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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 May 2025 | Viewed by 1621

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 polices can be found here.

Published Papers (2 papers)

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Research

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
Viewed by 634
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
Viewed by 692
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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