Lifetime cost optimization of structures by a combined condition–reliability approach

Cost optimization for the determination of the most effective maintenance strategy of deteriorating structures has recently been used by researchers and accepted by experts. The main reasons for this lie in strict requirements of budgetary efficiency and in reliability requirements for structures. Accessible data directly related to structural performance include the condition state, obtained from generally stipulated visual inspections, and the reliability state, derived from the inspection or monitoring programs combined with numerical computations. In this paper, a novel approach to cost models for condition and reliability profiles is described. Global cost optimization can be achieved by means of multi-objective optimization or by means of a Cost-Optimized Condition-Reliability Profile (COCRP) approach. Since the assessment of structures is under uncertainty, it is essential to embed the COCRP concept in a full probabilistic framework. Probabilistic COCRP computations can be performed, even for complex structures, within a reasonable time by using advanced Monte Carlo Methods such as Latin Hypercube Sampling. The proposed COCRP approach allows realistic and efficient treatment of structures that involve uncertainties and detects the parameters having the most significant effects on lifetime cost.