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Sustainability
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Machine Learning and Artificial Intelligence in Geotechnical and Underground Infrastructures
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Machine Learning and Artificial Intelligence in Geotechnical and Underground Infrastructures

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: 25 January 2026 | Viewed by 4955

Special Issue Editors

Prof. Dr. Qiujing Pan

E-Mail Website
Guest Editor
School of Civil Engineering, Central South University, Changsha 410083, China
Interests: data-driven geotechnics; tunnelling; digital twin in geotechnical and underground engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Dalong Jin

E-Mail Website
Co-Guest Editor
School of Civil engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: tunnel and underground space, shield tunneling, soil-structure interaction
Dr. Yutao Pan

E-Mail Website
Co-Guest Editor
Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, 7052 Trondheim, Norway
Interests: underground construction; risk assessment; ground improvement
Special Issues, Collections and Topics in MDPI journals
Dr. Hui Xu

E-Mail Website
Co-Guest Editor
School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China
Interests: municipal solid waste; construction solid waste; excavated soil; slope stability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The modern engineering industry has erected many geotechnical infrastructures (e.g., slopes, tunnels, sewers, subway stations, and deep foundations), facing many uncertainties in geological formation and construction workmanship and simultaneously producing a large amount of multi-source and heterogeneous data during the planning, design, construction, and maintenance of these underground assets. In the era of Industry 4.0, engineers and researchers in the civil community have become more interdisciplinary and are required to harness the potential of machine learning, artificial intelligence, and information modeling techniques to provide novel solutions to new challenges in the design, construction, and maintenance of underground engineering.

To advance the development and application of machine learning and artificial intelligence in geotechnical and underground engineering, and to enhance its digital, information, and intelligence capabilities, Sustainability presents a Special Issue that focuses on the application of machine learning and artificial intelligence technologies in the design, construction, operation, and maintenance of geotechnical and underground infrastructures.

Prof. Dr. Qiujing Pan
Guest Editors

Dr. Dalong Jin
Dr. Yutao Pan
Dr. Hui Xu
Co-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. Sustainability 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

  • data-centric geotechnics
  • geotechnical risk assessments and management
  • computer vision in geotechnical engineering
  • building information modeling and digital twin
  • integration of data-driven and physics-based methods

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

Published Papers (3 papers)

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Research

21 pages, 4796 KiB  
Article
Prediction and Control of Existing High-Speed Railway Tunnel Deformation Induced by Shield Undercrossing Based on BO-XGboost
by Ruizhen Fei, Hongtao Wu and Limin Peng
Sustainability 2024, 16(23), 10563; https://doi.org/10.3390/su162310563 - 2 Dec 2024
Cited by 1 | Viewed by 1152
Abstract
The settlement of existing high-speed railway tunnels due to adjacent excavations is a complex phenomenon influenced by multiple factors, making accurate estimation challenging. To address this issue, a prediction model combining extreme gradient boosting (XGBoost) with Bayesian optimization (BO), namely BO-XGBoost, was developed. [...] Read more.
The settlement of existing high-speed railway tunnels due to adjacent excavations is a complex phenomenon influenced by multiple factors, making accurate estimation challenging. To address this issue, a prediction model combining extreme gradient boosting (XGBoost) with Bayesian optimization (BO), namely BO-XGBoost, was developed. Its predictive performance was evaluated against conventional models, such as artificial neural networks (ANNs), support vector machines (SVMs), and vanilla XGBoost. The BO-XGBoost model showed superior results, with evaluation metrics of MAE = 0.331, RMSE = 0.595, and R2 = 0.997. In addition, the BO-XGBoost model enhanced interpretability through an accessible analysis of feature importance, identifying volume loss as the most critical factor affecting settlement predictions. Using the prediction model and a particle swarm optimization (PSO) algorithm, a hybrid framework was established to adjust the operational parameters of a shield tunneling machine in the Changsha Metro Line 3 project. This framework facilitates the timely optimization of operational parameters and the implementation of protective measures to mitigate excessive settlement. With this framework’s assistance, the maximum settlements of the existing tunnel in all typical sections were strictly controlled within safety criteria. As a result, the corresponding environmental impact was minimized and resource management was optimized, ensuring construction safety, operational efficiency, and long-term sustainability. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Geotechnical and Underground Infrastructures)
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19 pages, 8296 KiB  
Article
Research on Multi-Objective Optimization of Shield Tunneling Parameters Based on Power Consumption and Efficiency
by Wei Wang, Huanhuan Feng, Yanzong Li, Xudong Zheng, Jinhui Qi and Huaize Sun
Sustainability 2024, 16(14), 6152; https://doi.org/10.3390/su16146152 - 18 Jul 2024
Cited by 4 | Viewed by 1425
Abstract
The shield tunneling method is commonly used in the development and construction of underground spaces, and the adjustment of its parameters is a crucial part of shield construction. However, there are relatively few studies on optimizing tunneling parameters from a sustainable perspective, with [...] Read more.
The shield tunneling method is commonly used in the development and construction of underground spaces, and the adjustment of its parameters is a crucial part of shield construction. However, there are relatively few studies on optimizing tunneling parameters from a sustainable perspective, with a focus on energy saving and emission reduction. This study addresses this gap by combining engineering geological conditions with shield machine propulsion parameters in a specific section of metro construction in China. By aiming to reduce power consumption and improve efficiency, an improved particle swarm optimization algorithm based on the concept of Pareto optimal solutions was employed to optimize the tunneling parameters. The results demonstrated that the optimized parameters reduced power consumption and improved efficiency. This validates the feasibility of the optimization scheme and its potential for broader applications in sustainable underground construction. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Geotechnical and Underground Infrastructures)
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14 pages, 1288 KiB  
Article
Geotechnical Site Characterizations Using a Bayesian-Optimized Multi-Output Gaussian Process
by Ming-Qing Peng, Zhi-Chao Qiu, Si-Liang Shen, Yu-Cheng Li, Jia-Jie Zhou and Hui Xu
Sustainability 2024, 16(13), 5759; https://doi.org/10.3390/su16135759 - 5 Jul 2024
Cited by 1 | Viewed by 1547
Abstract
Geotechnical site characterizations aim to determine site-specific subsurface profiles and provide a comprehensive understanding of associated soil properties, which are important for geotechnical engineering design. Traditional methods often neglect the inherent cross-correlations among different soil properties, leading to high bias in site characterization [...] Read more.
Geotechnical site characterizations aim to determine site-specific subsurface profiles and provide a comprehensive understanding of associated soil properties, which are important for geotechnical engineering design. Traditional methods often neglect the inherent cross-correlations among different soil properties, leading to high bias in site characterization interpretations. This paper introduces a novel data-driven site characterization (DDSC) method that employs the Bayesian-optimized multi-output Gaussian process (BO-MOGP) to capture both the spatial correlations across different site locations and the cross-correlations among various soil properties. By considering the dual-correlation feature, the proposed BO-MOGP method enhances the accuracy of predictions of soil properties by leveraging information as much as possible across multiple soil properties. The superiority of the proposed method is demonstrated through a simulated example and the case study of a Taipei construction site. These examples illustrate that the proposed BO-MOGP method outperforms traditional methods that fail to consider both types of correlations, as evidenced by the reduced prediction uncertainty and the accurate identification of cross-correlations. Furthermore, the ability of the proposed BO-MOGP method to generate conditional random fields supports its effectiveness in geotechnical site characterizations. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Geotechnical and Underground Infrastructures)
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