In recent years, intelligent technologies represented by the Internet of Things (IoT), artificial intelligence (AI), and digital twins have brought revolutionary breakthroughs to the operation and maintenance of urban rail underground engineering. The resilience theory has promoted the transformation of engineering from “passive disaster prevention” to “active disaster adaptation”. Meanwhile, innovation in materials and rapid-repair technologies have enhanced the disaster resistance and recovery capabilities of structures. However, current research still faces issues such as fragmented technologies, theoretical developments lagging behind practical needs, and incomplete standard systems.

The special issue is planned to gather the latest research results, promote interdisciplinary integration, and advance two critical industry objectives: first, to build an integrated “perception-diagnosis-decision” intelligent operation and maintenance system, breaking through the technical barriers in structural health diagnosis under complex environments; second, to develop promotable engineering paradigms and technical standards, facilitating the transformation of scientific research achievements into practical productivity. The special issue will establish an exchange platform for academia and engineering sections, providing theoretical foundations and practical references for the safe, intelligent, and sustainable development of urban rail underground engineering.

I. Themes of the Special Issue

This special issue focuses on intelligent diagnosis and governance technologies for urban rail underground engineering structures, covering theoretical research, technological innovation, and practical applications. Key areas include (but are not limited to):

1. Intelligent Inspection and Monitoring Technologies

o Non-destructive testing technologies based on IoT, UAVs, and robots;

o Multi-source heterogeneous data fusion and real-time monitoring methods;

o Structural state-sensing sensors and edge computing technologies.

2. Structural Pathology Mechanisms and Performance Assessment

o Mechanisms of material deterioration and structural damage evolution in complex environments;

o Structural safety assessment methods based on big data and machine learning;

o Models for predicting remaining structural service life and quantifying risks.

3. Intelligent Diagnosis and Decision-Making Systems

o Digital twin-driven structural health management systems;

o AI-driven algorithms for pathology identification and diagnosis;

o Development and application of intelligent operation and maintenance decision support platforms.

4. Structural Repair and Reinforcement Technologies

o New materials, methods, and processes for structural repair and reinforcement.

5. Engineering Practices and Innovative Cases

o Case studies on intelligent diagnosis of typical urban underground engineering;

o Application effects of repair and reinforcement technologies in practical engineering.

II. Submission Requirements

1. Manuscripts must be original research results that have not been published in other journals or conferences.

2. Content should be academically innovative and of engineering practical value, with clear logic and robust data.

3. Manuscripts should follow the formatting guidelines of Urban Rapid Rail Transit (available for download on the official website).

4. Please submit manuscripts via the journal’s official website (http://www.urt.cn/). Click “中文刊投稿” (Chinese Journal Submission) to enter the editorial system of Urban Rapid Rail Transit, then click “作者投稿” (Author Center). Please select the “智能诊断与治理” (Intelligent Diagnosis and Governance) special issue column during submission, and indicate “Special Issue Submission + Paper Title” in the email subject.

5. Deadline for submission: September 30, 2025.

III. Guest Editors of the Special Issue

• Tang Chao, Professor-level Engineer/Researcher, Deputy Chief Engineer, Beijing Urban Construction Exploration & Surveying Design Research Institute Co., Ltd.

E-mail: tangchao0312@126.com

• Bao Yan, Professor, Deputy Director, Institute of Intelligent Construction and Engineering Management, Beijing University of Technology

E-mail: baoy@bjut.edu.cn

• Wei Yun, Professor-level Senior Engineer, Chief Expert and Deputy Chief Engineer, Beijing MTR Corporation Limited; President, Beijing Metro Technology Innovation Research Institute

E-mail: luckyboy0309@163.com