Life-Cycle Cost Evaluation Strategy for High-Performance Control Systems under Uncertainties

Abstract

High-performance control systems (HPCSs), including active, hybrid, and semiactive control strategies, can perform over a wide excitation bandwidth and are therefore good candidates for multihazard mitigation. However, the number of HPCS applications in the field is very limited. This is likely due to the perceived higher costs of installation, maintenance, possible malfunction, and lack of tools to financially justify their implementation. Such financial justifications could be conducted through life-cycle cost (LCC) analysis, but would result in a computationally demanding task due to the very large number of simulations required given the large number of uncertainties. In this paper, two sets of methods for conducting LCC analyses are compared and their performance is assessed as a function of LCC estimation accuracy and computational requirements. The first set is based on deterministic scenarios and on the simulation of all possible scenarios; it is termed “what-if analysis.” Variations of the what-if method are investigated, where the simulations are conducted only for the most likely scenarios, termed “most-likely (ML)” analysis. The second set is based on stochastic scenarios and on Monte-Carlo (MC) analysis. Variations of the MC method are investigated, one based on the coefficient of variation of output data and one proposed by the authors based on the convergence of the estimated costs, termed bounded MC. A demonstration of the LCC analysis methodology is conducted where an HPCS is used for the mitigation of seismic-induced vibrations on a five-story structure. Uncertainties under consideration include sensor failure, mechanical wear, tear, and seismic events. Results are compared against the uncontrolled structure and a passive viscous strategy, and demonstrate that (1) the LCC methodology can be used to financially justify the utilization on an HPCS; and (2) the bounded MC method leads to accurate cost estimations using a lower number of simulations.