keywordPlus | CLAY | - |
dc.subject.keywordAuthor | base resistance | - |
dc.subject.keywordAuthor | pile foundation | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | pile load tests | - |
dc.subject.keywordAuthor | pile settlement | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.