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Infrastructures
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Advances in Artificial Intelligence for Geotechnical Engineering
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Advances in Artificial Intelligence for Geotechnical Engineering

A Special Issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 31 October 2026 | Viewed by 4025

Editors

Dr. Zia Ur Rehman

E-Mail Website
Guest Editor
College of Science and Engineering, School of Engineering, University of Derby, Derby, UK
Interests: civil engineering; geotechnical engineering; computational modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Usama Khalid

E-Mail Website
Guest Editor
Geotechnical Engineering Department, National Institute of Transportation, National University of Sciences and Technology, Islamabad, Pakistan
Interests: geo-structures; eco-friendly construction materials; geo-environmental infrastructure; desiccation crack impact on infrastructure; predictive modelling techniques; constitutive modelling of deep foundations
Special Issues, Collections and Topics in MDPI journals
Dr. Nauman Ijaz

E-Mail Website
Guest Editor
School of Civil Engineering, Quanzhou University of Information Engineering, Quanzhou 362000, China
Interests: soil–structure interaction; ground improvement techniques; low carbon building construction material; geotechnical design for building foundations; bearing capacity and settlement; seismic performance of foundations; sustainable foundation systems; computational modeling of foundation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of artificial intelligence (AI) and machine learning technologies has opened new frontiers across many branches of engineering, including geotechnical engineering. As geotechnical systems often involve complex, nonlinear, and spatially variable conditions, traditional modelling approaches can be limited in their capacity to handle uncertainty, heterogeneity, and large datasets. AI-based approaches provide promising alternatives that can enhance predictive capabilities, improve design efficiency, and enable real-time decision-making in geotechnical engineering practice.

This Special Issue aims to present recent developments, innovative applications, and theoretical advancements in the use of AI for geotechnical engineering problems. It will serve as a platform for researchers and practitioners to share knowledge, foster collaboration, and highlight the role of AI in shaping the future of geotechnical research and practice.

High-quality submissions are invited that address, but are not limited to, the following areas:

  • AI-driven modelling of soil behaviour and geotechnical parameters
  • Machine learning and deep learning applications in site characterization
  • Surrogate and reduced-order models for computationally intensive geotechnical simulations
  • Symbolic and interpretable AI (e.g., Genetic Expression Programming, Grammatical Evolution) for geotechnical analysis
  • AI-enhanced risk assessment for landslides, foundations, and underground structures
  • Integration of sensor data and AI for real-time geotechnical monitoring
  • Seismic response prediction using AI methods
  • AI-based optimization in geotechnical design and decision-making
  • Case studies showcasing practical implementation of AI in geotechnics

Dr. Zia Ur Rehman
Dr. Usama Khalid
Dr. Nauman Ijaz
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Infrastructures is an international peer-reviewed open access monthly 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 1800 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
  • deep learning
  • soil
  • soil–structure interaction
  • optimization
  • constitutive modelling
  • numerical modelling

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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

