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

Xiao Li, Xin Liu, Clyde Zhengdao Li, Zhumin Hu, Geoffrey Qiping Shen, Zhenyu Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Foundation 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.

Original languageEnglish
Pages (from-to)715-724
Number of pages10
JournalStructural Health Monitoring
Volume18
Issue number3
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • displacement prediction
  • Foundation pit monitoring
  • least squares support vector machines
  • multi-point measurement
  • support vector machines

ASJC Scopus subject areas

  • Biophysics
  • Mechanical Engineering

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