Modelling the mechanical behaviour of soils using machine learning algorithms with explicit formulations

Pin Zhang, Zhen Yu Yin, Yin Fu Jin, Xian Feng Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

This study systematically presents the application of machine learning (ML) algorithms for constructing a constitutive model for soils. A genetic algorithm is integrated with ML algorithms to determine the global optimum model, and the k-fold cross-validation method is used to enhance the models’ robustness. Three typical ML algorithms with formulations explicitly expressed [i.e., back-propagation neural network (BPNN), extreme learning machine (ELM) and evolutionary polynomial regression (EPR)], and two modelling strategies (i.e. total or incremental stress–strain strategies) are used. A synthetic database is first generated based on a simple constitutive model to objectively evaluate the performance of three ML algorithms and two modelling strategies. Next, the optimum ML algorithm and the well evaluated modelling strategy are applied to experimental tests for examining its robustness. All results indicate that a BPNN-based constitutive model using the incremental stress–strain strategy performs best in modelling the mechanical behaviour of soils in terms of interpolation and extrapolation abilities, followed by ELM and then EPR.

Original languageEnglish
JournalActa Geotechnica
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Constitutive model
  • Evolutionary computation
  • Extreme learning machine
  • Neural network
  • Optimization
  • Soils

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Earth and Planetary Sciences (miscellaneous)

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