Artificial intelligence-enhanced non-destructive defect detection for civil infrastructure

As civil engineering projects become more complex, ensuring the integrity of infrastructure is essential. Traditional inspection methods may damage structures, highlighting the need for non-destructive testing. However, conventional non-destructive methods involve challenges in assessing complex civil infrastructure due to manual operation and subjective interpretation. The integration of artificial intelligence has revolutionized non-destructive testing for civil infrastructure: it rapidly processes data, detects minor defects autonomously, and provides early warnings. This paper explores the significant advancements in artificial intelligence-enhanced non-destructive testing, particularly in radar detection, radiography, and sound-based technologies. Their synergy not only elevates the accuracy and efficiency of structural assessments but also extends the applicability of non-destructive testing techniques in order to address a broad spectrum of complex structural challenges more effectively. These advancements promise breakthroughs in automated inspections, real-time structural monitoring, and predictive maintenance, marking a significant leap forward in the field of civil infrastructure defect detection. • Identify key challenges in non-destructive testing for civil infrastructure defect detection. • Highlight artificial intelligence's role in advancing radar, radiographic, and sound-based defect detection techniques. • Review significant strides made in artificial intelligence-enhanced non-destructive testing. • Provide a roadmap for standardization, enabling intelligent defect detection and safer, more reliable infrastructure.