[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120335-en":3,"doc-seo-120335-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120335,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Physics-informed machine learning in geotechnical engineering: a direction paper","This direction paper explores the evolving landscape of physics-informed machine learning (PIML) methodologies in geotechnical engineering, delivering a comprehensive overview of current progress and proposing future research directions. By leveraging the intrinsic link between geophysical phenomena and geotechnical processes, it analyzes how physics-based models can be integrated with machine learning to improve interpretability, accuracy, and reliability. The paper reviews recent PIML applications across soil mechanics, hydrology, site investigation, slope stability, and foundation engineering, then outlines promising research paths including domain knowledge integration, explainability, multiphysics/multiscale modeling, complex constitutive laws, digital twins, and large AI models for resilient infrastructure.","GEOMECHANICS AND GEOENGINEERING 2025, VOL. 20, NO. 5, 1128–1159  \n[https://doi.org/10.1080/17486025.2025.2502029](https://doi.org/10.1080/17486025.2025.2502029)  \n| \u003Cbr>Physics-informed machine learning in geotechnical engineering: a direction paper\u003Cbr>Biao Yuana, Chung Siung Choob, Lit Yen Yeob, Yu Wangc, Zhongxuan Yangd, Qingzheng Guand, Stephen Suryasentanae, Jinhyun Choo f, Hao Sheng, Maria Megiah, Jiangwei Zhangi, Zhongqiang Liuj, Yanjie Song a, Hui Wangk and Xiaohui Chen a\u003Cbr>aGeomodelling and Artificial Intelligence Centre, School of Civil Engineering, University of Leeds, Leeds, UK; bFaculty of Engineering, Computing and Science, Swinburne University of Technology, Kuching, Malaysia; cDepartment of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China; dDepartment of Civil Engineering, Zhejiang University, Hangzhou, China; e Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow, UK; fDepartment of Civil and Environmental Engineering, KAIST, Daejeon, South Korea; gKlohn Crippen, Berger, Brisbane, Queensland, Australia; hDepartment of Structural Mechanics and Hydraulic Engineering, University of Granada, Granada, Spain; iSchool of Resources and Geosciences, China University of Mining and Technology, Xuzhou, China; jDepartment of Natural Hazards, Norwegian Geotechnical Institute (NGI), Oslo, Norway; kDepartment of Civil and Environmental Engineering and Engineering Mechanics, University of Dayton, Dayton, OH, USA |  |  |\n| --- | --- | --- |\n| ABSTRACT\u003Cbr>This direction paper explores the evolving landscape of physics-informed machine learning (PIML) methodologies in the field of geotechnical engineering, aiming to provide a comprehensive overview of current advancements and propose future research directions. Recognising the intrinsic connection between geophysical phenomena and geotechnical processes, we delve into the intersection of physics-based models and machine learning techniques. The paper begins by elucidating the significance of incorporating physicsinformed approaches, emphasising their potential to enhance the interpretability, accuracy and reliability of predictive models in geotechnical applications. We review recent applications of PIML in soil mechanics, hydrology, geotechnical site investigation, slope stability analysis and foundation engineering, showcasing successes and challenges. Furthermore, we identify promising avenues for future research in geotechnical engineering, including the integration of domain knowledge, model explainability, multiphysics and multiscale problems, complex constitutive models, as well as digital twins and large AI models within PIML frameworks. As geotechnical engineering embraces the paradigm shift towards data-driven methodologies, this direction paper offers valuable insights for researchers and practitioners, guiding the trajectory of PIML for sustainable and resilient infrastructure development. |  | ARTICLE HISTORY\u003Cbr>Received 1 August 2024 Accepted 30 April 2025\u003Cbr>KEYWORDS\u003Cbr>physics-informed machine learning (PIML); geotechnical engineering; AI for geoscience; literature review; direction paper |\n| 1. Introduction\u003Cbr>1.1. Introduction of physics-informed machine learning into geotechnical engineering\u003Cbr>In recent years, the intersection of machine learning and physical laws has led to the emergence of a novel paradigm known as physics-informed machine learning (PIML) . This approach integrates well-established machine learning algorithms with the fundamental principles of physical sciences, resulting in models that are not only data-driven but also adhere to underlying physical laws.\u003Cbr>Physics-informed neural networks (PINNs) represent a breakthrough in combining physical laws with deep learning techniques, specifically neural networks, to solve both forward and inverse problems with high data efficiency (Raissi et al. 2019) . This framework is | a part or subset of the broader field of PIML, where mach","cbCailiRISJxRNaf","https://ap.wps.com/l/cbCailiRISJxRNaf","pdf",2886994,1,32,"English","en",105,"# Abstract\n# Introduction\n## Introduction of physics-informed machine learning into geotechnical engineering\n# Article information\n## Article history\n## Keywords","[{\"question\":\"What is the goal of this direction paper on physics-informed machine learning (PIML) in geotechnical engineering?\",\"answer\":\"It provides an overview of current PIML advancements in geotechnical engineering and proposes future research directions.\"},{\"question\":\"How does PIML differ from traditional machine learning in this context?\",\"answer\":\"PIML integrates physical laws into the learning process so models remain data-driven while adhering to known physical constraints, often via terms in the loss function.\"},{\"question\":\"Which geotechnical application areas are reviewed in the paper?\",\"answer\":\"The paper reviews PIML applications in soil mechanics, hydrology, geotechnical site investigation, slope stability analysis, and foundation engineering.\"}]","Physics-informed machine learning in geotechnical engineering: a direction paper | 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