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Recent disruptions of communities due to natural hazard events such as hurricanes and earthquakes have led to increased calls for improved resiliency of the built environment. The "built environment" denotes constructed facilities such as buildings and bridges, as well as infrastructure systems such as power delivery, transportation roadways, and water utilities. "Resiliency" is defined here as the "recovery and adaptability" during and after events which disrupt civil infrastructure services. In the context of this paper, the critically important service is energy delivery, on which many other services such as communications and transportation networks depend. The robustness of the building energy supply can be significantly enhanced through on-site renewable sources such as photovoltaic panels coupled with storage batteries. The degree to which the energy demand is met by the on-site capacity in the future will be determined largely upon advances in renewable energy generation and storage as well as in efficiency gains for commonly used equipment and appliances such as lighting fixtures and cooling systems. In this paper, we propose an improved design approach for the energy capacity of existing and new buildings as part of a greater regional community in which the total energy capacity requirements are met through increasingly enhanced on-site permanent power links, as opposed to increased reliance on the existing power grid. The metrics for characterizing resiliency will be "robustness," "redundancy," "resourcefulness," and "rapidity," with the associated metrics for sustainability being self-reliance and intergenerational equity enhancement. [ABSTRACT FROM AUTHOR]

According to NOAA, eleven billion-dollar weather disasters occurred in the United States in 2012. One of the predominant reasons that storms become disasters is the failure of civil infrastructure systems, also known as "lifelines," on which communities rely. The approach to modeling resilience herein is through the use of systems-based models of performance. The models are based on empirical evidence of the ability of the underlying structural delivery system to provide essential infrastructure services to the community. For example, electric power delivery performance, subject to extreme storms, is sensitive to the combined system of mounted electrical and mechanical equipment, substations, and poles and towers that comprise the distribution and transmission systems. Interdependency is assessed first through previously derived input-output models of recovery to storm hazards and then significantly expanded in this paper. Further, the research results, based upon examination of extensive geocoded data for actual events, show that tropical and midlatitude cyclonic storm systems are similar in their destructive tendencies in coastal regions of the United States. These types of storms are composed of the multiple hazards of high wind speeds, precipitation, and flooding due to storm surge or heavy rainfall. Traditional analyses of infrastructure performance have relied upon evaluating the effect of one hazard or demand variable such as wind speed or peak storm surge on the loss of structural capacity. This investigation suggests that multivariate distributions of the demand variables of wind speed, storm surge, and rainfall are important for the analysis of infrastructure performance. Novel aspects of the research presented here include the synthesis of input-output interdependency models with logit transformations for system-based structural fragility relationships using multivariate hazard information, the development of multivariate hazard representations for select storms, and the use of single degree of freedom (SDOF) system analogs to model the minimization of lifeline inoperability. Another novel aspect of the research involves the extension of the inoperability SDOF model to assess correlation of the SDOF parameters with multiple weather hazard variables. Characterization of this correlation allows for the implementation of numerical simulations to predict system performance for various demand levels. [ABSTRACT FROM AUTHOR]