35 pages, 11877 KB  
Article
Reliability-Based Slope Stability Analysis Using Particle Swarm-Optimized Neural Network: Benchmarking Against Conventional Probabilistic Methods Using a Lebanese Case Study
by Shaza Soleiman and Muhsin Elie Rahhal
Infrastructures 2026, 11(9), 295; https://doi.org/10.3390/infrastructures11090295 - 24 Aug 2026
Viewed by 321
Abstract
Probabilistic slope stability analysis requires tools that are both computationally efficient and accurate for uncertainty propagation. This study develops a reliability-oriented surrogate framework coupling a multilayer perceptron artificial neural network with particle swarm optimization (ANN–MLP–PSO). The model was trained on 2014 homogeneous slope [...] Read more.
Probabilistic slope stability analysis requires tools that are both computationally efficient and accurate for uncertainty propagation. This study develops a reliability-oriented surrogate framework coupling a multilayer perceptron artificial neural network with particle swarm optimization (ANN–MLP–PSO). The model was trained on 2014 homogeneous slope cases drawn from literature records and mechanics-based simulations. PSO identified a best-performing six-hidden-layer architecture achieving a coefficient of determination of R2 = 0.95 on the held-out test set. The trained surrogate was embedded in a probabilistic sampling framework to estimate the probability of failure (Pf), reliability index (β), and factor-of-safety quantiles, then applied to the Mansourieh slope near Beirut, Lebanon, under dry and wet conditions. Outputs were benchmarked against the First-Order Second-Moment method (FOSM), the Point Estimate Method (PEM), and Monte Carlo simulation (MCS). The comparison showed that the ANN–MLP–PSO surrogate reproduced the dry-to-wet changes in factor-of-safety distributions, probability of failure, and reliability index obtained from the conventional reliability methods under the same probabilistic assumptions, with wet-scenario failure probabilities ranging from approximately 86% to 99%. Despite quantitative differences, all four methods identified the same reliability trend and engineering interpretation. Once trained, the surrogate enabled rapid probabilistic evaluation without repeated deterministic calculations, providing an efficient tool for slope stability screening and uncertainty-aware geotechnical decision support. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Geotechnical Engineering)
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36 pages, 11622 KB  
Article
Explainable Hybrid Intelligence for Predicting Tunnel Water Inrush Quantity Under Small-Sample, High-Heterogeneity Conditions: GAN Augmentation and Swarm-Optimized CatBoost
by Rui Huang, Yige Chen, Lanjing Wang, Jing Zhan, Yuanfan Ji, Tingyu Huang and Yanbo Yang
Infrastructures 2026, 11(6), 183; https://doi.org/10.3390/infrastructures11060183 - 25 May 2026
Viewed by 419
Abstract
This study aims to explore a leakage-aware and explainable machine learning framework for predicting tunnel water inrush quantity (WIQ) under small-sample and high-heterogeneity geological conditions. A project-level dataset was compiled at a fixed spatial granularity of 30 m per excavation segment by integrating [...] Read more.
This study aims to explore a leakage-aware and explainable machine learning framework for predicting tunnel water inrush quantity (WIQ) under small-sample and high-heterogeneity geological conditions. A project-level dataset was compiled at a fixed spatial granularity of 30 m per excavation segment by integrating forward prospecting outputs, construction-face observations, and geological reports, and six hydrogeological–structural indicators were used to predict the water inflow rate in cubic meters per hour. To overcome data scarcity and improve generalization, a tabular generative adversarial network (GAN) was introduced to augment the training distribution while preserving marginal statistics and inter-variable dependence, and a swarm-intelligence optimizer was employed to tune a Categorical Boosting (CatBoost) regressor for stable performance. In addition, six mainstream tree-based learners were benchmarked under a unified protocol, and model transparency was ensured through a multi-level interpretability suite combining SHapley Additive exPlanations (SHAP) attribution, partial dependence with individual conditional expectation (ICE) diagnostics, and interaction surfaces. Results show that, under the present fixed split, training-set augmentation was associated with improved performance for the evaluated baseline learners, and the proposed hybrid model achieved encouraging hold-out accuracy. However, because the dataset contains only 55 real samples and the test set contains only 11 real samples, the reported performance should be interpreted as an initial project-specific indication rather than robust evidence of generalizable reliability. Interpretability analyses further identify lithologic and reflector-related factors as dominant drivers, and reveal nonlinear response patterns and interaction-sensitive high-risk regions. Overall, the proposed framework shows potential to improve predictive performance and engineering interpretability for the studied project, and may provide a useful reference for drainage and reinforcement planning. Further confirmation through repeated data splitting, additional samples, and external validation is still needed before broader application. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Geotechnical Engineering)
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18 pages, 2708 KB  
Article
Interpretable Ensemble Machine Learning for Liquefaction Risk Prediction
by Doszhan Tuzelbayev, Sung-Woo Moon, Minho Lee, Shynggys Abdialim, Elijah Adebayonle Aremu, Alfrendo Satyanaga and Jong Kim
Infrastructures 2025, 10(11), 304; https://doi.org/10.3390/infrastructures10110304 - 11 Nov 2025
Viewed by 1068
Abstract
This paper presents a comprehensive machine learning (ML) framework for predicting liquefaction risk, a crucial aspect of seismic hazard assessment. A benchmark geotechnical dataset with multi-dimensional input features was used to evaluate several ML classifiers, followed by hyperparameter optimization through stratified 5-fold cross-validation. [...] Read more.
This paper presents a comprehensive machine learning (ML) framework for predicting liquefaction risk, a crucial aspect of seismic hazard assessment. A benchmark geotechnical dataset with multi-dimensional input features was used to evaluate several ML classifiers, followed by hyperparameter optimization through stratified 5-fold cross-validation. Optimized models were combined into a soft Voting Ensemble to enhance stability and accuracy of liquefaction potential prediction. The proposed ensemble model achieved a mean accuracy of 90.12% and a recall of 97.23%, outperforming individual models in most folds. The ensemble’s effectiveness was further evidenced by its precision-recall (PR) and receiver operating characteristic (ROC) curves, with areas under the curve (AUC) of 0.962 and 0.931, respectively—closely matching those of the Gradient Boosting classifier, indicating comparable discriminatory performance. Additionally, SHapley Additive exPlanations (SHAP) analysis was conducted on the ensemble model to assess contributions of each geotechnical inputs to the predictions, revealing that normalized shear wave velocity (VS1) as the most influential variable in liquefaction prediction. The proposed framework demonstrates a robust, interpretable, and performance-consistent approach for liquefaction risk assessment. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Geotechnical Engineering)
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