Geomechanics and Engineering

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An evolutionary hybrid optimization of MARS model in predicting settlement of shallow foundations on sandy soils

  • Luat, Nguyen-Vu (Department of Architectural Engineering, Sejong University) ;
  • Nguyen, Van-Quang (Department of Civil and Environmental Engineering, Hanyang University) ;
  • Lee, Seunghye (Department of Architectural Engineering, Sejong University) ;
  • Woo, Sungwoo (TechSquare Ltd.) ;
  • Lee, Kihak (Department of Architectural Engineering, Sejong University)
  • Received : 2020.01.13
  • Accepted : 2020.05.18
  • Published : 2020.06.25
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Abstract

This study is attempted to propose a new hybrid artificial intelligence model called integrative genetic algorithm with multivariate adaptive regression splines (GA-MARS) for settlement prediction of shallow foundations on sandy soils. In this hybrid model, the evolution algorithm - Genetic Algorithm (GA) was used to search and optimize the hyperparameters of multivariate adaptive regression splines (MARS). For this purpose, a total of 180 experimental data were collected and analyzed from available researches with five-input variables including the bread of foundation (B), length to width (L/B), embedment ratio (Df/B), foundation net applied pressure (qnet), and average SPT blow count (NSPT). In further analysis, a new explicit formulation was derived from MARS and its accuracy was compared with four available formulae. The attained results indicated that the proposed GA-MARS model exhibited a more robust and better performance than the available methods.

Keywords

Acknowledgement

This research was supported by Ministry of Land, Infrastructure and Transport of Korean Government (Grant 20CTAP-C143093-03).

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