2023 journal article
Automated Model-Based 3D Scan Planning for Prefabricated Building Components
Journal of Computing in Civil Engineering.
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(Web of Science)
Source: ORCID
Added: November 25, 2022
Modular construction can improve construction performance (i.e., cost, schedule, and safety) by prefabricating modules at an off-site facility and installing them at a construction site. However, when defects of modules are not easily repairable on the construction site, they cause additional cost overruns and delays due to long lead times of refabrication and reshipment. Thus, quality assessment of modular components at the fabrication facility before shipment is very important. The current inspection practices rely on manual measurement, which can be imprecise, labor-intensive, and time-consuming. To address this issue, some research efforts are made on the module inspection techniques (e.g., estimates of geometric properties and surface quality) using laser-scanned data. The accuracy of these techniques relies on the quality (i.e., coverage and resolution) of the scan data. However, ensuring the consistent quality of data is a major challenge as there is little to no research on optimal scan planning for modular components. Therefore, this paper proposes a model-based 3D scan planning method for modular components that ensures user-specified scan quality. Given a 3D computer-aided design (CAD) or building information modeling (BIM) model, scanner property, and user’s quality requirement, this method automatically computes the input parameters for the laser scanner (i.e., angular step and field of view) and optimal scan positions. It also predicts the scan quality and shows the areas that will not meet the user requirement due to geometric constraints (i.e., self-occluded surfaces). This study was validated through two case studies using two modular-sized structures in a fabrication facility. The results showed that the scan planner is able to accurately predict the scanning quality and ensure that the output scan will meet the user quality requirement.