Geomechanics and Engineering

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Usage of coot optimization-based random forests analysis for determining the shallow foundation settlement

  • Yi, Han (School of Architecture, Anhui Science and Technology University) ;
  • Xingliang, Jiang (CCCC Water Transportation Consultants Co., Ltd.) ;
  • Ye, Wang (School of Architecture, Anhui Science and Technology University) ;
  • Hui, Wang (Department of Civil Engineering, Tongji University)
  • Received : 2022.10.13
  • Accepted : 2023.01.13
  • Published : 2023.02.10
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Abstract

Settlement estimation in cohesion materials is a crucial topic to tackle because of the complexity of the cohesion soil texture, which could be solved roughly by substituted solutions. The goal of this research was to implement recently developed machine learning features as effective methods to predict settlement (Sm) of shallow foundations over cohesion soil properties. These models include hybridized support vector regression (SVR), random forests (RF), and coot optimization algorithm (COM), and black widow optimization algorithm (BWOA). The results indicate that all created systems accurately simulated the Sm, with an R2 of better than 0.979 and 0.9765 for the train and test data phases, respectively. This indicates extraordinary efficiency and a good correlation between the experimental and simulated Sm. The model's results outperformed those of ANFIS - PSO, and COM - RF findings were much outstanding to those of the literature. By analyzing established designs utilizing different analysis aspects, such as various error criteria, Taylor diagrams, uncertainty analyses, and error distribution, it was feasible to arrive at the final result that the recommended COM - RF was the outperformed approach in the forecasting process of Sm of shallow foundation, while other techniques were also reliable.

Keywords

Acknowledgement

Survey of industrial land and establishment of database in Shengli East Road Area in Bengbu City of Anhui Province (880635); Domestic Visiting Project (gxgnfx2022042); Outstanding young and middle-aged backbone teachers project of college-level (210036); General Natural Science Project of College-level (2021zryb12).

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