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A selective disassembly multi-objective optimization approach for adaptive reuse of building components

Sanchez, Benjamin, Rausch, Christopher, Haas, Carl, Saari, Rebecca
Resources, conservation, and recycling 2020 v.154 pp. 104605
assets, buildings, circular economy, construction industry, decision making, design for environment, environmental impact, planning, quantitative analysis
Adaptive reuse of buildings plays a key role in the transition from a resource-based economy and towards a Circular Economy (CE) in the construction industry. Adaptive reuse has the potential to maximize the residual utility and value of existing assets through green design methods such as selective disassembly planning. Studies in the field of selective disassembly are scarce and there is no evidence of established methodologies for the optimization of the environmental and financial benefits. A multi-objective analysis is key to obtaining several effective selective disassembly plans for the adaptive reuse of an existing asset through the combination of different deconstruction methods. The analysis is carried out in terms of the physical, environmental, and economic constraints of the deconstruction methods per building component. The Sequential Disassembly Planning for Buildings (SDPB) method is used in order to generate the optimized disassembly plans for retrieving target components. At the end, a weighted multi-objective optimization analysis is incorporated to generate the set of noninferior solutions that minimizes environmental impacts and building cost. The results show that different complete disassembly plans exist for all the possible combinations. The possible combinations are driven by the deconstruction methods per component, as well as the dismantling interdependence. The method described in this study can be used to improve the project outcomes according to specific goals and constraints (e.g. environmental, economic, technical). The results of this study improve the decision-making process for adaptive reuse building projects by adding comprehensive quantitative analysis towards sustainable management and conservation of resources.