Convolutional neural networks for automated damage recognition and damage type identification

Recurring expenses associated with preventative maintenance and inspection produce operational inefficiencies and unnecessary spending. Human inspectors may submit inaccurate damage assessments and physically inaccessible locations, like underground mining structures, and pose additional logistical challenges. Automated systems and computer vision can significantly reduce these challenges and streamline preventative maintenance and inspection. The authors propose a convolutional neural network (CNN)-based approach to identify the presence and type of structural damage. CNN is a deep feed-forward artificial neural network that utilizes learnable convolutional filters to identify distinguishing patterns present in images. CNN is invariant to image scale, location, and noise, which makes it robust to classify damage of different sizes or shapes. The proposed approach is validated with synthetic data of a composite sandwich panel with debonding damage, and crack damage recognition is demonstrated on real concrete bridge crack images. CNN outperforms several other machine learning algorithms in completing the same task. The authors conclude that CNN is an effective tool for the detection and type identification of damage.