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Advances in Intelligent Operation, Monitoring, and Maintenance of Infrastructure

A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Civil Engineering".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 443

Editors


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Guest Editor
Department of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, ND 58102, USA
Interests: optical sensing; structural health monitoring; advanced coating materials; AI prediction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

At present, increasing attention is being directed toward the sustainable operation and long-term resilience of civil infrastructure systems, particularly in the context of aging assets, growing traffic demands, and exposure to harsh environments. Numerous studies highlight the need to move beyond traditional inspection and maintenance practices toward intelligent, data-driven approaches that integrate advanced sensing, real-time monitoring, and predictive analytics. To address these challenges, technologies such as distributed fiber optic sensing, wireless sensor networks, and digital twin frameworks are increasingly being adopted to enable continuous condition assessment and early damage detection. Despite the complexity of infrastructure behavior under coupled mechanical, environmental, and operational loads, significant progress can be achieved through the integration of high-fidelity sensing data with physics-based models and machine learning techniques. In parallel, the incorporation of automation, artificial intelligence, and smart decision-support systems offers new opportunities to optimize maintenance strategies, reduce lifecycle costs, and enhance system reliability and safety. Important considerations also include structural durability, material degradation mechanisms, and the interaction between infrastructure performance and external factors such as climate, traffic, and environmental exposure.

The main objective of this Special Issue is to bring together original contributions that present recent Advances in Intelligent Operation, Monitoring, and Maintenance of Infrastructure systems. We welcome experimental studies, field implementations, and numerical simulations that focus on innovative sensing technologies, data-driven modeling, structural health monitoring, and predictive maintenance strategies. Topics of interest include, but are not limited to, fiber optic sensing, digital twins, machine learning and artificial intelligence applications, corrosion and material degradation monitoring, smart transportation infrastructure, and resilient infrastructure systems under extreme conditions. Applications may span a wide range of infrastructure types, including bridges, pavements, pipelines, buildings, and energy systems. This Special Issue also encourages interdisciplinary research that integrates sensing, modeling, and decision-making to advance the next generation of intelligent infrastructure management systems.

Prof. Dr. Ying Huang
Dr. Luyang Xu
Guest Editors

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Keywords

  • intelligent infrastructure systems
  • structural health monitoring (SHM)
  • distributed fiber optic sensing (DFOS)
  • digital twin and data-driven modeling
  • predictive maintenance and AI analytics

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Published Papers (1 paper)

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Research

38 pages, 6149 KB  
Article
A Hybrid Experimental–Numerical Framework for Monitoring Bottom-Up Reflective Cracking in Asphalt-Overlaid PCC Pavements Using OFDR-Based Distributed Fiber Optic Sensing
by Yasir Mahmood, Luyang Xu, Dawei Zhang, Ying Huang, Pan Lu, Kathryn Quenette, Nof Yasir, Rouzbeh Ghabchi, Muhammad Ilyas, Junyi Duan and Chengcheng Tao
Appl. Sci. 2026, 16(17), 8573; https://doi.org/10.3390/app16178573 - 28 Aug 2026
Viewed by 209
Abstract
Reflective cracking is one of the primary causes of premature deterioration in asphalt-overlaid Portland cement concrete (PCC) pavements, reducing service life and increasing maintenance costs. Since crack initiation begins within the underlying PCC layer before becoming visible at the pavement surface, conventional inspection [...] Read more.
Reflective cracking is one of the primary causes of premature deterioration in asphalt-overlaid Portland cement concrete (PCC) pavements, reducing service life and increasing maintenance costs. Since crack initiation begins within the underlying PCC layer before becoming visible at the pavement surface, conventional inspection methods have limited capability for early damage detection and continuous monitoring. This study presents a hybrid experimental–numerical framework for monitoring and interpreting bottom-up reflective cracking by integrating Optical Frequency Domain Reflectometry (OFDR)-based Distributed Fiber-Optic Sensing (DFOS), laboratory-scale three-point bending tests, and finite element (ABAQUS) modeling. Rectangular and semi-cylindrical asphalt-overlaid PCC specimens were instrumented with surface-bonded distributed optical fibers arranged in a serpentine sensing layout with approximately 20 mm spacing to continuously monitor strain evolution during flexural loading. In both tested specimen configurations, the three-point bending tests produced bottom-up crack initiation at the predefined notch within the PCC layer, followed by crack propagation toward the asphalt overlay. Crack-width measurements showed that the maximum crack opening occurred near the notch and progressively decreased toward the asphalt overlay, consistent with the expected flexural stress distribution. The OFDR-based DFOS system successfully identified localized strain concentrations associated with crack initiation and propagation, demonstrating its capability for continuous distributed monitoring of fracture evolution. Finite-element simulations identified tensile stress and strain-localization patterns that showed good qualitative spatial correspondence with the experimentally observed cracking region and distributed strain measurements. The combined experimental, sensing, and numerical results demonstrate the proposed framework’s capability to monitor and interpret bottom-up reflective cracking and highlight the potential of OFDR-based distributed fiber-optic sensing for structural health monitoring, condition assessment, and future field-scale monitoring of rehabilitated concrete pavements. Full article
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