CNN-Based Automatic Mobile Reporting System and Quantification for the Concrete Crack Size of the Precast Members of OSC Construction

Abstract

Civil infrastructure over the years has experienced a dominant reliance on concrete material compared to other construction materials. Human inspection is the main mode of inspection for such structures, including concrete columns, which has been proven to be inaccurate and time-consuming. Convolutional neural networks (CNNs) are a substitute for such problems for both detection and quantification. However, storing the results and visualizing them at a later stage has always been a challenge. Additionally, integration of the concrete crack deep learning model to a mobile platform is an area that has received less attention. This study focuses on segmenting the concrete crack sections using the latest state-of-the-art (YOLOv7) neural network, which is then used to obtain the quantification data about the length and width of the detected crack using image binarization, and finally the results are published using a reporting system integrated to a mobile platform using a web and IoT system. The published report uses a checklist from the quantification results to grade the crack as well as its structure. The results show a mAP of 0.85, while the quantification results show a 10.82% absolute error, respectively. The reporting system takes a combined average of 5940 ms to store the data inside a database, which is then published through a mobile device. It has been demonstrated through this study that an automatic mobile reporting system is feasible to be used on buildings for maintenance, which can be further applied across other sectors of construction for monitoring and repair purposes.

Keywords

• crack quantification
• CNN
• precast concrete (PC) member
• database management system
• off-site construction (OSC)