A Scanner Technology Acceptance Model for Construction Projects. (2017)

• Record Type: Journal Article
• Authors: Sepasgozaar, Samad M.E.; Shirowzhan, Sara; Wang, Cynthia (Changxin)
• Is Part Of: Procedia engineering. Volume 180(2017)
• Page Start: 1237
• Page End: 1246
• Publication Date: 2017
• Subjects: Sensing Technology -- Laser -- Mobile Scanner -- Acceptance Model -- Construction; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2017.04.285
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Acquiring 3D building geometries is crucial for rapid energy exchange modelling of buildings. With a significant potential to contribute to high-performance built environment, positioning systems and laser scanners are being increasingly utilised to the construction industry for acquisition of 3D models of buildings. However, there are major barriers to successful scanner implementation in construction projects including a lack of knowledge about the scanner applications, low skilled workers and complicated data analysis processes. While many studies focus on general IT/ICT adoption in construction, there is a lack of understanding about the process of sensing technology adoption and its utilization in construction. To address the gap in the literature, this paper presents the Scanner Technology Acceptance Model (STAM) utilizing two main criteria; 'usefulness' and 'ease of use' each measured by a range of factors. The model is verified based on the result of the application of scanner and location system in a university building renovation project. The scanners were used to collect raw 3D point clouds to transfer into a compact, semantically rich models aiding in updating construction drawings. The findings show the effectiveness of the STAM. STAM enables technology suppliers to predict the technology diffusion rate, and helps the users to make decisions on choosing the right technology in construction projects. Further research based on this investigation will validate and enhance the model by using larger scale consultation and interview process, and this will be reported in a future research paper.