Detection of defects in building materials using artificial intelligence systems

Abstract

This paper focuses on the problem of automatic defect detection in building materials and the use of deep learning and pattern recognition to solve this problem. The paper describes various methods that can be used to solve this problem, including transfer learning, data augmentation, and fine-tuning, and discusses the advantages and limitations of each approach. The article also describes a convolutional neural network (CNN) architecture that can be used to detect defects in building materials, specifying the purpose and functionality of each layer. In addition, the article presents the mathematical formulas necessary for this approach, including the convolution operation, the ReLU activation function, the maximum association operation, the dropout operation, and the sigmoid activation function. Overall, the paper highlights the potential of deep learning and pattern recognition in building materials quality control and the benefits that automated systems can bring to the construction industry. The use of these technologies can increase efficiency, reduce costs, and improve the quality of construction projects, ultimately leading to safer and more durable structures.