Machine learning model for predicting the crack detection and pattern recognition of geopolymer concrete beams

Abstract

Abstract One of the major challenges in the construction industry is the detection of cracks in concrete structures and identification of failure types of these structures that lead to their degradation. Manual quality checks are prone to human error, and require longer response time and specialist experience and knowledge. Therefore, visualizing the cracks and identifying failures in concrete structures using computer techniques is now a preferred option. The present work focuses on identifying the cracks using image processing and failure pattern recognition technique by employing suitable machine learning algorithms, and validating the techniques using Python programming. For this purpose, M30 grade geopolymer and conventional concrete beams were cast using Basalt Fibre Reinforced Polymer/Glass Fibre Reinforced Polymer and Steel bars. The beams were subjected to four point static bending test by varying the shear span to the effective depth ratio. The experimental images were used for image processing and failure pattern recognition in Python language. Employing six machine learning classifiers, the failures in the structures were classified into three classes namely, flexure, shear, and compression. The machine learning classifiers were also adopted to determine the confusion matrix, accuracy, precision, and recall scores. It was found that among the six classifiers used, the support vector classifier gave the best performance with 100% accuracy in identifying the failure patterns.