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Determination of bearing capacity of shallow foundation using soft computingJagan, J.Chowdhury, SwaptikGoyal, PratikSamui, PijushDalkiliç, YildirimFoundationsLearning systemsSoft computingCohesionless soilDeveloped modelExtreme learning machineGaussian process regressionGeotechnicalMinimax probability machineShallow foundationsUltimate bearing capacityBearing capacityThe ultimate bearing capacity is an important criterion for the successful implementation of any geotechnical projects. This chapter studies the feasibility of employing Gaussian process regression (GPR), Extreme learning machine (ELM) and Minimax probability machine regression (MPMR) for prediction of ultimate bearing capacity of shallow foundation based on cohesionless soils. The developed models have been compared on the basis of coefficient of relation (R) values (GPR= 0.9625, ELM= 0.938, MPMR= 0.9625). The results show that MPMR is more efficient tool but the models of GPR and ELM also gives satisfactory results. © 2017 by IGI Global. All rights reserved.IGI Global2016Book Chapterhttps://rdlopen.rawdatalibrary.net/r/ebyu/14943https://doi.org/10.4018/978-1-5225-0788-8.ch061978-152250789-5; 1522507884; 978-152250788-8enNature-Inspired Computing: Concepts, Methodologies, Tools, and Applicationsinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/bookParthttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/14943