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.

中文翻译：

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用于民用基础设施的人工智能增强无损缺陷检测


随着土木工程项目变得越来越复杂，确保基础设施的完整性至关重要。传统的检测方法可能会损坏结构，因此需要进行无损检测。然而，由于手动作和主观解释，传统的非破坏性方法在评估复杂的土木基础设施方面存在挑战。人工智能的集成彻底改变了民用基础设施的无损检测：它可以快速处理数据，自主检测轻微缺陷，并提供早期预警。本文探讨了人工智能增强无损检测的重大进展，特别是在雷达探测、射线照相和基于声音的技术方面。它们的协同作用不仅提高了结构评估的准确性和效率，还扩展了无损检测技术的适用性，以便更有效地应对各种复杂的结构挑战。这些进步有望在自动检测、实时结构监控和预测性维护方面取得突破，标志着民用基础设施缺陷检测领域的重大飞跃。