Methodology for improving the net environmental impacts of new buildings through product recovery management

Buildings contribute significantly to the global environmental load caused by human activities. There has been a growing interest in improving a building's performance over all of the life-cycle stages (production, construction, operation, and End-of-Life [EoL]). Several studies have recognized the importance of the EoL stage in buildings in terms of sustainability and Circular Economy (CE). A methodology for improving the net environmental impacts of new buildings through Product Recovery Management (PRM) is presented in this thesis. It starts with a CE perspective that emphasizes the importance of adaptive reuse of buildings over new construction. Context is established with a relevant case study in the Waterloo Region. Then, product recovery planning methods that meet environmental life-cycle objectives as well as cost objectives are presented that enhance the attractiveness of adaptive reuse as an alternative. Validation of the proposed methods is achieved through functional demonstration with case studies. Together, these methods form a rational approach to improve the net environmental impact of buildings in our economy. The overall proposed framework in this thesis have demonstrated to be effective to improve sustainability in the construction industry by providing a better understanding of the net environmental impacts and economic potential benefits of buildings' adaptive reuse. Finally, this thesis marks a reference for the development of innovative user-friendly methods and tools for reducing inefficiencies in the process of adaptive reuse through PRM

Efficient architectural structural element decomposition

© 2016 Elsevier Inc. Decomposing 3D building models into architectural elements is an essential step in understanding their 3D structure. Although we focus on landmark buildings, our approach generalizes to arbitrary 3D objects. We formulate the decomposition as a multi-label optimization that identifies individual elements of a landmark. This allows our system to cope with noisy, incomplete, outlier-contaminated 3D point clouds. We detect four types of structural cues, namely dominant mirror symmetries, rotational symmetries, shape primitives, and polylines capturing free-form shapes of the landmark not explained by symmetry. Our novel method combine these cues enables modeling the variability present in complex 3D models, and robustly decomposing them into architectural structural elements. Our proposed architectural decomposition facilitates significant 3D model compression and shape-specific modeling.Kobyshev N., Riemenschneider H., Bódis-Szomorú A., Van Gool L., ''Efficient architectural structural element decomposition'', Computer vision and image understanding, vol. 157, pp. 300-312, April 2017.status: publishe