Abstract:
A problem often arising in engineering applications of computer models is to determine the importance of each data item in the large pool of required input factors. This paper explores a statistical approach for investigating factor sensitivities. The methodology is demonstrated with the HDM-III highway life-cycle cost analysis model. Specifically, the net present value (NPV) of life-cycle costs predicted by the HDM-III model is analyzed, and sensitivities of NPV to the link characterization input factors are investigated. In the statistical designed experiment, combinations of the input factors are chosen using Latin hypercube sampling, a method well suited to the deterministic HDMIII model. Two analyses of the output data are performed, based on a first-order regression approximation and on a Gaussian stochastic-process model. For NPV, the factor rankings are similar, but the sensitivities obtained from the two techniques show some marked differences. This demonstrates the greater flexibility of the stochasticprocess model in dealing with nonlinearities and factor interactions in complex inputoutput relationships.
Keywords:
Computer experiment, data needs, life-cycle cost, maintenance and rehabilitation.
tr75.pdf