Predicting load on ground anchor using a metaheuristic optimized least squares support vector regression model: A Taiwan case study

Min Yuan Cheng, Minh Tu Cao*, Po Kun Tsai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Failure of ground anchor is a major cause of landslides and severe natural hazards, especially in the highly developed mountainous areas such as New Taipei City. Accurately estimating load on ground anchors is thus essential for evaluating the stability status of slope to prevent landslide from happening. This study first employed correlation analyses to identify possible influential factors of load on ground anchors. Second, various artificial intelligence models were used to map the relationship of the found influencing factors with the current load on ground anchors. The results indicated that the symbiotic organisms search-optimized least squares support vector regression (SOS-LSSVR) model had the optimal accuracy by earning the smallest value of mean absolute percentage error (9.10%) and the most outstanding value of correlation coefficient (R = 0.988). The study applied the established inference model for the real case of estimating load on un-monitoring ground anchors. The analyzed results strongly advised administrators to conduct site surveying and patrolling more frequently to take early proper actions. In summary, the obtained results have demonstrated SOS-LSSVR as an effective alternative for the conventional subjective evaluation methods, which is able to rapidly provide accurate values of load on un-monitoring ground anchors.

Original languageEnglish
Pages (from-to)268-282
Number of pages15
JournalJournal of Computational Design and Engineering
Volume8
Issue number1
DOIs
StatePublished - 1 Feb 2021

Keywords

  • artificial intelligence
  • least squares support vector regression (LSSVR)
  • load cell
  • load on ground anchor
  • slope stability
  • symbiotic organisms search (SOS)

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