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Open-source software, within the civil engineering field, can easily foster innovation and accessibility by providing cost-effective tools for design and analysis, because of its accessible nature. Meanwhile Scan-to-BIM refers to the process of using captured data from 3D scanning technologies, to create accurate, detailed as-built Building Information Models (BIM), facilitating tasks such as renovation, maintenance, and management throughout the infrastructure lifecycle. Currently, the existing literature on Scan-to-BIM highlights significant advances in integrating various sensing technologies to improve building documentation. Nevertheless, a lot of research focuses on proprietary software solutions, which restricts smaller projects' access and scalability. To address this problem, this study aims to develop an open-source Scan-to-BIM workflow, based on multi-sensor surveying.
For this purpose, the study employs the open-source software Blender, paired with the Bonsai add-on, to create a BIM model from the data gathered by various aerial and hand-held surveys taking place at the Faculty of Engineering of the University of Porto (FEUP). At the same time, these surveys are used to integrate three major spatial sensing technologies to capture exterior and interior data: Unmanned Aerial Vehicle (UAV) based photogrammetry and thermography, and an iOS device equipped with Light Detection and Ranging (LiDAR). The photogrammetry and LiDAR results are processed and optimized to then be aligned and used as a guide for Open-BIM (open-source BIM) modelling. Meanwhile, the images gathered from the thermal camera are used as a texture that is projected on top of the Open-BIM model, following an Image-to-BIM workflow, allowing for comprehensive thermal data analysis.
The main findings of the study prove that an affordable open-source Scan-to-BIM workflow is fully achievable. That means that entities such as educational institutions, small businesses, enthusiasts and projects with limited resources, can have access to high-quality 3D documentation and diagnostic capabilities. Additionally, when aligning the different datasets from the multi-sensor surveys, the photogrammetry demonstrated millimetric-level accuracy relative to the real building, and with LiDAR’s case, little to no requirement for scale adjustment was needed to match and align them, indicating that the integration of diverse data sources within the workflow is reliable and produces coherent, spatially accurate 3D models. Finally, during the BIM data integration, the open-source software Blender, with the Bonsai add-on, established itself as a complete and versatile platform capable of managing complex Scan-to-BIM processes efficiently, demonstrating that open-source tools can effectively support advanced BIM tasks, offering a practical and cost-effective alternative to proprietary software solutions.