Abstract
Concrete structures face significant challenges related to crack formation and structural deterioration. Automatedcrack detection and structural health monitoring (SHM) have emerged as critical areas of research to ensurethelongevity and safety of infrastructure. This systematic review examines the integration of artificial intelligence(AI)and machine learning (ML) techniques for automated crack detection and SHM in concrete structures. Thepapersynthesizes findings from 200+ research articles, focusing on deep learning methodologies, data acquisitionstrategies, sensor technologies, and real-world applications. Key challenges identified include data scarcity, modelgeneralization, computational efficiency, and practical deployment barriers. This review provides a comprehensiveoverview of current AI-driven approaches, including convolutional neural networks (CNNs), recurrent neuralnetworks (RNNs), and hybrid architectures, while highlighting gaps in existing research and proposingfutureresearch directions for developing robust, scalable, and cost-effective automated monitoring systems.
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