Optimizing concrete crack detection an echo state network approach with improved fish migration optimization

There are numerous reasons for concrete buildings cracks, like stress loads, material flaws, and environmental impacts. It is important to find and investigate the concrete cracks during analyzing the safety and structural soundness of buildings, bridges, and other infrastructure. However, there are many models available for concrete crack detection, an efficient approach is needed because the existing methods often have flaws like overfitting, high computational complexity, and noise sensitivity, which can lead to accurate crack detection and classification. This paper proposes an enhanced version of the fish migration optimization (IFMO) method combined with an optimized echo state network (ESN) model for concrete fracture detection using the combination form is established for improving the detection accuracy of the model by optimal arrangement of the ESN. The suggested ESN/IFMO model was tested on the SDNET2018 dataset, which comprises concrete photos with diverse fracture patterns, and the results were compared to several other state-of-the-art approaches. The suggested ESN/IFMO model shows potential as a more effective solution for concrete crack identification, increasing accuracy by 3.75-8.19% over current models such as DL, DINN, AlexNet, CNN, and LSTM, as well as increasing F1 score by 5.14-12.55%.