Structural health monitoring (SHM) plays an increasingly important role in managing aging, safety-critical infrastructure under growing environmental and operational demands. In recent years, fiber-optic sensors (FOSs) have attracted significant attention for SHM applications due to their immunity to electromagnetic interference, durability in harsh environments, multiplexing capability, and suitability for both localized and fully distributed measurements. In parallel, advances in machine learning (ML) have enabled new approaches for extracting actionable insights from large, high-dimensional sensing datasets. This paper presents a systematic review of FOS-based SHM systems integrated with ML across civil, transportation, energy, marine, and aerospace infrastructures. Following PRISMA 2020 guidelines, peer-reviewed studies were identified and synthesized to examine sensing principles, deployment configurations, data characteristics, and learning-based analytical strategies. Fiber optic technologies are categorized into point-based, quasi-distributed, and fully distributed systems, and their capabilities for capturing strain, temperature, and spatiotemporal structural responses are critically evaluated. ML approaches are examined from a task-oriented perspective, including damage detection, localization, severity assessment, environmental compensation, and prognosis, with emphasis on the alignment between sensing configurations and appropriate learning paradigms. Key challenges remain, particularly regarding large data volumes, environmental variability, limited labeled damage datasets, model generalization, and system-level integration. Emerging directions such as physics-informed and hybrid learning, transfer learning, uncertainty-aware modeling, and integration with digital twins are discussed as pathways toward more robust and scalable SHM systems. By jointly addressing sensing physics and data-driven intelligence, this review provides a structured reference and practical roadmap for advancing intelligent FOS-based SHM in next-generation infrastructure.

Climate change significantly impacts transportation infrastructure, particularly asphalt pavements. Similarly, the heat absorption of paved surfaces, especially conventional black pavements, significantly intensifies the urban microclimate. Paved surfaces, including asphalt pavements, account for over 30% of the covered surfaces and are vulnerable to rising temperatures, which cause not only pavement distress, such as rutting and cracking, but also urban heat islands (UHI). Sustainable pavement solutions, specifically colored pavements, have been investigated for their potential to mitigate these effects. This review presents an extensive overview of current pavement technologies, emphasizing conventional asphalt’s economic, environmental, and functional characteristics. A discussion of the benefits and challenges of colored pavements is also provided, including their ability to reduce UHI, enhance safety, and contribute to sustainable urban growth. This paper discusses advancements in pavement material science, the use of recycled materials, and the application of reflective coatings, providing insights into sustainable infrastructure development. Transitioning from conventional black pavements to sustainable colored alternatives is not merely a matter of material choice but a strategic transition toward resilient urban planning. Increasing demand for environmentally friendly infrastructure could prompt the construction industry to adopt colored pavements as a tool to promote environmental stewardship.

Power grid infrastructures, essential to modern societies for electricity distribution, are prone to vulnerabilities due to their numerous sensitive components, necessitating a comprehensive risk assessment. Uncertainty in historical failure data often compromises accurate risk quantification, leading to the integration of expert elicitation as a solution. This study develops a Bayesian network (BN) risk assessment model integrated with fuzzy set theory (FST), referred to as the fuzzy Bayesian network (FBN). By incorporating expert insights, this model quantifies internal and external risk variables more comprehensively. Crisp probabilities (CPr), derived from regional transmission operator (RTO) failure incident data, are complemented by fuzzy probabilities (FPr) from expert elicitation. The findings indicate that equipment conditions, specifically transmission lines and circuit breakers, are critical threats to power grids. Environmental factors, particularly storms, emerge as vulnerability risks. A comparison of results using both CPr plus FPr versus FPr alone underscores the utility of expert elicitation in risk assessment. This research demonstrates the effectiveness of FBNs through expert elicitation, providing a comprehensive and accurate framework for power grid risk assessment. To improve risk evaluation in critical infrastructure, integrated data collection techniques are recommended.

To reduce carbon emissions from fossil fuel generators in sustainable energy systems, an option is increasing the integration of gas-fired generators into the power system. The increasing reliance on natural gas for electricity generation has strengthened the interdependence between the electric power network and the natural gas infrastructure within the Integrated Power and Gas System (IPGS). This strengthened interdependence increases the risk that disruptions originating in one system may propagate to the other, potentially leading to extensive cascading failures throughout the IPGS. Ensuring the reliability of critical energy infrastructure is vital for sustainable development. This paper proposes a vulnerability assessment method for the IPGS using an influence graph, which can be formulated based on fault chain theory to capture the interactions among failed components in the IPGS. With the influence graph, eigenvector centrality is used to pinpoint the critical components in the IPGS. The proposed methodology is validated using 39-bus 29-node IPGS through the Scenario Analysis Interface for Energy Systems (SAInt) software version 3.5.17.7. Results show that the proposed method has effectively identified the most critical branches in the IPGS, which play a key role in initiating cascading failures. These insights contribute to enhancing the resilience and sustainability of interconnected energy systems.

Accurate weigh-in-motion (WIM) systems are critical for effective transportation network planning, pavement asset management, and axle-load enforcement. However, conventional surface-mounted or cut-in WIM sensors often suffer from poor durability, temperature sensitivity, and limited awareness of lateral wheel offset. This study presents an embedded WIM approach that integrates glass fiber-reinforced polymer-packaged fiber Bragg grating (FBG) sensors with physics-based simulation and data-driven estimation. Six FBG sensors were installed at two depths (2.5” and 4.5”) within the flexible pavement to enhance strain transfer efficiency and capture multilayer mechanical responses. Vertical strain fields under varying wheel loads and transverse offsets were modeled in KENPAVE, and corresponding Bragg wavelength shifts were correlated with radial distance r from the wheel path. A Gaussian inverse model was developed to estimate the lateral wheel offset and compensate for wander-induced variations before load inference. Field validation was conducted using data from nineteen trucks at a North Dakota weigh station, where Bragg shifts and independently verified axle weights were collected for calibration and testing. Among several machine-learning regressors trained on extracted features, XGBoost achieved the highest accuracy, yielding a mean absolute percentage error of 9.9%. These results demonstrate the field-scale proof-of-concept feasibility of the integrated sensing–simulation–machine learning framework under live traffic conditions. The proposed system offers a non-intrusive, robust, and wander-resilient solution, establishing a methodological foundation for scalable high-level WIM implementation, subject to expanded statistical validation and controlled certification testing.

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.