Smart Technologies for Sustainable and Resilient Underground Infrastructures

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 953

Special Issue Editors


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Guest Editor
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China
Interests: intelligent planning techniques for underground space; sustainable underground urban systems; metro-led underground space development; urban informatics for underground space

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Guest Editor
School of Resources and Civil Engineering, Northeastern University, Shenyang, China
Interests: tunnelling and geotechnical engineering; high-performance materials for underground structures; multiscale analysis of underground structures

Special Issue Information

Dear Colleagues,

Recent decades have witnessed a worldwide surge in urban underground spatial development. Such trends are primarily driven by a need to address pressing urban challenges, such as land scarcity, traffic congestion, and environmental degradation. Rapidly expanding underground infrastructure systems (including components such as utility networks, metro systems, underground roads, pedestrian networks, public spaces, disaster-resistant structures, and storage facilities), are crucial for modern high-density cities. They provide essential services related to transportation, utilities, and public amenities.

However, these complex systems face mounting challenges related to sustainable construction requirements, efficient structural defect detection, effective lifecycle maintenance, and the optimized management of intricate underground systems. These challenges demand innovative solutions to ensure the long-term sustainability and resilience of underground infrastructure. In recent years, the unprecedented development of smart technologies, including advanced engineering informatics, smart and automated construction methods, intelligent operation and maintenance strategies, generative planning and design tools, high-performance materials, and artificial intelligence, has yielded transformative opportunities to address these challenges and optimize underground infrastructure performance throughout its lifecycle.

Accordingly, this Special Issue, titled “Smart Technologies For Sustainable and Resilient Underground Infrastructures”, aims to showcase cutting-edge research and development in the application of smart technologies for enhancing the sustainability and resilience of modern underground infrastructure. It seeks to foster a deeper understanding of how these technologies can be leveraged to improve the planning, design, construction, operation, and maintenance of underground systems, ultimately contributing to better urban built environments.

This Special Issue welcomes original research articles and reviews on advances in underground infrastructure development. Areas of interest include, but are not limited to, the following:

  • Artificial intelligence for the predictive maintenance of underground infrastructure;
  • Engineering informatics for the smart sensing and monitoring of underground infrastructure;
  • Automated construction technologies for underground infrastructure;
  • Resilience-based designing and retrofitting for underground infrastructure;
  • The use of sustainable materials and high-performance construction for underground infrastructure;
  • Intelligent planning and design methods for integrated underground infrastructure systems;
  • Lifecycle assessments of sustainable underground infrastructure solutions;
  • Environmental, social, and governance issues regarding underground infrastructure development.

We look forward to receiving your contributions.

Dr. Yunhao Dong
Dr. Tong Zhang
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. 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

  • underground infrastructure
  • resilience
  • sustainability
  • high-performance cementitious materials
  • engineering informatics
  • planning and design
  • smart construction
  • intelligent operation
  • lifecycle analysis

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Published Papers (1 paper)

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Research

24 pages, 10558 KB  
Article
Hybrid Machine Learning Meta-Model for the Condition Assessment of Urban Underground Pipes
by Mohsen Mohammadagha, Mohammad Najafi, Vinayak Kaushal and Ahmad Jibreen
Infrastructures 2025, 10(11), 282; https://doi.org/10.3390/infrastructures10110282 - 23 Oct 2025
Viewed by 358
Abstract
Urban water infrastructure faces increasing deterioration, necessitating accurate, cost-effective condition assessment. Traditional inspection techniques are intrusive and inefficient, creating demand for scalable machine learning (ML) solutions. This study develops a hybrid ML meta-model to predict underground pipe conditions using a comprehensive dataset of [...] Read more.
Urban water infrastructure faces increasing deterioration, necessitating accurate, cost-effective condition assessment. Traditional inspection techniques are intrusive and inefficient, creating demand for scalable machine learning (ML) solutions. This study develops a hybrid ML meta-model to predict underground pipe conditions using a comprehensive dataset of 11,544 records. The objective is to enhance multi-class classification performance while preserving interpretability. A stacked hybrid architecture was employed, integrating Random Forest, LightGBM, and CatBoost models. Following data preprocessing, feature engineering, and correlation analysis, the neural network-based stacking meta-model achieves 96.67% accuracy, surpassing individual base learners while delivering enhanced robustness through model diversity, improved probability calibration, and consistent performance on challenging intermediate condition classes, which are essential for condition prioritization. Age emerged as the most influential feature, followed by length, material type, and diameter. ROC-AUC scores ranged from 0.894 to 0.998 across all models and classes, confirming high discriminative capability. This work demonstrates hybrid architectures for infrastructure diagnostics. Full article
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