A data-driven framework to support resilient and sustainable early design
| dc.contributor.author | Zaker Esteghamati, Mohsen | en |
| dc.contributor.committeechair | Rodriguez-Marek, Adrian | en |
| dc.contributor.committeechair | Flint, Madeleine Marie | en |
| dc.contributor.committeemember | Charney, Finley A. | en |
| dc.contributor.committeemember | Leon, Roberto T. | en |
| dc.contributor.committeemember | Zobel, Christopher W. | en |
| dc.contributor.department | Civil and Environmental Engineering | en |
| dc.date.accessioned | 2023-01-28T07:00:06Z | en |
| dc.date.available | 2023-01-28T07:00:06Z | en |
| dc.date.issued | 2021-08-05 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | Recent advances in structural engineering aspire to achieve higher societal objectives than focusing solely on safety. Two main current objectives are resiliency (i.e., the built environment's ability to rapidly and equitably recover after an external shock, among other definitions) and sustainability (i.e., the ability to meet current needs without preventing future generations from meeting theirs, among other definitions). Therefore, holistic design approaches are needed that can include and explicitly evaluate these objectives at different steps, particularly the earlier stages. The importance of earlier stages stems from the higher freedom to make critical decisions – such as material and building system selection – without incurring higher costs and effort on the designer. Performance-based engineering (PBE) is a quantitative approach to calculating the impact of natural hazards on the built environment. The calculated impacts from PBE can then be communicated through a more easily understood language such as monetary values. However, several challenges should be first addressed to apply PBE in early design. First, PBE assessments are time- and effort-intensive and require expertise that might not be available to the designer. Second, a typical early design exploration evaluates many alternatives, significantly increasing the already high computational and time cost. Third, PBE requires detailed design and building information which is not available at the preliminary stages. This lack of knowledge is coupled with additional uncertainties due to the random nature of natural hazards and building system characteristics (e.g., material strength or other mechanical properties). This dissertation proposes a framework to incorporate PBE in early design, and tests it for concrete mid-rise offices in Charleston, South Carolina. The centerpiece of this framework is to use data-driven modeling to learn directly from assessments. The data-driven modeling treats PBE as a pre-configured data inventory and develops statistical surrogate models (i.e., simplified mathematical models). These models can then relate early design parameters to building seismic and environmental performance. The inventory is also supported by prior knowledge, structured as a database of published literature on PBE assessments. Lastly, the knowledge-based and data-driven models are applied in a specific order to narrow the performance range for given building layout and system. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:32005 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/113550 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution-NonCommercial 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | en |
| dc.subject | Performance-based engineering | en |
| dc.subject | Sustainability | en |
| dc.subject | Early design | en |
| dc.subject | Surrogate modeling | en |
| dc.subject | Statistical learning | en |
| dc.title | A data-driven framework to support resilient and sustainable early design | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Civil Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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