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The Application of Machine Learning in Geotechnical Engineering, 2nd Edition
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The Application of Machine Learning in Geotechnical Engineering, 2nd Edition

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

Deadline for manuscript submissions: 10 July 2025 | Viewed by 5414

Special Issue Editor

Prof. Dr. Wei Gao

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Hohai University, Nanjing 210024, China
Interests: application of artificial intelligence and big data technology in geotechnical engineering; development and utilization of smart underground space; intelligent prevention and control of geological disasters; intelligent construction of tunnels and underground engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural geological bodies are the objects of geotechnical engineering; their mechanical properties and internal structure are very complex. Most of the geotechnical engineering problems involve the coupling of multiple fields and multiple phases. Unsafe geotechnical engineering can result in serious engineering disasters, such as landslide and surface subsidence, etc., which cannot be solved well using traditional methods (e.g., theoretical methods, numerical methods and experimental methods). The development of artificial intelligence has supported better solutions to geotechnical engineering problems, and machine learning methods have been applied widely, currently representing a hot research topic. The present Special Issue intends to present new applications of machine learning methods in the field of geotechnical engineering, from planning and design to construction. The topics of interest include, but are not limited to, the applications of machine learning methods for slope engineering, underground engineering, and foundation engineering, the applications of machine learning methods in geomechanics, etc.

This Special Issue will publish high-quality original research papers on topics including, but not limited to, the following:

  • Applications of artificial neural networks;
  • Applications of deep learning methods;
  • Applications of swarm intelligence;
  • Applications of evolutionary algorithms;
  • Applications of big data analysis;
  • Applications of biological computation;
  • Applications of nature-inspired computation;
  • Applications of support vector machine, support vector regression, etc.;
  • Intelligent forecasting of geotechnical engineering disasters.

Prof. Dr. Wei Gao
Guest Editor

Manuscript Submission Information

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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

  • artificial neural networks
  • deep learning
  • big data
  • swarm intelligence
  • evolutionary algorithms
  • geotechnical engineering
  • slope engineering
  • underground engineering
  • foundation engineering
  • geomechanics

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Related Special Issue

Published Papers (5 papers)

