A new methodology to automatically detect cracks in existing RC bridges

The paper presents a novel approach to detect cracks in existing reinforced concrete (RC) bridges using computer vision (CV) techniques as smart sensors and to identify existing damages from photos. This method involves training specialized convolutional neural networks (CNNs) to identify cracks in RC components, focusing on automated detection. The process begins with defining a detailed dataset of labeled crack images by domain experts in the field. Subsequently, CNNs designed for crack detection are trained and assessed. The effectiveness of the method is initially evaluated through visual comparisons, with more specific evaluations planned to use defined metrics upon completion of development. This innovative methodology aims to drive digital progress and artificial intelligence applications in advanced visual inspections, ultimately safeguarding the structures of existing bridge stock.