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http://dx.doi.org/10.12989/gae.2020.21.6.583

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)
Publication Information
Geomechanics and Engineering / v.21, no.6, 2020 , pp. 583-598 More about this Journal
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
multivariate adaptive regression spline; genetic algorithm; evolutionary hybrid model; settlement prediction; shallow foundation;
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Times Cited By KSCI : 10  (Citation Analysis)
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