Precision crack analysis in concrete structures using CNN, SVM, and KNN: a machine learning approach

Abstract

Cracks in structures are discontinuities that occur due to stress, material degradation, or design flaws, compromising structural integrity. Detecting and analyzing cracks is crucial for assessing safety and determining maintenance needs. Various methods like image processing, machine learning, and deep learning are employed for accurate crack identification and classification. Advanced algorithms enhance detection accuracy and efficiency, leading to improved structural maintenance strategies. Addressing cracks promptly ensures prolonged infrastructure lifespan and safety. The goal of this research is to completely change the field of concrete cracking analysis through the use of cutting-edge machine learning technologies. The main objective is to design and implement a crack analysis system that uses cutting-edge techniques such as Convolutional Neural Network (CNN), support vector machine (SVM), and the k-nearest-neighbor (KNN). By harnessing the power of machine learning, the project aims to achieve unprecedented accuracy in crack detection, enabling precise identification of crack characteristics including length, width, and depth. Through meticulous coding and collaboration utilizing platforms like Google Colab, the study seeks to establish a robust framework for effectively and efficiently classifying cracks in structures. This innovative approach holds promise for enhancing structural integrity assessment and facilitating timely maintenance interventions, thus contributing to safer and more resilient infrastructure.