Single-shot multibox detector- and building information modeling-based quality inspection model for construction projects

Abstract

Abstract Identifying, locating, and evaluating concrete construction defects in the construction sector consumes time and is overlaid by subjectivity. This study proposes a single-shot multibox detector (SSD)-based and building information modeling (BIM)-based quality inspection framework to improve work efficiency and decrease unnecessary deviations resulting from human judgment. The SSD-based defect recognition model, which can identify surface defects with 86.56% average recall and 75.34% average precision, is trained using 1387 defect images of crack, honeycombing, water infiltration, rebar exposure, and edge or corner damage. Furthermore, a division algorithm is created to divide the BIM model surfaces into pieces, in which each piece is assigned with a unique location code number. After photographing a construction project according to the coding sequence, the defects in the images are identified and located on the BIM model. Then, the frequency of the quality defects, defect aggregation zones, and quality status in the BIM model are provided to the managers for fast-paced analysis. The case study results indicate that our inspection framework has the shortest inspection time and the highest kappa coefficients (0.816, 0.901, and 0.799) for crack, honeycombing, and reinforcement exposure prediction. The inspection accuracy of our model exceeds 98.5% for each defect. Our novel quality inspection framework can remarkably improve the efficiency and effectiveness of quality management and lead to a higher level of project management.