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

作     者：M.E.Al-Atroush A.E.Aboelela Ezz El-Din Hemdan M.E.Al-Atroush;A.E.Aboelela;Ezz El-Din Hemdan

作者机构：Civil and Environmental Engineering ProgramEngineering Management DepartmentCollege of EngineeringPrince Sultan UniversityRiyadhSaudi Arabia Department of Structural EngineeringFaculty of EngineeringAin Shams UniversityCairoEgypt Structure and Materials Research LabPrince Sultan UniversityRiyadhSaudi Arabia Computer Science and Engineering DepartmentFaculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt 

出 版 物：《Journal of Rock Mechanics and Geotechnical Engineering》 (岩石力学与岩土工程学报（英文版）)

年 卷 期：2024年第16卷第9期

页      面：3812-3840页

核心收录：

学科分类：081401[工学-岩土工程] 08[工学] 0814[工学-土木工程] 

基　　金：supported by Prince Sultan University(Grant No.PSU-CE-TECH-135 2023) 

主　　题：Laterally loaded drilled shaft load transfer and failure mechanisms Physics-informed neural networks(PINNs) P-y curves Artificial intelligence(AI) Dataset 

摘      要：In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** 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 *** 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 ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** 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 *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.