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.

Bridge health monitoring is crucial for ensuring the safety and durability of infrastructure. In traditional methods, physics-based models have high interpretability but are difficult to handle complex nonlinear problems, while purely data-driven machine learning methods are limited by data scarcity and physical inconsistency. Physics-informed machine learning, as an emerging “gray box” paradigm, effectively integrates the advantages of both by embedding physical laws (such as control equations) into machine learning models in the form of constraints, priors, or residuals. This article systematically elaborates on the core fusion mechanism of physics-informed machine learning (PIML) in bridge engineering, innovative applications throughout the entire lifecycle of design, construction, operation, and maintenance, as well as its unique data augmentation strategy. Research has shown that PIML can significantly improve the accuracy and robustness of damage identification, load inversion, and performance prediction, and is the core engine for constructing dynamic and predictive digital twin systems. Despite facing challenges in complex physical modeling, loss function balancing, and engineering interpretability, PIML represents a fundamental shift in bridge health monitoring towards intelligent and predictive maintenance by combining advanced strategies such as active learning and meta learning with IoT technology.

Due to the large geometric size and the influence of dense embedded steel bars, It is difficult to use the traditional nondestructive testing (NDT) method for evaluation the inner defects of mass concrete. A comprehensive nondestructive testing method for internal defects of mass concrete was proposed, which used elastic wave Computed Tomography (CT) scanning for general survey, ultrasonic tomography and ground penetrating radar (GPR) technology for fine investigation. The method was applied to the defect detection of a bearing platform concrete belonging to an interchange bridge. Finally, the reliability of this method was verified by the results of core drilling. The results show that the proposed method can effectively detect the distribution of defects in concrete, provided a new solution and practical guidance for the internal defect detection and reinforcement of mass concrete structure.

A new type of static load test system and test method based on steel screw anchor pile is proposed. Through field uplift tests of single pile and pile group, the influence of soil properties, pile length and other factors on the uplift bearing capacity of steel screw pile is analyzed. The influence range of steel screw pile on the displacement of adjacent soil and pile foundation are revealed. The calculation method of ultimate uplift bearing capacity of single steel screw anchor pile is established. The reliability of the test method is verified by practical cases. The test results show that the uplift bearing capacity of steel screw pile is higher than that of straight rod pile. According to the properties and compactness of the soil, the coefficient of uplift side resistance improvement of steel screw piles can be taken as 1.2 – 1.6. The construction of steel screw pile is convenient and can be reused, without special treatment of the test site. The test method can save up to 70% of the cost compared with traditional surcharge test method, and greatly improve the safety of large-tonnage static load test.