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.

In moderate climates, the operation of windows is the most common way to control for thermal comfort. Window-opening behavior (WOB) is a complex process influenced by multiple factors, yet only simple bi-variate analyses between variables obtained from longitudinal datasets have been examined. The goal of this study is to investigate the effects of indoor and outdoor environmental parameters on WOB using a statistical modeling approach called “structural equation modeling.” The results show that the indoor environmental parameters, such as operative temperature and air velocity, mediated the relationship between the outdoor environmental parameters, such as outdoor air temperature and wind gust, and the WOB. The indoor wet-bulb globe temperature rose as the solar radiation increased, and subsequently, both parameters affected the WOB. Also, an increase in outdoor wind gust led to higher indoor air velocity, which in turn resulted in a lower chance of occupants opening the window. By enhancing our understanding of the relationship between these theoretical parameters, improved design strategies on the mediating parameters can be prioritized and communicated early in the building design phase leading to more informed design decisions.