Evolutionary polynomial regression based modelling of clay compressibility using an enhanced hybrid real-coded genetic algorithm

Zhenyu Yin, Y.-F. Jin, H.-W. Huang, S.-L. Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)

Abstract

© 2016A new approach for evaluating the compressibility of remoulded clays using the evolutionary polynomial regression (EPR) and optimization methods is proposed. An efficient hybrid real-coded genetic algorithm (RCGA) with a new hybrid strategy combined with a self-adaptive mutation is first developed. Then, the enhanced RCGA is applied to construct the EPR procedure for compression index. To highlight the performance of the RCGA in the proposed procedure, three other excellent optimization algorithms are selected and compared. All comparisons between predictions and measurements demonstrate that the EPR-based modelling of clay compressibility using the enhanced RCGA gives a more accurate and reliable correlation between the compression index and physical properties of remoulded clays.
Original languageEnglish
Pages (from-to)158-167
Number of pages10
JournalEngineering Geology
Volume210
DOIs
Publication statusPublished - 5 Aug 2016
Externally publishedYes

Keywords

  • Atterberg limits
  • Clays
  • Compressibility
  • Evolutionary regression
  • Genetic algorithm
  • Hybrid strategy

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geology

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