Abstract:
Concrete bridges require timely and accurate defect detection in order to maintain their structural safety, time-consuming, expensive processes often suffer from human errors. Meta-learning is applied to enhance convolutional neural networks for multi-target defect classification. The CODEBRIM dataset is harnessed in this study, challenging due to overlapping defects and varying environmental conditions. MetaQNN and ENAS are two advanced neural architecture search techniques used to automatically design optimized CNN models. These models are then compared with traditional architectures like ResNet, VGG, and DenseNet. Experimental results show that meta-learned models achieve significant improvements in terms of classification accuracy and computational efficiency. The most accurate models attained 75% test accuracy, reflecting the strength of simultaneous multi-defect identification. It proposes an AI-powered way of automated bridge inspection that is expected to be faster, more reliable, and less expensive than today, enhancing security on infrastructure items and lower maintenance costs.
