An Artificial Intelligence-Based Method for Crack Detection in Engineering Facilities around Subways

Abstract

While the construction and operation of subways have brought convenience to commuters, it has also caused ground subsidence and cracks of facilities around subways. The industry mainly adopts traditional manual detection methods to monitor these settlements and cracks. The current approaches have difficulties in achieving all-weather, all-region dynamic monitoring, increasing the traffic burden of the city during the monitoring work. The study aims to provide a large-scale settlement detection approach based on PS-InSAR for the monitoring of subway facilities. Meanwhile, this paper proposes a crack detection method that is based on UAVs and the VGG16 algorithm to quantify the length and width of cracks. The experimental data of Shenzhen University Section of Metro Line 9 are used to verify the proposed settlement model and to illustrate the monitoring process. The developed model is innovative in that it can monitor the settlement of large-scale facilities around the subway with high accuracy around the clock and automatically identify and quantify the cracks in the settled facilities around the subway.