Conclusions and Recommendations

The design of multihazard resilient and sustainable buildings (RSB) is necessarily complex owing to the various economic, environmental, and technical considerations that must be factored into the design decision-making process. To facilitate broad exploration while identifying sufficing design solutions within a reasonable amount of time and effort, a general mathematical framework that views design as a sequential decision process was developed and applied for RSB design. The design and construction of RSB requires a significant amount of data ranging from recovery models to cost data for alternative strategies/designs. A data-driven and convergence-training framework is needed in which probabilistic- and artificial intelligence-based approaches assess networks' performance, quantify infrastructure network interdependence, and make projections on systems' resilience to natural and human hazards. Such a training framework can help prepare a new cadre of professionals to address the transdisciplinary challenges of infrastructure and societal resilience and sustainability.