An evolutionary hybrid optimization of MARS model in predicting settlement of shallow foundations on sandy soils
Nguyen-Vu Luat,Van-Quang Nguyen,Seunghye Lee,Sungwoo Woo,Kihak Lee
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.
Nguyen-Vu Luat, Seunghye Lee and Kihak Lee — Department of Architectural Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, South Korea
Van-Quang Nguyen — Department of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, South Korea
Sungwoo Woo — TechSquare Ltd. South Korea, 25 Banpodae-ro, Seocho-gu, Seoul 06710, South Korea
PDF Viewer
Preview is limited to the first 3 pages. Sign in to access the full PDF.