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Research

25 pages, 10287 KiB  
Article
Multi-Objective Optimization for Controlling Conflicts in Roadway Surrounding Rock Induced by Floor Stress-Relief Groove
by Yanting Qin, Yuantian Sun, Guichen Li, Jinghua Li, Sen Yang, Enqing Chen and Cheng Zhang
Appl. Sci. 2024, 14(22), 10178; https://doi.org/10.3390/app142210178 - 6 Nov 2024
Viewed by 411
Abstract
This paper studies the effectiveness of the stress-relief groove on the floor of deep coal roadway and determines the influence of the stress-relief groove parameters on the surrounding rock through qualitative analysis. Based on the displacement conflict problem, evaluation indicators were established, and [...] Read more.
This paper studies the effectiveness of the stress-relief groove on the floor of deep coal roadway and determines the influence of the stress-relief groove parameters on the surrounding rock through qualitative analysis. Based on the displacement conflict problem, evaluation indicators were established, and the optimal solution set was obtained. The innovations of this research include: 1. For geotechnical numerical simulations, novel stress monitoring and plastic zone monitoring techniques have been introduced to accurately reflect the condition of the surrounding rock; 2. The effects of floor relief grooves in deep roadway on surrounding rock have been analyzed, and the advantages and utilities of central and corner relief grooves have been determined; 3. The usability of small datasets has been enhanced by applying SEGA to optimize machine learning models with data augmentation techniques; 4. Multi-objective optimization algorithms have been applied to geotechnical engineering, providing valuable references for decision making. The results demonstrate that multi-objective optimization can significantly enhance the effectiveness of surrounding rock control, resolve conflicts, and achieve more reasonable construction plans. This research provides new theoretical foundations and practical guidance for deep mine roadway-surrounding rock control. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering, 2nd Edition)
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17 pages, 5872 KiB  
Article
Prediction Models and Feature Importance Analysis for Service State of Tunnel Sections Based on Machine Learning
by Debo Zhao, Yujia Yang, Chengyong Cao and Bin Liu
Appl. Sci. 2024, 14(20), 9167; https://doi.org/10.3390/app14209167 - 10 Oct 2024
Viewed by 946
Abstract
The evaluation of tunnel service conditions is a core problem in the maintenance of tunnel structures during their life cycles. To address this problem, machine learning algorithms were applied to the National Tunnel Inventory (NTI) database of the Federal Highway Administration of the [...] Read more.
The evaluation of tunnel service conditions is a core problem in the maintenance of tunnel structures during their life cycles. To address this problem, machine learning algorithms were applied to the National Tunnel Inventory (NTI) database of the Federal Highway Administration of the United States to predict the service states of the structural, civil, and non-structural sections of a tunnel, respectively. The results indicate that ensemble learning algorithms such as Light Gradient Boosting Machine (LGBM) and Random Forest outperform Support Vector Machine, Multi-Layer Perceptron, Decision Tree, and K-Nearest Neighbor in solving imbalanced classification problems presented in the NTI database. The machine learning models established using the LGBM algorithm exhibited prediction accuracies of 90.9%, 96.4%, and 77.3% for the structural, civil, and non-structural sections, respectively. The importance sorting of features influencing the tunnel’s service state was then performed based on the LGBM model, revealing that the features with a significant impact on the service states of the structural, civil, and non-structural sections are service time, tunnel length and width, geographic position (longitude and latitude), minimum vertical clearance, annual average daily traffic (AADT), and annual average daily truck traffic (AADTT). Data-driven LGBM models identified human factors such as AADT and AADTT as key features influencing the service states of tunnels’ structural sections, and these factors should be taken into consideration in further research to elucidate the potential physical mechanisms. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering, 2nd Edition)
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17 pages, 3339 KiB  
Article
Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
by Mintae Kim, Muharrem A. Senturk and Liang Li
Appl. Sci. 2024, 14(19), 8695; https://doi.org/10.3390/app14198695 - 26 Sep 2024
Viewed by 1061
Abstract
Soil consolidation, particularly in fine-grained soils like clay, is crucial in predicting settlement and ensuring the stability of structures. Additionally, the compressibility of fine-grained soils is of critical importance not only in civil engineering but also in various other fields of study. The [...] Read more.
Soil consolidation, particularly in fine-grained soils like clay, is crucial in predicting settlement and ensuring the stability of structures. Additionally, the compressibility of fine-grained soils is of critical importance not only in civil engineering but also in various other fields of study. The compression index (Cc), derived from soil properties such as the liquid limit (LL), plastic limit (PL), plasticity index (PI), water content (w), initial void ratio (e0), and specific gravity (Gs), plays a vital role in understanding soil behavior. This study employs machine learning algorithms—the random forest regressor (RFR), gradient boosting regressor (GBR), and AdaBoost regressor (ABR)—to predict the Cc values based on a dataset comprising 915 samples. The dataset includes LL, PL, W, PI, Gs, and e0 as the inputs, with Cc as the output parameter. The algorithms are trained and evaluated using metrics such as the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Hyperparameter optimization is performed to enhance the model performance. The best-performing model, the GBR model, achieves a training R2 of 0.925 and a testing R2 of 0.930 with the input combination [w, PL, LL, PI, e0, Gs]. The RFR model follows closely, with a training R2 of 0.970 and a testing R2 of 0.926 using the same input combination. The ABR model records a training R2 of 0.847 and a testing R2 of 0.921 under similar conditions. These results indicate superior predictive accuracy compared to previous studies using traditional statistical and machine learning methods. Machine learning algorithms, specifically the gradient boosting regressor and random forest regressor, demonstrate substantial potential in predicting the Cc value for fine-grained soils based on multiple soil parameters. This study involves leveraging the efficiency and effectiveness of these algorithms in geotechnical engineering applications, offering a promising alternative to traditional oedometer testing methods. Accurately predicting the compression index can significantly aid in the assessment of soil settlement and the design of stable foundations, thereby reducing the time and costs associated with laboratory testing. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering, 2nd Edition)
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21 pages, 17813 KiB  
Article
Parametric Investigation of Corner Effect on Soil Nailed Walls and Prediction Using Machine Learning Methods
by Semiha Poyraz and İsa Vural
Appl. Sci. 2024, 14(16), 7331; https://doi.org/10.3390/app14167331 - 20 Aug 2024
Viewed by 991
Abstract
The performance of soil nailed walls is evaluated based on lateral displacements, especially in high walls. In this study, the displacement behavior of nailed walls, which are frequently preferred in retaining wall systems in hard clayey soils, was examined by taking into account [...] Read more.
The performance of soil nailed walls is evaluated based on lateral displacements, especially in high walls. In this study, the displacement behavior of nailed walls, which are frequently preferred in retaining wall systems in hard clayey soils, was examined by taking into account the corner effect. The nailed wall model was created using Plaxis 2D v.23, and the performance of the model was verified with the results of inclinometer measurements taken on-site. To assess the influence of excavation pit dimensions on the corner effect, 25 three-dimensional and 25 plane–strain slice models were created using Plaxis 3D v.23, and the effect of excavation pit dimensions on the plane–strain ratio (PSR) was determined. Then, analysis studies were carried out by creating 336 3D and 336 plane–strain slice models with variable parameters, such as slope angle (β), wall angle (α), nail length (L/H), excavation depth (H), and distance from the corner (xH). Its effects on PSR were determined. The interactions of the parameters with each other and PSR estimation were evaluated using machine learning (ML) methods: artificial neural networks (ANN), classifical and regression tree (CART), support vector regression (SVR), extreme gradient boosting (XGBoost). The proposed ML prediction methods and PSR results were compared with performance metrics and reliable results were obtained. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering, 2nd Edition)
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45 pages, 15849 KiB  
Article
Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil
by Ammar Alnmr, Haidar Hosamo Hosamo, Chuangxin Lyu, Richard Paul Ray and Mounzer Omran Alzawi
Appl. Sci. 2024, 14(11), 4819; https://doi.org/10.3390/app14114819 - 2 Jun 2024
Cited by 3 | Viewed by 1463
Abstract
This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite [...] Read more.
This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering, 2nd Edition)
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