Face-Vs-Segment Comparison: A New Method For Comparing AS-Planned And AS-Built Models

Advances in the Fourth industrial revolution (4IR) have significantly impacted the Architecture, Engineering, and Construction (AEC) industry, introducing innovations such as Building Information Modelling (BIM), laser scanning, and Lean construction approaches. These advancements have enhanced the performance of construction progress monitoring. However, mistakes still occur during construction process due to drawing errors, poor communication, limited expertise, accidents, and other factors. The primary goal of progress monitoring is to promptly detect mistakes during construction, identify their nature, communicate the is
sue to the responsible party for correction, and update the project drawings (or BIM model) accordingly. This ensures that a stable and validated as-planned model is maintained for subsequent inspections. In this context, the Scan-vs-BIM approach has been proposed to improve the construction progress monitoring process by leveraging 4IR technologies. However, an extensive literature review of current Scan-vs-BIM applications revealed a significant gap in how deviations between the as-planned, and as-built models of a building under construction are identified, reported, and visualised. To address this gap, this research proposes an improved Scan-vs-BIM methodology. The novel part of this methodology is the development of a new comparison method in Python to compare the as-planned and as-built model of the indoor environment of a building under construction. The new comparison method is termed Face-vs-Segment comparison and focuses on comparing the two models in a building element surface level. It was initially applied to test data, and subsequently to real case study data, to increase its robustness, and demonstrate its practical contribution. This novel comparison method offers a customised way of reporting data by incorporating semantic information into the results, and by documenting the magnitude and direction of deviations in building elements. The research concludes that the proposed improved construction progress monitoring methodology can significantly reduce the time required for monitoring, increase overall productivity, and finally positively contribute to decision-making process. It is anticipated that with further research on this methodology, the construction progress monitoring process can be substantially improved.