Deca Convolutional Layer Neural Network (DCL-NN) Method for Categorizing Concrete Cracks in Heritage Building

Abstract

It is critical to develop a method for detecting cracks in historic building concrete structures. This is due to the fact that it is a method of preserving historic building and protecting visitors from the collapse of a historic structure. The purpose of this research is to determine the best method for identifying cracks in the concrete surface of old buildings by using cracked images of old buildings. The various surface textures, crack irregularities, and background complexity that distinguish crack detection from other forms of image detection research present challenges in crack detection of old buildings. This study presents a framework for detecting concrete cracks in old buildings in Semarang's old town using a modified Convolutional Neural Network with a combination of several convolutional layers. This study employs ten convolutional layers (Deca Convolutional Layer Neural Network (DCL-NN)) to provide mapping features for images of concrete cracks in ancient buildings at preservation area. This study also compares commonly used machine learning models such as KNeighbors (n neighbors=3), Random Forest, Support Vector Machine (SVM), ExtraTrees (n estimators=10), and other CNN-pretained models such as VGG19, Xception, and MobileNet. Four performance indicators are used to validate each model's performance: accuracy, recall, precision, F1-score, Matthews Correlation Coefficient (MCC), and Cohen Kappa (CK). This study's data set is comprised of primary data obtained from cracked and normal images of several buildings in Semarang's old town. The accuracy of this study using DCL-NN is 98.87%, recall is 99.40%, precision is 98.33%, F1 is 98.86%, MCC is 97.74%, and CK is 98.86% for crack class. From this study, it was found that the ten convolution layers have higher classification performance compared to other comparison models such as machine learning and other CNN models and are more effective in detecting cracks in concrete structures.