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Applied Sciences
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Applications of Machine Learning in Geotechnical Engineering
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Applications of Machine Learning 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 786

Special Issue Editors

Dr. Esteban Díaz

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Guest Editor
Department of Civil Engineering, University of Alicante, 03690 Alicante, Spain
Interests: machine learning; geotechnics; soil mechanics; ground improvement; artificial intellegence
Dr. Tiago Filipe da Silva Miranda

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Minho, 4704-553 Braga, Portugal
Interests: geotechnics; machine learning
Prof. Dr. Cheng-Yu Ku

E-Mail Website
Guest Editor
Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung, Taiwan
Interests: numerical methods; geotechnical engineering; groundwater modeling; groundwater; landslides modeling; slope stability; engineering geology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore and showcase innovative applications of machine learning and artificial intelligence in the field of geotechnical engineering. As the complexity of geotechnical challenges continues to grow, advanced computational methods offer new avenues for addressing these issues more effectively and sustainably.

We welcome original research contributions that utilize machine learning techniques to address a broad spectrum of geotechnical engineering challenges. From micro-scale soil behaviour to large-scale infrastructure projects, we seek papers that demonstrate how these advanced methods can enhance our understanding and improve engineering practises.

We especially encourage papers that focus on the following:

  • The interpretability and generalizability of machine learning models in geotechnical contexts.
  • Data-centric approaches.
  • Demonstrating clear potential for improving or transforming geotechnical engineering practises.

This Special Issue will publish, but is not limited to, high-quality original research papers on topics including machine learning and artificial intelligence applied to the following areas:

  • Slope stability analysis and landslide prediction.
  • Shallow and deep foundations analysis and design.
  • Embankments behaviour and design.
  • Site characterization.
  • Underground engineering and tunnelling.
  • Rocks and soils behaviour modelling and characterization.
  • Site investigation.
  • Ground improvement techniques.
  • Forecasting of geotechnical disasters.
  • Geohazard assessment and risk mitigation.
  • Sustainable geotechnical solutions.
  • Prediction of ground movements.
  • Groundwater flow.
  • Retaining walls analysis and design.
  • Characterization and behaviour of problematic soils (expansive, collapsible, organic, non-engineered fills, etc.).

Dr. Esteban Díaz
Dr. Tiago Filipe da Silva Miranda
Prof. Dr. Cheng-Yu Ku
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
  • artificial intelligence
  • geotechnical engineering
  • deep learning
  • underground engineering
  • foundation engineering
  • artificial neural networks
  • soil mechanics
  • rocks mechanics
  • geomechanics

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

Published Papers (2 papers)

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Research

21 pages, 18601 KiB  
Article
Predicting Clay Swelling Pressure: A Comparative Analysis of Advanced Symbolic Regression Techniques
by Esteban Díaz and Roberto Tomás
Appl. Sci. 2025, 15(10), 5603; https://doi.org/10.3390/app15105603 - 16 May 2025
Viewed by 97
Abstract
Swelling pressure is a key geotechnical property that influences the behaviour and stability of engineering structures built on expansive clayey soils. This pressure can be measured directly through laboratory tests or estimated using indirect methods. This paper analyses a dataset of undisturbed clay [...] Read more.
Swelling pressure is a key geotechnical property that influences the behaviour and stability of engineering structures built on expansive clayey soils. This pressure can be measured directly through laboratory tests or estimated using indirect methods. This paper analyses a dataset of undisturbed clay samples from southeastern Spain using advanced symbolic regression techniques, namely: deep symbolic regression (PhySO), high-performance symbolic regression (PySR), multi-objective symbolic regression (MOSR), and physics-guided symbolic regression (PGSR). These methods provide interpretable results as equations, unlike standard machine learning models. All generated equations showed high performance (R2 > 0.91 and MAE < 23 kPa) and simplicity, making them suitable for practical engineering applications. PySR yielded the best overall metrics (R2 = 0.933, MAE = 20.49 kPa), particularly excelling in high-pressure ranges, while PhySO demonstrated the most balanced performance, especially for low to medium pressures. MOSR minimized edge-case bias, and PGSR, despite lower overall performance, remained competitive. The plasticity index (PI) was identified as the most influential factor in all models, followed by the percentage of fines. The use of undisturbed samples enhanced the reliability of the findings, and the resulting equations enable a flexible estimation of swelling pressure based on commonly available geotechnical parameters. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Geotechnical Engineering)
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18 pages, 6744 KiB  
Article
A Spatiotemporal-Adaptive-Network-Based Method for Predicting Axial Forces in Assembly Steel Struts with Servo System of Foundation Pits
by Weiwei Liu, Jianchao Sheng, Jian Zhou, Jinbo Fu, Wangjing Yao, Kuan Chang and Zhe Wang
Appl. Sci. 2025, 15(5), 2343; https://doi.org/10.3390/app15052343 - 22 Feb 2025
Viewed by 334
Abstract
The axial force in assembly steel struts with servo systems is a critical indicator of stability in foundation pit support systems. Due to its high sensitivity to temperature variations and direct influence on the lateral deformation of the foundation pit enclosure structure, accurate [...] Read more.
The axial force in assembly steel struts with servo systems is a critical indicator of stability in foundation pit support systems. Due to its high sensitivity to temperature variations and direct influence on the lateral deformation of the foundation pit enclosure structure, accurate prediction is essential for safety monitoring and early warning. This study proposes a novel method for predicting the axial force in assembly steel struts with servo systems based on a spatiotemporal adaptive network. The method begins by feeding historical axial force data from multiple steel struts into an LSTM network to extract temporal sequence features. A self-attention mechanism is then employed to capture the global dependencies within the axial force data, enhancing the feature representation. Concurrently, a convolutional neural network (CNN) is utilized to extract local spatial features. Additionally, excavation depth and excavated soil stratification data are processed through convolutional operations to derive stratification-related features. Subsequently, the temporal and spatial features of axial force are fused with stratification-related features derived from excavation data and further refined through a CNN, enabling more accurate predictions. Validation using deep foundation pit data from a metro station in Zhejiang Province demonstrated the method’s reliability and improved performance across multiple metrics compared to the existing approaches. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Geotechnical Engineering)
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