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

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第9期16卷 3812-3840页

作者： Al-Atroush M.E. Aboelela A.E. El-Din Hemdan E. 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

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 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. © 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences

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

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