A 2004 Skidmore Owings and Merrill report (in Simiu E. (2011) , Appendix 5, Wiley, Hoboken, NJ) notes that the ASCE 7 Standard (American Society of Civil Engineers (2002) ASCE 7-02, Reston, Va) is incomplete insofar as it provides no guidance on wind load factors appropriate for use with the Standard's wind tunnel procedure. The purpose of this paper is to contribute to such guidance. Based on a classical definition of wind load factors as functions of uncertainties in the micrometeorological, wind climatological, aerodynamics and structural dynamics elements that determine wind loads, the paper presents a simple, straightforward approach that allows practitioners to use appropriate wind load factors applicable when those uncertainties are either the same as or different from those assumed in the development of the ASCE 7 Standard. Illustrations of the approach are presented for a variety of cases of practical interest. In estimating design wind loads, the various uncertainties should not be accounted for in isolation, for example by specifying peak pressure coefficients with percentage points higher than those corresponding to their expected values. Rather, to achieve risk-consistent designs, the uncertainties should be accounted for collectively, in terms of their joint effect on the design wind loading. The design wind effect is equal to the estimated expectation of the peak wind effect times a load factor that, in most cases, is not significantly different from the load factor explicitly or implicitly specified in the ASCE 7 Standard. Notably, the load factor is not affected significantly by errors associated with interpolations required in typical Database Assisted Design applications. However, if the available wind speed records are several times shorter than, say, 20 to 30 years, the wind load factors increase by amounts of the order of 15 %.

Estimating the economic burden of disasters requires appropriate models that account for key characteristics and decision making needs. Natural disasters in 2011 resulted in $366 billion in direct damages and 29,782 fatalities worldwide. Average annual losses in the US amount to about $55 billion. Enhancing community and system resilience could lead to significant savings through risk reduction and expeditious recovery. The management of such reduction and recovery is facilitated by an appropriate definition of resilience and associated metrics with models for examining the economics of resilience. This paper provides such microeconomic models, compares them, examines their sensitivities to key parameters, and illustrates their uses. Such models enable improving the resiliency of systems to meet target levels.