Building Information Modeling (BIM) has been developed in response to the growing complexity of construction projects. BIM implementation is beneficial throughout the entire building life cycle, and thus has been widely adopted in new projects. However, BIM implementation in existing buildings is impeded by the lack of as-built models. Conventionally, three-dimensional (3D) as-built BIMs are generated by experienced modelers, which is time-consuming and error-prone. To cater to the need, Scan-to-BIM is a solution for automation in as-built BIM generation. In the context of automated Scan-to-BIM, the parametric modeling process is worth investigating as it has the ability to not only reconstruct 3D objects from a variety of categories from point clouds but also offer flexibility to update objects simply by changing the values of the correlative parameters. Hence, this study proposes a semi-automated framework for assisting 3D as-built modeling through a parametric modeling approach. The presented methodology starts with wall boundary parameter extraction through 3D to 2D projection of the wall segments and line detection techniques, followed by retrieving geometric parameters of all other non-wall elements via CloudCompare. The extracted parameters are structured into a Microsoft® Excel® file and fed into Autodesk® Dynamo for 3D BIM creation using a series of designed logic. To substantiate the viability, the proposed framework is employed in two datasets containing structural, architectural, furniture, mechanical, and plumbing objects (further categorized into structural, hosted and non-hosted elements). The Intersection over Union (IoU) of structural elements was 96.35%, while the root-mean-square error (RMSE) of hosted and non-hosted elements was 3.634 and 2.607 mm, respectively. This study established a universal methodology for semi-automated 3D as-built modeling that can guide future research.

The traditional linear economy model of water resource management, which follows a take-make-use-dispose pattern, is increasingly inadequate in meeting the rising demand driven by population growth and climate change. In contrast, the circular economy, which emphasizes the repeated use of water, has been proposed and advocated as a promising alternative over the past few decades. Nevertheless, research endeavours have yet to reach the public in an efficient way, resulting in a gap of public engagement in circular economy adoption. With advancements in computing power, this study investigates the potential of mixed reality (MR) technology to enhance learning about circular economy in water systems, focusing on engineering students. The authors compared the MR-based learning approach with conventional learning methods leveraging a proposed quantitative workflow. Fifty engineering students participated in a controlled experiment to assess learning outcomes. The findings significantly highlight the effectiveness of MR with 27.1% improvement of assessment scores, surpassing the 11.5% improvement gain from the traditional learning approaches. This research underscores MR’s potential in translating complex ideas into more accessible format. Future research will expand participant diversity and apply MR to other sectors, enhancing public understanding and adoption of circular economy concepts.