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 effect of fluidized bed combustion ashes (FBC) and cement by monodoping and compound doping on the mechanical properties and water resistance of phosphogypsum composite cementitious materials was studied, and the mechanism was explored by SEM. The results showed that the cement and fluidized bed combustion ashes by monodoping have a negative effect on the compressive strength of phosphogypsum, but can effectively improve its softening coefficient. When cement and fluidized bed combustion ashes mixed together, the softening coefficient increased with the increase of cement from 0 to 10%. But the softening coefficient decreased with the increase of cement from 0 to 20%, however, the compressive strength increased. Through the ANOVA analysis, the optimal dosage of cement, fluidized bed combustion ashes and phosphogypsum was obtained. Through the SEM, the hydration products covered on the surface of gypsum crystal, increased the compactness of the hardened body.