Artificial Intelligence in Damage Detection of Concrete Structures: Techniques, Integration and Future Directions

Abstract

The chapter thoroughly explores the pivotal role played by Artificial Intelligence (AI) in the identification of damages in concrete structures. It delves into conventional methods, their limitations, and how AI can effectively complement these approaches. The basics of AI, encompassing machine learning and deep learning, are elucidated within the specific context of damage detection. Additionally, the chapter examines data acquisition and pre-processing techniques tailored for AI models. It sheds light on AI-driven damage detection methodologies, such as the utilization of convolutional neural networks for image analysis, vibration analysis, and AI-enhanced non-destructive testing methods, highlighting their precision in identifying structural issues. Moreover, the chapter investigates the integration of AI into structural health monitoring systems, providing in-depth discussions on data fusion and real-time monitoring. Emphasis is placed on the significance of performance assessment and model validation to ensure the reliability of AI algorithms. The chapter also addresses future trends, including the integration of AI with the Internet of Things (IoT), and delves into ethical considerations in the sphere of infrastructure development. In summary, the chapter underscores AI's transformative potential in revolutionizing damage detection and structural health assessment, contributing to the creation of more resilient and sustainable concrete structures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.