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Neural Computing and Applications

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03-08-2020 | Original Article

The feasibility of PSO–ANFIS in estimating bearing capacity of strip foundations rested on cohesionless slope

Authors: Hossein Moayedi, Abbas Rezaei

Published in: Neural Computing and Applications | Issue 9/2021

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Abstract

This study aimed to assess particle swarm optimization (PSO) optimized with an adaptive network-based fuzzy inference system (ANFIS) for the prediction of the ultimate bearing capacity of strip footing resting on a sloping crest. To make datasets, a 2524 finite element model (FEM) simulation analysis was performed. In this sense, the k-fold validation technique selected for the selection of the testing and validation purposes. Also, the variables of the ANFIS algorithm (i.e., the number of clusters), and the PSO algorithm (the population size were optimized using a series of trial and error process. The second main objective of the current study was to assess the applicability of PSO–ANFIS in estimating the ultimate bearing capacity of the strip footing resting on a single cohesionless slope when it was subjected to an external vertical applied stress. The input parameters were the soil properties (S_type) (classified into five different classes from the weakest, shown as S₁, to the strongest type, displayed in higher order), slope angle (β), setback distance ratio (b/B) (the distance of the footing from the slope crest to the footing width), and vertical settlement (U_y) of the footing, while the main output was the applied vertical stresses (F_y) that can be applied on the footing. Note that the ultimate bearing capacity (F_ult) is the maximum vertical stress when U_y = 0.1 B. A thorough comparison between the measured FEM simulations, the proposed ANFIS and PSO–ANFIS models were carried out to demonstrate their effectiveness in predicting a reliable F_ult resting on cohesionless slopes.

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Title: The feasibility of PSO–ANFIS in estimating bearing capacity of strip foundations rested on cohesionless slope
Authors: Hossein Moayedi
Abbas Rezaei
Publication date: 03-08-2020
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 9/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05231-9

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