Augmenting progress monitoring in soil-foundation construction utilizing SOLOv2-based instance segmentation and visual BIM representation

Abstract

Efficient management of construction progress requires regular progress tracking, but monitoring progress on a daily basis can be both time-consuming and labor-intensive since the meticulous manual data processing is involved. Soil-foundation construction entails multiple uncertain underground safety risks, the elimination of which requires significant time and effort, thereby increasing the likelihood of schedule overruns. Automation and augmentation of progress monitoring are necessary for soil-foundation construction. There are two primary research challenges in the context of soil-foundation construction: the precise differentiation of objects with similar apparent characteristics, and the accurate detection of partially obscured objects. The existing approaches primarily address such issues by image post-processing rather than augmenting image feature inference. To enhance the efficiency of progress monitoring and facilitate unmanned inspection, an integrated computer vision-based framework for the tracking of soil-foundation construction progress was proposed. An improved SOLOv2 was utilized to qualitatively and quantitatively recognize the construction progress. Subsequently, the actual and differential progress could be integrated into streamlined BIM based on a self-adaptive grid-based mapping method. The approach was applied to a campus building project in China, resulting in a high segmentation accuracy (mAP = 90.9%). The improved SOLOv2 was found to surpass other state-of-the-art segmentation algorithms. Further, the impact of grid size on the mapping accuracy of construction progress was explored. The present study promotes automated schedule tracking and provides a feasible approach for developing a digital twin of soil-foundation construction.