Error analysis for approximate structural life-cycle reliability and risk using machine learning methods

Abstract

Life-cycle management under uncertainty relies on the determination of life-cycle failure probability and risk. Usually, both life-cycle failure probability and risk are approximated by adding up the annual quantities during the service life of a structure. This approximation is based on the assumptions of (a) low annual failure probabilities and (b) independence of annual failure events. As a structure continuously deteriorates over time, both assumptions are likely to be violated. Therefore, it is crucial to investigate the error associated with this approximation so that the downstream management decisions such as inspection planning and maintenance optimization can be well grounded. In this paper, the error of approximate life-cycle failure probability and risk is analyzed considering different distribution types of structural capacity and demand as well as various deterioration mechanisms. Several machine learning methods are used for this purpose. Specifically, conditions under which the error is acceptable are identified in qualitative analysis using patient rule-induction method (PRIM). Quantitative error analysis based on polynomial and support vector regression is conducted to develop error correction techniques suitable for life-cycle analysis and management. The results of the error analysis are applied in the life-cycle analysis of a deteriorating structure to demonstrate the magnitude of approximation error and the importance of error correction.