Automated Pattern Recognition of Structural Anomalies Using Convolutional Neural Networks

The early and accurate detection of structural anomalies is vital for maintaining the safety and serviceability of civil infrastructure. This study presents a convolutional neural network (CNN)-based framework for the automated recognition of damage patterns in reinforced concrete and steel structures. Using a real-world dataset comprising over ۱۵,۰۰۰ high-resolution images of bridges and buildings under varying conditions, including cracks, corrosion, and spalling, the model was trained to detect anomalies with minimal human supervision. The dataset was augmented through geometric and photometric transformations to improve generalization across lighting, angle, and surface variations. A custom deep CNN architecture with ۱۲ convolutional layers and residual connections was implemented, achieving an average classification accuracy of ۹۶.۳% on a held-out test set. The model was benchmarked against standard architectures such as VGG۱۶ and ResNet۵۰, outperforming them in both precision and inference speed. The results demonstrate that deep learning models can reliably identify diverse structural damages, enabling scalable and real-time monitoring in smart infrastructure systems. Future research will focus on integrating thermal imaging and drone-based image acquisition to further enhance detection performance in real-time field conditions.

Keywords:

Structural anomaly detection , Convolutional neural networks (CNN) , Deep learning , Concrete cracks , Steel corrosion , Image-based monitoring , Pattern recognition , Infrastructure health assessment , Computer vision in civil engineering , Automated damage classification