A data-driven framework to support resilient and sustainable early design

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Virginia Tech


Early design is the most critical stage to improve the resiliency and sustainability of buildings. An unaided early design follows the designer's accustomed domain of knowledge and cognitive biases. Given the inherent limitations of human decision-making, such a design process will only explore a small set of alternatives using limited criteria, and most likely, miss high-performing alternatives. Performance-based engineering (PBE) is a probabilistic approach to quantify buildings performance against natural hazards in terms of decision metrics such as repair cost and functionality loss. Therefore, PBE can remarkably improve early design by informing the designer regarding the possible consequences of different decisions. Incorporating PBE in early design is obstructed by several challenges such as time- and effort-intensiveness of performing rigorous PBE assessments, a specific skillset that might not be available, and accrual of aleatoric (associated with innate randomness of physical systems properties and surrounding environment conditions) and epistemic (associated with the incomplete state of knowledge) uncertainties. In addition, a successful early design requires exploring a large number of alternatives, which, when compounded by PBE assessments, will significantly exhaust computational resources and pressure the project timeline. This dissertation proposes a framework to integrate prior knowledge and PBE assessments in early design. The primary workflow in the proposed framework develops a performance inventory to train statistical surrogate models using supervised learning algorithms. This performance inventory comprises PBE assessments consistent with building taxonomy and site, and is supported by a knowledge-based module. The knowledge-based module organizes prior published PBE assessments as a relational database to supplement the performance inventory and aid early design exploration through knowledge-based surrogate models. Lastly, the developed knowledge-based and data-driven surrogate models are implemented in a sequential design exploration scheme to estimate the performance range for a given topology and building system. The proposed framework is then applied for mid-rise concrete office buildings in Charleston, South Carolina, where seismic vulnerability and environmental performance are linked to topology and design parameters.



Performance-based engineering, Sustainability, Early design, Surrogate modeling, Statistical learning