AI-Powered Crack Analysis: Leveraging NLP for Structural Health Monitoring

Abstract

Cracks in structures pose significant risks to safety, integrity, and functionality, necessitating efficient and accurate analysis methods. This paper presents an innovative approach to crack analysis by integrating Artificial Intelligence (AI) techniques with Natural Language Processing (NLP) methodologies. Initially, NLP techniques are employed to extract key concepts, methodologies, and findings related to crack analysis from textual sources. Subsequently, features relevant to crack detection, classification, and severity assessment are extracted from the processed text data. AI models, including supervised learning algorithms such as Convolutional Neural Networks (CNNs) and unsupervised clustering methods, are trained using labelled data to detect cracks, classify their types, and assess their severity. Reinforcement learning techniques may also be explored for optimizing inspection strategies and maintenance schedules based on crack analysis results. Furthermore, the integration of sensor data, such as images from cameras and measurements from ultrasonic devices, enriches the analysis process, providing more accurate and comprehensive insights into crack formation and propagation. Real-time monitoring systems, coupled with AI models, enable continuous assessment of structural health, with alert mechanisms in place to notify relevant stakeholders of potential issues. This approach not only streamlines crack analysis processes but also enhances the efficiency and effectiveness of structural health monitoring systems, ultimately contributing to safer and more resilient infrastructure.

Keywords

• crack analysis
• artificial intelligence (ai)
• structural health monitoring
• cnn