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Sustainable and Resilient Civil Infrastructure with Intelligence and Digital Transformation

Submission Deadline: 01 June 2026 (closed) View: 1126 Submit to Special Issue

Guest Editor(s)

Dr. Zhenkun Li

Email: zhenkun.li@aalto.fi

Affiliation: Department of Civil Engineering, Aalto University, Espoo 02150, Finland

Homepage:

Research Interests: structural health monitoring, resilience of infrastructures, artificial intelligence in engineering

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Dr. Bozhou Zhuang

Email: bzhuang31@gatech.edu

Affiliation: School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

Homepage:

Research Interests: intelligent structural design, structural health monitoring (SHM), machine/deep learning, generative AI, non-destructive evaluation, nuclear energy structures

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Assist. Prof. Liangfu Ge

Email: liangfu.ge@polyu.edu.hk

Affiliation: Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong

Homepage:

Research Interests: structural health monitoring, structural damage detection, traffic load monitoring, digital twins, automated urban scanning

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Assist. Prof. Kun Feng

Email: kun.feng@aru.ac.uk

Affiliation: School of Engineering and the Built Environment, Anglia Ruskin University, Peterborough PE1 5BW, UK

Homepage:

Research Interests: structural health monitoring, vehicle bridge interaction, signal processing, machine learning

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Summary

Facing growing challenges such as climate change, aging assets, and rapid urbanization, the sustainability and resilience of civil infrastructure have become global priorities. Ensuring long-term performance and safety now depends on integrating advanced sensing technologies, intelligent materials, and data-driven approaches grounded in digital transformation. Recent advances in structural health monitoring (SHM), non-destructive testing (NDT), artificial intelligence (AI)-driven analytics, and digital twins are reshaping how infrastructure is assessed and maintained. Meanwhile, smart materials such as self-sensing concretes are enabling real-time responsiveness and adaptability. This Special Issue invites interdisciplinary contributions that explore innovative, intelligent, and digital methods to enhance the sustainability, resilience, and efficiency of civil infrastructure systems.

Topics of interest include but are not limited to:
· Recent advancements in SHM systems, sensors, and networked monitoring frameworks
· AI-based condition assessment, damage detection, and life-cycle prediction
· NDT techniques for sustainable infrastructure inspection
· Digital twin and building information modelling (BIM) integration for infrastructure modelling and maintenance
· Intelligent and functional materials with self-sensing or adaptive capabilities
· Signal processing, data fusion, and cloud-based SHM platforms
· Resilient design strategies and performance-based approaches
· Sustainable material use, recycling, and digital tools for green construction
· Infrastructure resilience to climate-induced stresses and environmental hazards
· Field case studies demonstrating practical deployment of digital and intelligent technologies

Keywords

structural health monitoring, digital transformation, resilient infrastructure

Published Papers

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Open Access

ARTICLE

An Integrated Simulation Framework for Reliability Assessment of Bolted Joints in Lightweight High-Speed Train Battery Housings
Lin Zang, Muhammad Fadhil Marsani, Caiyan Long, Yongping Yu
Structural Durability & Health Monitoring, Vol.20, No.4, 2026, DOI:10.32604/sdhm.2026.080465
（This article belongs to the Special Issue: Sustainable and Resilient Civil Infrastructure with Intelligence and Digital Transformation)
Abstract To bridge the gap between isolated structural and connection analyses in lightweight rail vehicle design, this study establishes an integrated modelling-simulation-verification framework. This integrated framework, developed via parametric FE modelling, multi-axial load simulation (per DIN EN 12663 & BS 7608), and standards-based verification, was applied to a specific housing design. Key results: (1) Under vertical ±3 g static loads, the tray experiences the maximum equivalent stress of 186.6 MPa, yielding a safety factor of 1.1 against the yield strength, which satisfies the minimum requirement (s₁ ≥ 1.15) of the DIN EN 12663 standard. (2) Under fatigue More >

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ARTICLE

Deep Learning-Based Structural Displacement Identification and Quantification under Target Feature Loss
Lishuai Zhu, Guangcai Zhang, Qun Xie, Zhen Peng, Li Ai, Ruijun Liang, Taochun Yang
Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.074620
（This article belongs to the Special Issue: Sustainable and Resilient Civil Infrastructure with Intelligence and Digital Transformation)
Abstract Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features, making it difficult for traditional methods to ensure high accuracy and robustness. Therefore, this study proposes a structural displacement identification and quantification method that integrates YOLOv8n with an improved edge-orientation gradient-based template matching algorithm. By combining deep learning techniques with traditional template matching methods, the accuracy and robustness of monitoring are enhanced under adverse conditions such as noise and extremely low illumination. Specifically, in the edge-orientation gradient matching stage, the Canny-Devernay sub-pixel edge detection technique and… More >

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ARTICLE

A Robust Damage Identification Method Based on Modified Holistic Swarm Optimization Algorithm and Hybrid Objective Function
Xiansong Xie, Xiaoqian Qian
Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.074148
（This article belongs to the Special Issue: Sustainable and Resilient Civil Infrastructure with Intelligence and Digital Transformation)
Abstract Correlation function of acceleration responses-based damage identification methods has been developed and employed, while they still face the difficulty in identifying local or minor structural damages. To deal with this issue, a robust structural damage identification method is developed, integrating a modified holistic swarm optimization (MHSO) algorithm with a hybrid objective function. The MHSO is developed by combining Hammersley sequence-based population initialization, chaotic search around the worst solution, and Hooke-Jeeves pattern search around the best solution, thereby improving both global exploration and local exploitation capabilities. A hybrid objective function is constructed by merging acceleration correlation… More >

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Sustainable and Resilient Civil Infrastructure with Intelligence and Digital Transformation

Guest Editor(s)

Summary

Keywords

Published Papers

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371

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92

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1523

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465

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593

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