Structural Performance Updating and Optimization with Conflicting Objectives under Uncertainty

Abstract

Managers of civil infrastructure aim at reaching the best balance between life-cycle performance of structures and the necessary maintenance, repair and replacement costs. However, long time prediction of performance, considering the effects of deterioration and maintenance actions, can not be very accurate, and decisions must be made considering the uncertainties in initial performance, deterioration, and time of application and effects of maintenance actions. The uncertainties in performance can be reduced through inspection, non-destructive testing or load tests. However, information from these sources is not completely accurate and must be combined with existing information, in order to obtain reliable posterior information and predict more accurately future deterioration. In this paper, the application of maintenance actions on existing bridges is optimized in order to obtain maximum performance and minimum life-cycle maintenance cost. Performance is defined in terms of the condition index, which describes the effects of deterioration as can be seen by an inspector, and the safety index, which measures the safety margin of the structure. Constraints are considered for both the condition and the safety index over the entire lifetime. Several maintenance actions are considered, each defined by probabilistic effects on performance, times of application and costs of applications. The reduction in uncertainty associated with periodical inspections is considered through updating of performance profiles. Considering multiple objective functions as well as the complexity of the performance profiles under maintenance, genetic algorithms are combined with Monte-Carlo simulation, in order to obtain a set of Pareto solutions. Examples of application are presented, showing the effects of inspection and updating on the optimization of maintenance strategies.