Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations  

作　　者：Abiodun Ismail Lawal Sangki Kwon 

机构地区：[1]Department of Energy Resources Engineering,Inha University,Incheon,South Korea [2]Department of Mining Engineering,Federal University of Technology,Akure,Nigeria

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2023年第3期747-759,共13页岩石力学与岩土工程学报（英文版）

基　　金：supported by Korea Research Fellowship Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(Grant No.2019H1D3A1A01102993);the Inha University Research Grant(2022).

摘　　要：Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.

关 键 词：Ultimate bearing capacity(UBC) GEOTECHNICS Grasshopper optimization algorithm(GOA) Salp swarm algorithm(SSA) Soft computing(SC)method 

分 类 号：TU4[建筑科学—土工工程]