The design of multi-hazard resilient and sustainable buildings (RSB) is necessarily complex due to the various economic, environmental, social, and technical considerations that must be factored into the decision-making process. These broad considerations result in tradeoffs among the numerous design objectives (e.g., minimizing life cycle cost, probable losses, and environmental impact). Negotiating these tradeoffs necessitates new decision-making frameworks and tools to enable efficient and effective exploration of multi-hazard RSB designs. Literature in the fields of behavioral economics, psychology, marketing, and cognitive engineering support the premise that decision-making is a simultaneous process of both constructing and satisfying one's preferences. The objective of this research is to formalize an innovative sequential decision process, tailored to simultaneous preference construction and satisfaction, for the design of multi-hazard RSB. Model simulations will be performed in a sequence of increasing fidelity while simultaneously decision-makers (DMs) become cognizant of the design space and inherent tradeoffs (trade space), form preferences, and then satisfy these preferences, culling the set of considered designs throughout to arrive at a final choice. Key to the framework's development are the following: (1) creating a complexity index that will situate the building in relation to its social, technical, economic, policy, and environmental context, and assessing the impact of this context on the sustainability of the building, and conversely, the impact of the building on its larger, regional context, (2) iteratively exploring for innovative multi-hazard RSB designs, (3) establishing sets of feasible design objectives and threshold values of design metrics, and (4) developing digital visualizations of the trade space to facilitate and justify design choices. The sequential decision framework will have a positive impact on the integrative design process by providing mechanisms for learning, visualizing, and sharing of information among the DMs, design team, and stakeholders so that the outcome is an environmentally, socially, financially responsible building design.
The outcome of this research project will be a rigorous, sequential, decision framework based on progressive model-based simulation and visualization algorithms to support trade space exploration for multi-hazard RSB designs. Evolutionary algorithms will be employed at each level of model fidelity to generate soil-foundation-structural-envelope building system design alternatives, which consider an array of materials, structural forms, and building components. A broad array of design metrics will be generated for each building design alternative and hazard scenario(s) by integrating existing probabilistic performance-based assessment models, life cycle assessment models, and new probabilistic building recovery models. The building recovery models will be developed using a systems reliability approach and will generate the post-event functionality and recovery time metrics. The building recovery models will explicitly account for internal functions and externalities, such as utilities and the capacity of both organizational and technical systems, thereby allowing a particular building design to be understood in the broader context of a community's resilience. Novel interactive visual analytic techniques and visualization algorithms will be developed to facilitate multi-dimensional trade space exploration, allowing DMs to negotiate and gain insight into the intricate relationships among the conflicting design metrics to identify designs that are both resilient and sustainable. The formalized framework will be applied to in-depth case studies for the design of mid- and high-rise residential, commercial, and mixed-use RSBs threatened by earthquakes and/or hurricanes in urban environments.
|Effective start/end date||3/1/15 → 9/30/22|
- National Science Foundation: $822,491.00