As urbanization accelerates, aging infrastructure demands more advanced inspection methods for structural health monitoring. The growing integration of artificial intelligence (AI) and computer vision technologies has significantly enhanced damage detection accuracy while simultaneously reducing inspection time and operational costs. Despite these advantages, the adoption of AI-based technologies in infrastructure maintenance remains limited due to challenges related to data. One major issue is the lack of comprehensive, task-specific annotated datasets. Another is the poor quality of images captured by drones or mobile devices, which are often affected by noise, blurring, and inconsistent lighting. Although recent advances in generative AI offer promising support for structural health monitoring, it remains unclear which models are best suited for specific tasks. This study examines the use of generative AI in structural health monitoring, focusing on key challenges such as limited datasets and low-quality image restoration. The review covers a range of generative AI technologies, outlining their principles, strengths, limitations, and representative applications to support the selection of appropriate tools for specific tasks. Generative AI models enable accurate image segmentation and structural anomaly detection using limited training data. The paper also explores new opportunities for integrating multi-modal generative AI to enhance human–computer interaction in support of structural health monitoring. A framework is proposed to streamline the use of generative AI technologies for data augmentation, image restoration, damage inspection, and human–computer interaction in structural health monitoring.

The design of FRP profile-concrete composite sections, including beams and decks, is usually governed by the shear strength of the FRP profiles. However, analytical methods that can precisely predict the shear capacity of the composite sections have not been well developed, because there is lack of knowledge of the FRP-concrete composite action and distribution of shear stress along the FRP. This paper investigates the shear behaviors of FRP-concrete composite sections and develops formulae to predict the shear capacity of the composite sections. First, flexural tests of three FRP-concrete composite beams were conducted to investigate the shear failure mode and interface behaviors. All the beams failed in FRP shear fracture along horizontal direction. Then, push-out tests were used to determine the slip property for the FRP-concrete interface which reveals that FRP stay-in-place form and steel bolts can ensure full and partial composite action, respectively. Based on the experimental study, closed-form equations to compute the maximum shear stress are derived and validated against experimental data in this paper and literature. Finally, simple yet reliable equations of shear capacity are derived and recommended for engineers to design the FRP-concrete composite sections.