Automated identification of structural crack using image-processing technique and optimized machine learning

Abstract

This paper explores the application of advanced machine learning and image processing methodologies, specifically targeting the detection of structural damage, particularly cracks, within the construction sector. By utilizing Convolutional Neural Network (CNN) technology on the Kaggle online platform for tasks like data analysis and model development, the study aims to automatically identify errors and defects on work surfaces, ultimately seeking to elevate the quality control processes in construction projects. The CNN architecture, tailored for image processing, plays a crucial role in effectively learning features and patterns, thereby significantly enhancing the precision in identifying errors and defects. The research employs a dataset consisting of 1000 images collected from real projects for training, validation, and testing of the proposed model. Through the integration of image processing, optimized CNN, and the resources provided by the Kaggle platform, the study strives to enhance the efficiency, accuracy, and automation of error and defect detection and classification, making notable contributions to the progression of quality management processes in the construction industry.