The U.S. Department of Transportation is responsible for implementing new safety improvements and regulations with the goal of ensuring limited funds are distributed to where they can have the greatest impact on safety. In this work, we conduct a study of new regulations and other reactions (such as recalls) to fatal accidents in several different modes of transportation implemented from 2002 to 2009. We find that in the safest modes of commercial aviation and bus transport, the amount of spending on new regulations is high in relation to the number of fatalities compared to the regulatory attention received by less safe modes of general aviation and private automobiles. Additionally, we study two major fatal accident investigations from commercial aviation and two major automotive recalls associated with fatal accidents. We find differences in the cost per expected fatality prevented for these reactions, with the airline accident investigations being more cost effective. Overall, we observe trends in both the automotive and aviation sectors that suggest that public transportation receives more regulatory attention than private transport. We also observe that the types of safety remedies utilized, regulation versus investigation, have varying levels of effectiveness in different transport modes. We suggest that these differences are indicative of increased public demand for safety in modes where a third party may be held responsible, even for those not participating in the transportation. These findings have important implications for the transportation industry, policymakers, and for estimating the public demand for safety in new transport modes.

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The focus of this work is on uncertainty characterization, sensitivity analysis, uncertainty propagation, and extreme-case analysis. To deal with the computationally expensive and complex NASA problem, a simpler toy problem is devised to mimic the NASA problem for which the true results were known. The toy problem helped in thoroughly testing the current methods and their repeatability. For uncertainty characterization, a novel cumulative density function matching method is proposed, which gave similar results as a standard Markov chain-Monte Carlobased Bayesian approach. An efficient reliability reanalysis-based probability-box sensitivity analysis method is employed to identify the most sensitive parameters to the risk analysis metrics. Uncertainty propagation to find extreme values for the risk analysis metrics is done using a single-loop efficient reliability reanalysis-based method. A modified version of the efficient reliability reanalysis is proposed that uses self-normalizing weights and caps on the weights; this is referred to as a capped self-normalizing efficient reliability reanalysis. This method showed considerably better performance at estimating risk analysis metrics for this application as compared to the generic efficient reliability reanalysis. The use of efficient reliability reanalysis was dictated by the cost of the black-box functions provided by NASA.Nomenclature D = modified Kolmogorov-Smirnov statistic F = cumulative density function f = probability density function g = requirement metrics I = indicator function J 1 = expected value of worst-case requirement metric J 2 = probability of failure n = given number of observations n 0 = number of aleatory samples for generating an empirical cumulative density function p = uncertain random variables q = sampling probability density function for efficient reliability reanalysis w = worst-case requirement metric x = intermediate variables θ= subparameters (epistemic uncertainty) Θ = vector of subparameters

Manufacturing tolerance allocation is a design challenge that plays an important role in balancing the cost and weight objectives for an aircraft. The purpose of this paper is to explore an approach to optimize manufacturing tolerances by combining the individual objectives of the quality, manufacturing and design teams. We illustrate this approach on a fatigue critical lap joint structure that consists of a wing spar and a strap that must tolerate the manufacturing errors associated with location and size of the fastener holes. These errors are modeled with industrial data collected from the wing assemblies of a business jet. A cost model is formulated in terms of the quality cost, manufacturing cost and performance cost, and optimal tolerance is found by minimizing the sum of these costs (i.e. total cost). It was found that as the aircraft size grows bigger the weight increases more quickly than the quality cost requiring the use of tighter tolerance. A sensitivity analysis is also performed to identify the input variables that have significant impact on the optimal tolerance and corresponding total cost. Nomenclatured = Fastener hole diameter e = Fastener edge distance h = Spar height L = Spar length nf = Total no. of fasteners nf-pf = Total no. of fasteners per foot PQN = Probability of quality notification PCV = Probability of constraint violation t = Thickness T = Tolerance w = Width w 0 = Zero tolerance width W = Weight Iini = Initial inspection interval I*ini = Initial inspection constraint C = Cost ET = Engineering time LT = Labor time EC = Engineering cost (hourly) LC = Labor time cost (hourly) λ = Tradeoff ratio