A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Community-Level Framework for Seismic Resilience. I: Coupling Socioeconomic Characteristics and Engineering Building Systems
AbstractThese two companion papers focus on the development of a coupled socioeconomic and engineering framework for community-level seismic resilience. The coupling of these two systems is used to enhance risk-informed decision making for selection of a community-level seismic retrofit plan. This first article, Part I, describes the coupled framework development, including the quantification of the effect that six socioeconomic and demographic variables—including age, ethnicity/race, family structure, gender, socioeconomic status, and the age and density of the built environment—have on four resilience metrics. Empirical data collected after previous earthquakes were used to determine relationships among these six variables and the vulnerability of the population, as understood through assessing three morbidity rates: the probabilities of injury, fatality, and posttraumatic stress disorder (PTSD) diagnosis. Prior to this study, the emotional health of the population has not been considered as an engineering metric, although social science research has established that this is one significant measure of community recovery. Initial cost, economic loss, number of morbidities, and recovery time were used as the four metrics for measuring community resilience. Part I concludes with a sensitivity study on the six variables. Based on the sensitivity study, low socioeconomic status was the highest contributor to injury and fatality, whereas a family structure with persons under 18 years old living in the household was the highest contributor to predicted PTSD diagnosis. Overall for all three morbidity rates, socioeconomic status was a higher predictor compared to ethnicity/race, and having a high percentage of females in the population caused increases in predicted morbidities. The decision-making algorithm, optimization, and several illustrative examples on Los Angeles County, California, are provided in Part II, the companion paper.
Community-Level Framework for Seismic Resilience. I: Coupling Socioeconomic Characteristics and Engineering Building Systems
AbstractThese two companion papers focus on the development of a coupled socioeconomic and engineering framework for community-level seismic resilience. The coupling of these two systems is used to enhance risk-informed decision making for selection of a community-level seismic retrofit plan. This first article, Part I, describes the coupled framework development, including the quantification of the effect that six socioeconomic and demographic variables—including age, ethnicity/race, family structure, gender, socioeconomic status, and the age and density of the built environment—have on four resilience metrics. Empirical data collected after previous earthquakes were used to determine relationships among these six variables and the vulnerability of the population, as understood through assessing three morbidity rates: the probabilities of injury, fatality, and posttraumatic stress disorder (PTSD) diagnosis. Prior to this study, the emotional health of the population has not been considered as an engineering metric, although social science research has established that this is one significant measure of community recovery. Initial cost, economic loss, number of morbidities, and recovery time were used as the four metrics for measuring community resilience. Part I concludes with a sensitivity study on the six variables. Based on the sensitivity study, low socioeconomic status was the highest contributor to injury and fatality, whereas a family structure with persons under 18 years old living in the household was the highest contributor to predicted PTSD diagnosis. Overall for all three morbidity rates, socioeconomic status was a higher predictor compared to ethnicity/race, and having a high percentage of females in the population caused increases in predicted morbidities. The decision-making algorithm, optimization, and several illustrative examples on Los Angeles County, California, are provided in Part II, the companion paper.
Community-Level Framework for Seismic Resilience. I: Coupling Socioeconomic Characteristics and Engineering Building Systems
Peek, Lori (author) / Sutley, Elaina J / van de Lindt, John W
2017
Article (Journal)
English