Application of machine learning for crack detection on concrete structures using CNN architecture

Abstract

Cracks in concrete structures are caused due to contraction and expansion irregularities, from potential damages caused in the buildings. These irregularities and damages are assessed by the engineers manually or through identification and prediction models with machine learning techniques to evaluate the impact and significance of the structural health in buildings. This research aims at applying machine learning based on VGG16-Net model for the detection of cracks in concrete structures. The proposed model is of CNN (convolutional neural network) + VGG neural network based architecture. The study uses the gradient boosting algorithm for image segmentation. The datasets are obtained from "Kaggle" resource and the library used is 'Hugging Face Transformers'. To evaluate the developed models' performance metrics such as "accuracy, precision, recall and f1-score" are used. The 'accuracy' score obtained is compared against the 'ViT' (Google transformer) accuracy rate, for comparison. The proposed model achieved 98% validation accuracy rate with 0.3% loss. Thus the developed research contributes an innovative and a novel ML model that predicts and identifies the cracks in concrete structures with less loss and higher accuracy with CNN architecture than ViT (vision transformer) models. Current study also provides more input upon CNN being more accurate than ViT models for future researchers for comparative analyses.