Automated Multi-Class Concrete Crack Detection and Severity Classification Using CNN-Based Deep Learning

Abstract

Structural integrity is essential to sustainable infrastructure development, particularly in concrete structures. These are prone to deterioration from environmental exposure, mechanical stress, and corrosion. Conventional inspection techniques such as manual surveys and non-destructive testing (NDT)—are labor-intensive, time-consuming, and often limited by human accuracy, making them unsuitable for large-scale deployment. This research proposes an automated system using a custom Convolutional Neural Network (CNN) architecture tailored for concrete defect detection and severity classification. The model was built with four convolutional blocks (32–256 filters), max-pooling layers, batch normalization, and a final dense layer, totaling approximately 129,000 parameters. It was trained on a custom-labeled dataset of 21,000 images (20,000 crack images and 1,000 corrosion images), collected from publicly available repositories and manually classified into seven categories: No Cracks, Hairline Cracks, Small Cracks, Moderate Cracks, Large Cracks, Very Large Cracks, and Cracks Due to Corrosion. Data augmentation techniques were used to address class imbalance and improve generalization. Experimental results showed 94.7% classification accuracy, 93.5% precision, 92.8% recall, and a 93.1% F1 score. The system processes ~25 images/sec on an NVIDIA RTX 3060 GPU, making it suitable for real-time applications. This system represents a scalable, high-performance approach to infrastructure health monitoring, contributing to safer and more effective structural maintenance.