Optimizing deep belief network for concrete crack detection via a modified design of ideal gas molecular dynamics

Concrete structures are prone to developing cracks, which can have a negative impact on their overall performance and longevity. It is essential to promptly identify and repair these cracks in order to ensure the structural integrity of the building. The present research concentrates on the development of crack diagnosis algorithms based on vision using an optimized version of Deep Neural Network (DNN). The DNN model employed in the current study is the deep belief network (DBN), while the optimization technique is based on a newly designed variant of the Ideal Gas Molecular Movement (MIGMM). By combining these two components, a highly effective crack detection system is created, capable of achieving higher classification rates. To train the DNN model, an image dataset comprising two classes, namely "no-cracks" and "cracks", has been utilized. The MIGMM has been applied to the DBN model, involving fine-tuning the network architecture's weights, substituting the categorization layer with two classes of output (cracks and no-cracks), and augmenting the picture dataset using stochastic angles of rotation. The proposed DBN/MIGMM model achieves exceptional performance, with an accuracy of 90.189%, specificity of 94.502%, precision of 94.586%, recall of 94.529%, and an F1-score of 88.093%, outperforming state-of-the-art methods such as Fully Convolutional Networks (FCN), You Only Look Once (YOLO), CrackSegNet, Convolutional Neural Networks (CNN), and Convolutional Encoder-Decoder Networks (CedNet). The present outcomes prepare a comprehensive superior assessment of the proposed model's effectiveness in accurately detecting and classifying cracks.