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Machine Learning and Numerical Modelling in Geotechnical Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 937

Special Issue Editors


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Guest Editor
School of Mining and Metallurgical Engineering, National Technical University of Athens, Zografou Campus, GR15773 Athens, Greece
Interests: underground space development; underground mine design; risk assessment in underground projects; ventilation; project cost estimation and feasibility assessment; applications of artificial neural networks in geoengineering
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Mining and Metallurgical Engineering, National Technical University of Athens, Zografou Campus, 9 Iroon Polytechniou Street, GR15773 Athens, Greece
Interests: rock mechanics and rock engineering; tunnelling; stability of underground openings; rock support and reinforcement
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Earth and Environnement, University of Leeds, Leeds LS2 9JT, UK
Interests: tunnelling; geotechnics; numerical modelling; underground space; sustainability; geothermal; rock mechanics; engineering geology; risk assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The proposed Special Issue of "Machine Learning and Numerical Modelling in Geotechnical Engineering" aims to explore the innovative integration of machine learning (ML) techniques and numerical modelling methodologies to address complex problems in geotechnical engineering. As the field progresses, traditional methods are increasingly complemented by advanced computational tools that offer enhanced predictive capabilities and greater insights into rock/soil behavior, foundation design, tunnels and underground space design, open-pit mine stability, and stability of slopes, as well as other critical aspects of geotechnical engineering.

We invite original research articles, reviews, and case studies that delve into the application of ML algorithms, such as neural networks, support vector machines, deep learning, and advanced/innovative numerical methods to model geotechnical phenomena. Contributions that demonstrate the hybridization of ML with finite element analysis (FEA), discrete element modelling (DEM), and other numerical methods are particularly welcome. We also seek papers that highlight the development and validation of novel ML models using extensive datasets, as well as those that present comparative studies between ML and traditional modelling approaches.

Key topics include, but are not limited to, the following:

  • Predictive modelling of soil/rock physical and mechanical properties using ML.
  • ML-based optimization in geotechnical design.
  • Applications of advanced and innovative 2D/3D/4D numerical modelling in geotechnical engineering.
  • Integration/coupling of ML and numerical modelling in geotechnical engineering.
  • Case studies demonstrating the practical implementation of ML / numerical modelling in geotechnical projects.
  • ML applications in mining operations, including ore body prediction and mine stability analysis.

This Special Issue aims to bridge the gap between theory and practice, offering a comprehensive overview of the current state of the art and future directions in the field. Through this endeavor, we hope to foster interdisciplinary collaboration and innovation, ultimately advancing the knowledge and application of ML and numerical modelling in geotechnical engineering.

Dr. Andreas Benardos
Dr. Pavlos Nomikos
Dr. Chrysothemis Paraskevopoulou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning in geotechnics
  • advanced numerical modelling techniques
  • predictive analysis for geotechnical properties
  • hybrid ML and numerical methods
  • finite element and discrete element coupling
  • geotechnical design optimization using ML
  • innovative computational geomechanics

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Published Papers (1 paper)

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Research

18 pages, 10387 KiB  
Article
Boosting Model Interpretability for Transparent ML in TBM Tunneling
by Konstantinos N. Sioutas and Andreas Benardos
Appl. Sci. 2024, 14(23), 11394; https://doi.org/10.3390/app142311394 - 6 Dec 2024
Viewed by 578
Abstract
Tunnel boring machines (TBMs) are essential for excavating metro tunnels, reducing disruptions to surrounding rock, and ensuring efficient progress. This study examines how machine learning (ML) models can predict key tunneling outcomes, focusing on making these predictions clearer. Specifically, the models aim to [...] Read more.
Tunnel boring machines (TBMs) are essential for excavating metro tunnels, reducing disruptions to surrounding rock, and ensuring efficient progress. This study examines how machine learning (ML) models can predict key tunneling outcomes, focusing on making these predictions clearer. Specifically, the models aim to predict surface settlements (ground sinking) and the TBM’s penetration rate (PR) during the Athens Metro Line 2 extension to Hellinikon. For surface settlements, four artificial neural networks (ANNs) were developed, achieving an accuracy of over 79%, on average. For the TBM’s PR, both an XGBoost Regressor (XGBR) and ANNs performed consistently well, offering reliable predictions. This study emphasizes model transparency mostly. Using the SHapley Additive exPlanations (SHAP) library, it is possible to explain how models make decisions, highlighting key factors like geological conditions and TBM operating data. With SHAP’s Tree Explainer and Deep Explainer techniques, the study reveals which parameters matter most, making ML models less of a “black box” and more practical for real-world metro tunnel projects. By showing how decisions are made, these tools give decision-makers confidence to rely on ML in complex tunneling operations. Full article
(This article belongs to the Special Issue Machine Learning and Numerical Modelling in Geotechnical Engineering)
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