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Determination of Bearing Capacity of Shallow Foundation Using Soft Computing
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Author(s): Jagan J. (VIT University, India), Swaptik Chowdhury (VIT University, India), Pratik Goyal (VIT University, India), Pijush Samui (NIT Patna, India) and Yıldırım Dalkiliç (Erzincan University, Turkey)
Copyright: 2017
Pages: 37
Source title:
Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0788-8.ch061
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Abstract
The 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.
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