Automated Crack Analysis and Reporting in Civil Infrastructure using Generative AI

Maintaining and inspecting infrastructure is crucial due to the safety hazards and economic costs of structural failures. Traditional methods are labor-intensive, time-consuming, and reactive. We propose an automated inspection system leveraging generative AI to enhance efficiency, predictive maintenance capabilities, and comprehensive data analysis. Our framework uses drone-based data acquisition with high-definition cameras and depth sensors, and a custom deep learning model, EyeNet, for precise crack detection. Generative AI techniques, including a Visual Question-Answering (VQA) model and an image-to-image model, are employed for detailed crack analysis and future crack pattern visualization, enabling proactive maintenance. The VQA model achieves an average Root Mean Square Error (RMSE) of 0.394 and an average Symmetric Mean Absolute Percentage Error (SMAPE) of 31.22%. A Large Language Model generates comprehensive reports with visualizations, accessible via a dedicated website. Our system significantly improves the inspection process compared to traditional methods, setting a new benchmark by combining generative AI for detailed crack analysis and predictive maintenance capabilities, creating a comprehensive inspection system for civil infrastructure.