Sequential backward feature selection for optimizing permanent strain model of unbound aggregates

Samuel Olamide Aregbesola, Jongmuk Won, Seungjun Kim, Yong Hoon Byun

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This study proposes a novel framework for identifying the optimal feature set required to predict the permanent strain of unbound aggregates. An experimental database consisting of 16 input features is preprocessed and the performance of 10 machine learning models is evaluated. The best-performing model is then paired with a sequential backward selection algorithm to determine the optimal feature set for predicting the permanent strain. Finally, the selected features are used to predict the permanent strain, and the performance is compared with those obtained from the principal components analysis. Six features are selected as the optimal feature set. Furthermore, the selected features accurately predict permanent strain with a root mean square error value of 0.014, which is smaller than those obtained from principal components analysis. Thus, the feature selection approach for machine learning models effectively predicts the permanent strain of unbound aggregates using a limited set of input features.

Original languageEnglish
Article numbere02554
JournalCase Studies in Construction Materials
Volume19
DOIs
StatePublished - Dec 2023

Keywords

  • Aggregate
  • Feature selection
  • Machine learning
  • Optimization
  • Permanent strain

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