The organic coating of bridge steel girders is subjected to physical scratches, corrosion, and aging in natural weathering. The breakdown of the coating may cause serviceability and safety problems if left unnoticed. Conventional coating inspection is time-consuming and lacks information about the coating’s chemical integrity. A hyperspectral imaging method is proposed to detect the condition of steel coatings based on coating-responsive features in reflectance spectra. A field test was conducted on the real-world bridge, which shows obvious signs of degradation. The hyperspectral signature enables an assessment of the coating’s health and defect severity. The results indicated that the coating scratch can be effectively located in the domain of a hyperspectral image and the scratch depth can be determined by mapping a scratch depth indicator (SDI = R532 nm/R641 nm). Rust sources and products in steel girders can be identified by the unique spectral signatures in the VNIR range, and the rust stains (and thus stain areas) scattered on the coating can be pinpointed at pixel level by the chloride rust (CR) indicators >1.11 (CR = R733 nm/R841 nm). The chemical integrity of a topcoat is demonstrated by the short-wave infrared spectroscopy and the topcoat degradation can be evaluated by the decreased absorption at 8000 cm⁻¹ and 5850 cm⁻¹. Hyperspectral imaging enables faster and more reliable coating condition detection by the spectral features and provides an alternative for multi-object coating detection.

Structural health monitoring (SHM) is crucial for preventing and detecting corrosion, leaks, and other risks in reinforced concrete (RC) structures, ensuring environmental safety and structural integrity. Optical fiber sensors (OFS), particularly long-period fiber gratings (LPFG), have emerged as a promising method for SHM. Various LPFG sensors have been widely used in SHM due to their high sensitivity, durability, immunity to electromagnetic interference (EMI) and compact size. This review explores recent advancements in LPFG sensors and offers insights into their potential applications in SHM.

Remote sensing detection of natural gas leaks remains challenging when using ground vegetation stress to detect underground pipeline leaks. Other natural stressors may co-present and complicate gas leak detection. This study explores the feasibility of identifying and distinguishing gas-induced stress from other natural stresses by analyzing the hyperspectral reflectance of vegetation. The effectiveness of this discrimination is assessed across three distinct spectral ranges (VNIR, SWIR, and Full spectra). Greenhouse experiments subjected three plant species to controlled environmental stressors, including gas leakage, salinity impact, heavy-metal contamination, and drought exposure. Spectral curves obtained from the experiments underwent preprocessing techniques such as standard normal variate, first-order derivative, and second-order derivative. Principal component analysis was then employed to reduce dimensionality in the spectral feature space, facilitating input for linear/quadratic discriminant analysis (LDA/QDA) to identify and discriminate gas leaks. Results demonstrate an average accuracy of 80% in identifying gas-stressed plants from unstressed ones using LDA. Gas leakage can be discriminated from scenarios involving a single distracting stressor with an accuracy ranging from 76.4% to 84.6%, with drought treatment proving the most successful. Notably, first-order derivative processing of VNIR spectra yields the highest accuracy in gas leakage detection.

The organic coating of bridge steel girders is subjected to physical scratches, corrosion, and aging in natural weathering. The breakdown of the coating may cause serviceability and safety problems if left unnoticed. A hyperspectral imaging method is proposed to detect the condition of steel coatings based on coating-responsive features in reflectance spectra. A field test was conducted on the real-world bridge which shows obvious signs of degradation. The hyperspectral signature enables an assessment of the coating health and defect severity. The results indicated that the coating scratch can be effectively located in the domain of a hyperspectral image and the scratch depth can be determined by mapping a scratch depth indicator (SDI=R532nm / R641nm). Rust sources and products in steel girders can be identified by the unique spectral signatures and the rust stains (and thus stain areas) scattered on the coating can be pinpointed at pixel level by the corrosion rust indicators. The chemical integrity of a topcoat is demonstrated by the short-wave infrared spectroscopy and the topcoat degradation can be evaluated by the absorption at 8000 cm-1 and 5850 cm-1.

This review examines the integration of sensor data into probabilistic bridge reliability analysis, covering the workflow from sensor selection and data acquisition through preprocessing, computational Bayesian inference and reliability index β computation to remaining service life estimation and maintenance optimization. Sensor selection is treated as a reliability engineering decision, with sensing technologies matched to the target reliability variable and governing deterioration mechanism. Sensor imperfections inherent to long-term structural health monitoring, including drift, bias, noise, missing data, synchronization errors, and calibration uncertainty, are evaluated through their propagation along the reliability analysis chain. Statistical filtering, thermal compensation, anomaly detection, and missing data imputation are examined as essential preprocessing stages for reliable downstream inference. Computational Bayesian methods are organized into core posterior inference techniques, including Markov Chain Monte Carlo, Sequential Monte Carlo, variational inference, approximate Bayesian computation, and neural simulation-based inference, and supporting techniques, including surrogate-based approaches, deterministic filtering, asymptotic approximations, and hybrid Bayesian reliability frameworks. The synthesis indicates that sensor imperfections can propagate into reliability indices and RSL estimates, that preprocessing is reliability-critical, and that no single inference method is universally suitable for sensor-informed bridge reliability assessment. This propagation is demonstrated by the illustrative scenario, in which RSL effects followed the ordering drift > missing data > bias; however, this ordering is conditional on the assumed parameter values and monitoring conditions and does not represent a general ranking of sensor imperfections. A key gap concerns the sensor-to-reliability pathway, as monitoring observations do not directly determine β but require mapping to limit state variables with consideration of identifiability and spatial representativeness. Integrating these elements with RSL estimation, value-of-information analysis, and lifecycle maintenance optimization supports evidence-based bridge management while highlighting challenges in sensor quality, model adequacy, uncertainty propagation, and computational implementation.