A Review of Machine Learning-Based Methods for Concrete Crack Detection

Accurate detection of cracks in concrete structures is essential for ensuring civil engineering safety. Traditional manual inspection and machine vision methods suffer from high subjectivity, low efficiency, and poor adaptability to complex environments. In recent years, machine learning (ML) has significantly improved the precision and automation of crack detection. This review provides a systematic examination of ML-based crack detection through a unique four-stage pipeline—data acquisition, preprocessing, model training, and evaluation. Additionally, the integration of multimodal data and unmanned aerial vehicle (UAV)-based inspection has expanded the scope and applicability of detection technologies. Despite these advancements, challenges remain, including limited accuracy for fine crack detection, immature dynamic monitoring techniques, and lack of industry standards. Future research should focus on optimizing model design, incorporating temporal analysis and uncertainty quantification, and developing intelligent structural health assessment systems. This review offers a technical reference for applying machine learning in crack detection and highlights future directions for interdisciplinary integration and standardization.