首页|Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

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In 2023,pivotal advancements in artificial intelligence(AI)have significantly experienced.With that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled shafts.This study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their'black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these studies.Furthermore,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter piles.An extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this gap.The paper concluded with a di-rection for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the inter-pretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

Laterally loaded drilled shaftload transfer and failure mechanismsPhysics-informed neural networks(PINNs)P-y curvesArtificial intelligence(AI)Dataset

M.E.Al-Atroush、A.E.Aboelela、Ezz El-Din Hemdan

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Civil and Environmental Engineering Program,Engineering Management Department,College of Engineering,Prince Sultan University,Riyadh,Saudi Arabia

Department of Structural Engineering,Faculty of Engineering,Ain Shams University,Cairo,Egypt

Structure and Materials Research Lab,Prince Sultan University,Riyadh,Saudi Arabia

Computer Science and Engineering Department,Faculty of Electronic Engineering,Menoufia University,Menoufia,Egypt

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Prince Sultan UniversityPrince Sultan University

PSU-CE-TECH-1352023

2024

10.1016/j.jrmge.2024.03.017

岩石力学与岩土工程学报(英文版)
中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD
影响因子：0.404
ISSN：1674-7755
年,卷(期)：2024.16(9)