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Arabian Journal for Science and Engineering

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20.02.2022 | Research Article-Civil Engineering

Reliability and Prediction of Embedment Depth of Sheet pile Walls Using Hybrid ANN with Optimization Techniques

verfasst von: T. Pradeep, Anasua GuhaRay, Abidhan Bardhan, Pijush Samui, Sanjay Kumar, Danial Jahed Armaghani

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 10/2022

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Abstract

Due to the fact that uncertainties in the field of geotechnical engineering are inescapable because this part of civil engineering deals largely with natural materials, the dependability analysis of geotechnical structures has gotten a lot of attention in recent decades. This study looks at the reliability of embedded depth of cantilever sheet pile walls that are embedded in cohesive soil and backfilled with cohesionless soil. For the reliability analysis, the first-order reliability method (FORM) was employed to analyse the cantilever sheet pile. With the use of various optimization strategies, namely artificial bee colony, ant colony optimization, ant lion colony, imperialist competitive algorithm, shuffled complex evolution and teaching–learning-based optimization, the widely used artificial neural network (ANN) is used to forecast the embedment depth of a cantilever sheet pile wall. The input variables for predicting pile embedded depth include soil properties such as cohesiveness (c), angle of internal friction (φ) and unit weight (γ). To assess the models` performance, the reliability index, rank analysis, Taylor diagram, accuracy matrix, DDR criterion, AIC criterion, and OBJ criterion are utilised. Based on the experimental results, the hybrid models of ANN and teaching–learning-based optimization (ANN-TLO) perform better in predicting the reliability of cantilever sheet pile walls. The proposed approach can be utilised to quantify the risk associated with such structures in civil engineering projects as an alternative tool.

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Titel: Reliability and Prediction of Embedment Depth of Sheet pile Walls Using Hybrid ANN with Optimization Techniques
verfasst von: T. Pradeep
Anasua GuhaRay
Abidhan Bardhan
Pijush Samui
Sanjay Kumar
Danial Jahed Armaghani
Publikationsdatum: 20.02.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 10/2022
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-022-06607-w

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