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As one of the serviceability limit states of structural design, excessive vibration has attracted more attention in recent years, with the design trend moving toward lighter and more slender structures. Footfall vibration contains high uncertainties in nature, with significant variations in walker weight, walking speeds, and dynamic load factor. Since conservative designs can often lead to significant cost premiums, this study focuses on the stochastic assessment of footfall vibration of on a composite steel floor to better understand the variation in performance of various design factors and better inform the ultimate decision-makers. To close the knowledge gap between academia and industry in this area, San Francisco State University and the University of South Carolina partnered with Arup through an NSF-funded Research Experience for Undergraduates (REU) program. A composite steel structure was modeled to resemble a typical office bay. The model was developed and analyzed in Oasys GSA. Monte Carlo simulation was used to quantify the probability of exceeding certain common vibration criteria. The results of this study would provide actionable guidance to stakeholders to weigh the benefits and costs between performance targets. 

Wildfire risk is a defining environmental challenge throughout much of the American West, as well as in other regions where complex social and ecological dynamics defy simple policy or management solutions. In such settings, diverse forms of land use, livelihoods, and accompanying values provide the conditions for trade-offs (e.g. between protecting homes from uncontrollable fires and restoring low-severity fire to ecosystems as a natural disturbance process). Addressing wildfire risk requires grappling with these trade-offs at multiple levels—given the need for action by individuals as well as by large and diverse stakeholder groups—and under conditions of considerable complexity. We evaluated how individual and collective perception of trade-offs varies as a function of complexity through analysis of the cognitive maps—representations of perceived causal relationships among factors that structure an individual’s understanding of a system—of 111 stakeholders in the Eastern Cascades Ecoregion of central Oregon. Bayesian statistical analysis revealed a strong tendency against perception of trade-offs in individual maps, but not in a collective map that resulted from the aggregation of all individual cognitive maps. Furthermore, we found that lags (the number of factors that mediated the effect of an action on multiple valued outcomes) limited perception of trade-offs. Each additional intervening factor decreased the likelihood of a trade-off by approximately 52% in individual cognitive maps and by 10% in the collective cognitive map. However, the heterogeneity of these factors increased the likelihood of perception of trade-offs, particularly among individual cognitive maps, for which each unit increase of the Shannon diversity index translated into a 20-fold increase in the likelihood of perception of trade-offs. Taken together, these results suggest that features of complexity have distinct effects on individual—and collective-level perception of trade-offs. We discuss implications for wildfire risk decision-making in central Oregon and in other complex wildfire-prone social-ecological systems.