Revolutionizing concrete analysis: An in-depth survey of AI-powered insights with image-centric approaches on comprehensive quality control, advanced crack detection and concrete property exploration

Abstract

Over the last two decades, the integration of big data and deep learning technologies has demonstrated remarkable effectiveness across various domains of civil engineering, leading to significant advancements in machine learning methodologies. Regardless of significant progress in various aspects of civil engineering, there appears to be a striking absence of thorough and all-encompassing evaluations that adequately cover critical domains such as structural damage identification (SDI), accurate prediction of concrete properties, precise crack detection, detailed analysis of crack properties and thorough non-destructive testing (NDT) methods applied specifically to concrete structures. This gap in the existing literatures hinders a comprehensive understanding and application of advanced techniques in these crucial areas. To bridge this knowledge gap, this paper offers an all-encompassing exploration of the recent developments and utilizations of artificial intelligence in civil engineering. Emphasizing concrete-centric structures, this study prioritizes two crucial facets: the automatize detection of concrete specific mechanical attributes and crack detection. To achieve this numerous recent surveys have utilized intelligent algorithms primarily grounded in image statistics, coupled with extensive data analytics and deep learning methodologies. By delving into this research, the paper sheds light on the trajectory and challenges encountered by artificial intelligence in civil engineering over the past few years. It highlights studies that emphasize structural maintenance and management, quality control, design optimization, and more, accomplished through image processing and computer vision techniques. In essence, this review seeks to unveil the profound impact of artificial intelligence in transforming civil engineering practices, with a strong emphasis on exploiting image processing and computer vision methods to tackle concrete-related issues.