Application of adaboost to the retaining wall method selection in construction

Yoonseok Shin, Dae Won Kim, Jae Yeob Kim, Kyung In Kang, Moon Young Cho, Hun Hee Cho

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


The appropriate selection of construction methods is a critical factor in the successful completion of any construction project. Artificial intelligence techniques are widely used to assist in the selection of a construction method. This paper proposes the use of the adaptive boosting (AdaBoost) model to select an appropriate retaining wall method suitable for particular construction site conditions, in order to examine the applicability of AdaBoost in construction method selection. To verify its applicability, the proposed model was compared with a support vector machine (SVM) model, which have been attracting attention for their high performance in various classification problems. The AdaBoost model showed a slightly more accurate result than the SVM model in the selection of retaining wall methods, demonstrating that AdaBoost has advantages (e.g., robustness against defective data with missing values) in application to decision support systems. Moreover, the AdaBoost model can be used in future projects to assist engineers in determining the appropriate construction method, such as a retaining wall method, at an early stage of the project.

Original languageEnglish
Pages (from-to)188-192
Number of pages5
JournalJournal of Computing in Civil Engineering
Issue number3
Publication statusPublished - 2009


  • Artificial Intelligence
  • Construction industry
  • Decision support
  • Retaining walls

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications


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