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Article: Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement

TitleFoundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement
Authors
Keywordsdisplacement prediction
Foundation pit monitoring
least squares support vector machines
multi-point measurement
support vector machines
Issue Date2019
Citation
Structural Health Monitoring, 2019, v. 18, n. 3, p. 715-724 How to Cite?
AbstractFoundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate prediction, machine learning methods are extensively applied to fulfill this purpose. However, these approaches, such as support vector machines, have limitations in terms of data processing efficiency and prediction accuracy. As an emerging approach derived from support vector machines, least squares support vector machine improve the data processing efficiency through better use of equality constraints in the least squares loss functions. However, the accuracy of this approach highly relies on the large volume of influencing factors from the measurement of adjacent critical points, which is not normally available during the construction process. To address this issue, this study proposes an improved least squares support vector machine algorithm based on multi-point measuring techniques, namely, multi-point least squares support vector machine. To evaluate the effectiveness of the proposed multi-point least squares support vector machine approach, a real case study project was selected, and the results illustrated that the multi-point least squares support vector machine approach on average outperformed single-point least squares support vector machine in terms of prediction accuracy during the foundation pit monitoring and prediction process.
Persistent Identifierhttp://hdl.handle.net/10722/326160
ISSN
2023 Impact Factor: 5.7
2023 SCImago Journal Rankings: 1.874
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Xiao-
dc.contributor.authorLiu, Xin-
dc.contributor.authorLi, Clyde Zhengdao-
dc.contributor.authorHu, Zhumin-
dc.contributor.authorShen, Geoffrey Qiping-
dc.contributor.authorHuang, Zhenyu-
dc.date.accessioned2023-03-09T09:58:27Z-
dc.date.available2023-03-09T09:58:27Z-
dc.date.issued2019-
dc.identifier.citationStructural Health Monitoring, 2019, v. 18, n. 3, p. 715-724-
dc.identifier.issn1475-9217-
dc.identifier.urihttp://hdl.handle.net/10722/326160-
dc.description.abstractFoundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate prediction, machine learning methods are extensively applied to fulfill this purpose. However, these approaches, such as support vector machines, have limitations in terms of data processing efficiency and prediction accuracy. As an emerging approach derived from support vector machines, least squares support vector machine improve the data processing efficiency through better use of equality constraints in the least squares loss functions. However, the accuracy of this approach highly relies on the large volume of influencing factors from the measurement of adjacent critical points, which is not normally available during the construction process. To address this issue, this study proposes an improved least squares support vector machine algorithm based on multi-point measuring techniques, namely, multi-point least squares support vector machine. To evaluate the effectiveness of the proposed multi-point least squares support vector machine approach, a real case study project was selected, and the results illustrated that the multi-point least squares support vector machine approach on average outperformed single-point least squares support vector machine in terms of prediction accuracy during the foundation pit monitoring and prediction process.-
dc.languageeng-
dc.relation.ispartofStructural Health Monitoring-
dc.subjectdisplacement prediction-
dc.subjectFoundation pit monitoring-
dc.subjectleast squares support vector machines-
dc.subjectmulti-point measurement-
dc.subjectsupport vector machines-
dc.titleFoundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1177/1475921718767935-
dc.identifier.scopuseid_2-s2.0-85046755485-
dc.identifier.volume18-
dc.identifier.issue3-
dc.identifier.spage715-
dc.identifier.epage724-
dc.identifier.eissn1741-3168-
dc.identifier.isiWOS:000465318500004-

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