Fiber Bragg grating (FBG) sensors have been applied to assess strains, stresses, loads, corrosion, and temperature for structural health monitoring (SHM) of steel infrastructure, such as buildings, bridges, and pipelines. Since a single FBG sensor measures a particular parameter at a local spot, it is challenging to detect different types of anomalies and interactions of anomalies. This paper presents an approach to assess interactive anomalies caused by mechanical loading and corrosion on epoxy coated steel substrates using FBG sensors in real time. Experiments were performed by comparing the monitored center wavelength changes in the conditions with loading only, corrosion only, and simultaneous loading and corrosion. The theoretical and experimental results indicated that there were significant interactive influences between loading and corrosion for steel substrates. Loading accelerated the progress of corrosion for the epoxy coated steel substrate, especially when delamination in the epoxy coating was noticed. Through the real-time monitoring from the FBG sensors, the interactions between the anomalies induced by the loading and corrosion can be quantitatively evaluated through the corrosion depth and the loading contact length. These fundamental understandings of the interactions of different anomalies on steel structures can provide valuable information to engineers for better management of steel structures.

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.

Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement.

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.

Wire-arc coatings have received substantial attention for corrosion protection; however, poor bonding often leads to delamination, corrosion initiation, and costly re-coating of structural components. This review combines bibliometric mapping with a focused technical synthesis to clarify how bonding performance has been studied in wire-arc coatings. Specifically, publication trends, keyword co-occurrence networks, and country-level co-authorship maps are used to map the evolution of the field and position adhesion-related studies within the broader literature. The analysis of 762 wire-arc coating publications from Web of Science (among 13,314 thermal spray coating records) reveals that research is centered on microstructure, mechanical properties, and corrosion resistance, with growing links to wire-based additive manufacturing. Keyword co-occurrence networks demonstrate clear process–structure–property relationships, while country-level collaboration maps highlight the leadership of China, the USA, and Germany. Critical to note, only eight publications systematically investigate the combined effects of substrate roughness, coating thickness, and Zn-Al coating composition on bond strength—representing less than 0.01% of the thermal spray literature. This pronounced research gap underscores the novelty of the present review, which synthesizes existing knowledge on adhesion mechanisms, identifies key process parameters, and establishes a research agenda to optimize wire-arc coatings for infrastructure corrosion protection. The technical synthesis highlights that adhesion is governed by the coupled effects of surface preparation (roughness and topography), coating build-up (thickness), and spray conditions (e.g., standoff distance and substrate preheating), which together influence coating microstructure and failure modes. These findings provide a structured framework to guide parameter selection for durable coatings.