High-strength bolted connections are fundamental load-bearing components in critical engineering infrastructures such as wind turbines, bridges, and heavy machinery. Under complex service environments involving dynamic loading, vibration, corrosion, and temperature variations, bolts are prone to interacting failure mechanisms, including fatigue fracture, corrosion-assisted cracking, hydrogen embrittlement, and progressive preload loss, which pose significant challenges for reliable condition monitoring and early fault diagnosis. This review provides a structured synthesis of recent advances in bolt health monitoring and intelligent fault diagnosis. A unified framework is established to link multi-physics failure mechanisms with multi-modal sensing technologies and data-driven diagnostic methods. Key sensing approaches—such as piezoelectric impedance techniques, ultrasonic phased array inspection, and computer vision-based monitoring—are critically reviewed in terms of their physical principles, diagnostic capabilities, and limitations. Furthermore, the transition from traditional model-based and signal-processing-driven methods to machine learning- and deep learning-based approaches is examined, with emphasis on multi-modal data fusion, real-time monitoring, and lifecycle-oriented health management enabled by IoT and digital twin technologies. Finally, key challenges and future research directions toward robust and scalable intelligent bolt health management systems are outlined. This review’s primary contribution lies in establishing a novel, integrated framework that links failure physics to sensing and diagnosis, thereby providing a structured roadmap for transitioning from isolated component monitoring to lifecycle-oriented, intelligent health management systems for critical bolted connections.

The existence of an interlayer has a significant effect on the stability of a rock salt gas storage cavity; therefore, an uncontrollable collapse of the interlayer would cause a series of issues. In this study, three types of mechanical instability criteria are comprehensively calculated. The limit radius of the interlayer is computed under different criteria, and the collapse radius of the interlayer is obtained by comparison. The calculation results of the mathematical model are highly accurate with respect to actual engineering logging data, in general with over 90% of accuracy. It is demonstrated that, besides the physical and mechanical characteristics of the sandwich, the location of the interlayer in the cavity and concentration of the brine also have an important effect on the collapse of the interlayer. The brine at the bottom of the cavity is nearly saturated. Therefore, an interlayer at this location does not easily collapse. The mathematical model established in this study is used in the seismic design and prediction of interlayer collapse during the construction of salt‐cavern gas storage facilities in China.

Under impact loading, the performance of ordinary waste glass powder concrete is lower than that of ordinary concrete. To enhance the impact resistance of waste glass powder concrete, renewable polyvinyl alcohol (PVA) fibers are introduced. Considering the effects of PVA fiber volume content (0.4%, 0.8%, 1.2%) and fiber length (6 mm, 12 mm, 18 mm), the basic mechanical properties and impact resistance of PVA-fiber-reinforced waste glass powder concrete are investigated through compression tests, flexural tests, and drop hammer impact tests. The results show that the incorporation of PVA fibers can effectively improve the impact resistance of waste glass powder concrete, and both the compressive and flexural strength are enhanced to varying degrees. The experimental results show that when the fiber volume content is 0.8% and the length is 12 mm, the flexural strength of the sample is the best. In addition, the impact of fiber length on performance varies by index; shorter fibers (6 mm) perform more prominently in enhancing impact toughness, while longer fibers (12 mm) are more conducive to the growth of flexural strength. There is a positive correlation between the impact resistance of the specimens and the fiber content; as the content of PVA fibers increases, both the initial crack impact number and the failure impact number show an increasing trend, and the energy absorbed by the concrete during initial cracking and failure gradually increases. The highest impact energy dissipation is achieved with the incorporation of 1.2% 6 mm fibers. Based on the experimental results, a functional relationship between fiber length, fiber content, and impact energy absorption is proposed for PVA-fiber-reinforced waste glass powder concrete.

Deep learning has shown promise for vibration-based damage detection, yet its practical deployment is hindered by data scarcity, environmental sensitivity, and limited interpretability. To address these issues, we propose a dual-stream framework that explicitly embeds fractional-order and multifractal physical priors. The Grünwald–Letnikov fractional derivative (ν = 1.3) sharpens damage-related singularities, while multifractal detrended fluctuation analysis (MF-DFA) produces spatial maps of multiscale complexity across the sensor array. A temporal stream (1D-CNN + BiLSTM) processes the fractional-order enhanced signals, and a spatial stream (2D-CNN) processes MF-DFA maps derived from the same enhanced signals. Under the evaluation protocol adopted in this study, the framework achieves 97.3% accuracy on Z24 and 94.8% in the independent within-dataset KW51 experiment. In a separate cross-bridge experiment, the Z24-pretrained model obtains 86.7% accuracy on KW51 without target-domain updating and 94.6% after fine-tuning on 10% of the target data.

Structural damage identification under environmental and operational variations (EOVs) remains a major challenge in civil infrastructure health monitoring because environmental shifts can be statistically correlated with damage-sensitive vibration features. This study presents a physics-informed spatiotemporal graph neural network with domain-adversarial feature disentanglement (CP-STGNN). The framework models the sensor network as a physics-guided graph, extracts spatiotemporal features with Chebyshev graph convolution and temporal convolution, suppresses environmental-domain information in the damage representation through gradient-reversal-based adversarial training, and regularizes signal reconstruction using structural dynamics. The term “causal disentanglement” is used here only as a design motivation for reducing environmental confounding; the implemented objective is domain-adversarial representation learning and does not constitute formal causal identification. The framework is evaluated on the Z-24 Bridge, KW51 Railway Bridge, and LANL three-story structure benchmarks. In the Z-24 cross-domain experiment, CP-STGNN attains an F1-score of 0.97 under the reported protocol, while the additional benchmark studies indicate improved robustness to operational variability and nonlinear response patterns. The results support the value of combining structural topology, physics-based regularization, and domain-invariant representation learning, while the applicability of the approach remains dependent on sensor configuration, reference structural information, and the representativeness of the training domains.