Review of Crack Depth Detection Technology for Engineering Structures: From Physical Principles to Artificial Intelligence

Ensuring the structural safety of components or facilities is essential for the smooth operation of industrial production and transportation. As a key index to evaluate structural health, the crack depth detection method has evolved from the early single physical field detection to the contemporary multi-physical field collaborative artificial intelligence algorithm. This paper presents a systematic review of crack depth detection technology under specific engineering conditions, such as those found in roads, train tracks, and engine blades. The framework categorizes and reviews detection technology according to detection principles, including physical principles, model inversion, hybrid methods, and evaluation indicators such as detection accuracy, speed, and cost. The paper compares various detection technologies, highlighting their advantages and limitations in real-world applications. The analysis reveals key challenges, which include complex environmental interference, the detection of microcracks and deep cracks, and the balance between accuracy and cost. Addressing these challenges is imperative to improving the reliability and generalization of detection technology. This paper proposes future research directions focusing on integrating multi-physical field detection with artificial intelligence, utilizing AI’s robust capabilities to develop more advanced methods for detecting crack depth.