Towards a Quantitative Approach for Monitoring and Evaluating Construction Defect Management Inspection Performance using Eye-tracking Technologies

Defect management (DM) inspections play an important role in the overall performance of construction projects. Identifying defects early within the construction project life cycle leads to mitigating significant reworks and potential hazards from occurring later in the building life cycle. Despite the importance of DM inspections in construction, currently, minimal approaches exist to evaluate or assess the performance quality of on-site DM inspectors. To address these issues, we present a novel data analysis tool that incorporates Building Information Modelling technologies to evaluate DM in-spection performance using eye gaze data captured during the DM inspection. This paper presents an overview of our proposed data analysis tool, which consists of a four-dimensional (4D) playback visualisation system and a quantitative data analysis simulator. We also present our findings from a pilot study that uses our proposed data analysis tool to comparatively evaluate the performance of two types of DM inspection approaches: conventional paper-based methods, and an experimental Augmented Reality (AR) system. We release our DM data analysis software as an open-source project under the MIT license.