Intelligent Materials and Structural Health: Mechanics, Damage Detection, Performance Enhancement, and AI-Driven Techniques

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 1 May 2026 | Viewed by 600

Special Issue Editors


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Jiangsu Key Laboratory of Environmental Impact and Structural Safety in Engineering, School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: multi-hazard risk assessment and resilience for engineering structures
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Guest Editor
School of Materials and Chemical Engineering, Xuzhou University of Technology, Xuzhou 221116, China
Interests: research on the mechanical mechanisms of smart materials

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Guest Editor
School of Transportation and Environment, Shenzhen Institute of Information Technology, Shenzhen 518109, China
Interests: structural health monitoring

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Guest Editor
School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: modular steel structure and seismic resilience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of intelligent materials and advanced structural health monitoring systems has revolutionized the field of civil and mechanical engineering, offering unprecedented opportunities to address aging infrastructure, environmental challenges, and extreme loading conditions. The convergence of smart material mechanics, high-precision damage detection, and AI-driven predictive technologies is critical for enhancing structural resilience, optimizing performance, and ensuring long-term sustainability. To advance this interdisciplinary frontier, we are pleased to announce this Special Issue, ‘Intelligent Materials and Structural Health: Mechanics, Damage Detection, Performance Enhancement, and AI-Driven Techniques’.

This Special Issue aims to compile cutting-edge research and innovative methodologies that bridge material intelligence, structural diagnostics, and computational advancements. We invite contributions addressing theoretical, experimental, and applied studies focused on intelligent material systems, damage-sensitive analytics, and AI-enhanced structural optimization. Topics of interest include, but are not limited to, the following:         

(1) Mechanical and multifunctional behavior of smart materials;
(2) Advanced sensor networks and IoT-enabled structural health monitoring;
(3) Machine learning/AI-driven damage identification and prognosis;
(4) Performance assessment and enhancement strategies;
(5) Digital twin frameworks for real-time structural integrity assessment;
(6) Multiscale modeling of material degradation and structural failure mechanisms;
(7) Energy-efficient materials for resilient and sustainable infrastructure;
(8) Autonomous damage detection via computer vision and robotics;
(9) AI-aided optimization of structural retrofitting and rehabilitation;
(10) Case studies on AI-integrated smart infrastructure systems.

By fostering collaboration across material science, mechanics, and data science, this Special Issue seeks to redefine the future of intelligent infrastructure and inspire next-generation solutions for global engineering challenges.

Dr. Xiaowei Zheng
Dr. Yao Zhang
Dr. Rumian Zhong
Dr. Qinglin Wang
Guest Editors

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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. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • smart materials
  • structural health monitoring damage detection
  • AI-driven mechanics
  • multiscale modeling
  • digital twin technology
  • self-healing polymers
  • resilient infrastructure
  • machine learning in structural engineering
  • sustainable material systems

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Published Papers (2 papers)

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Research

20 pages, 3583 KiB  
Article
Bridge Cable Performance Warning Method Based on Temperature and Displacement Monitoring Data
by Yan Shi, Yan Wang, Lu-Nan Wang, Wei-Nan Wang and Tao-Yuan Yang
Buildings 2025, 15(13), 2342; https://doi.org/10.3390/buildings15132342 - 3 Jul 2025
Viewed by 209
Abstract
Cable-stayed bridge cables experience significant tension over time, making the bridge cables prone to corrosion and fatigue. The direct measurement of cable length is not a standard capability in most current structural health monitoring systems, nor is long-term monitoring of cable changes. Bridge [...] Read more.
Cable-stayed bridge cables experience significant tension over time, making the bridge cables prone to corrosion and fatigue. The direct measurement of cable length is not a standard capability in most current structural health monitoring systems, nor is long-term monitoring of cable changes. Bridge displacements are caused by both dynamic loads (wind and traffic) and quasi-static factors, primarily temperature. This study filtered out dynamic responses by the three-sigma rule, multiple linear regression, interpolation method, and not-a-number calibration. Monitoring data were used to analyze the bridge’s thermal field distribution and the time-dependent variation of tower displacements. Correlation analysis revealed a strong linear correlation between air temperature and quasi-static tower-girder displacements. This research proposes to use the tower-girder distance (effective cable length) to represent the length of the cable, take the thermal expansion coefficient of the effective length of the cable as the quantitative index for long-term monitoring, and take its error as the performance early warning indicator. This method effectively monitors cable health and provides damage warnings. Full article
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16 pages, 1841 KiB  
Article
Fatigue Damage Prognosis Method for Main Girders of Cable-Stayed Bridges Based on Wavelet Neural Network
by Shan Huang, Rui Chen, Jun Ling and Nan Jin
Buildings 2025, 15(13), 2232; https://doi.org/10.3390/buildings15132232 - 25 Jun 2025
Viewed by 244
Abstract
At present, the research on bridge structure health monitoring mainly focuses on discovering existing structural damage and less on predicting when the damage will occur in the future. This paper proposes a fatigue damage prognosis method for the main girders of cable-stayed bridges [...] Read more.
At present, the research on bridge structure health monitoring mainly focuses on discovering existing structural damage and less on predicting when the damage will occur in the future. This paper proposes a fatigue damage prognosis method for the main girders of cable-stayed bridges based on wavelet neural networks (WNNs). This method integrates WNN with multi-scale finite element modeling to predict fatigue damage progression. First, the theoretical foundation and implementation algorithms of the WNN are elaborated on and applied to forecast the future load environments of cable-stayed bridges. Subsequently, multi-scale finite element models are employed to derive stress influence lines for critical fatigue-prone regions in the main girder of the cable-stayed bridge. Finally, fatigue reliability methods are utilized to predict the fatigue reliability indices, service life, and failure probabilities of critical fatigue details. The proposed prognosis method in this paper can accurately predict the future operation conditions and remaining service life of bridge structures so as to provide a more reasonable maintenance strategy for bridge structures. Full article
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