System reliability assessment of cable-stayed bridge assisted with GA-SVM learning during full lifetime

Abstract

The multi-component long-span bridges are under high-stress state and they are suffering the aging damage caused by adverse environments and loading during the full lifetime. This paper presents a machine learning framework for assessing the reliability of cable-stayed bridge systems. The support vector machine (SVM) method is adopted as a substitute for the complex structural finite element model in reliability analysis. In addition, the GA method is utilized for searching the extreme values of the constrained optimization function, which is used for calculating the time-variant reliability indexes. The β-bound method is adopted to identify failure modes of structural system. The time-variant influence on system failure mode is considered in this framework. A long-span prestressed concrete cable-stayed bridge in service is proposed to illustrate the proposed framework. The results show that the reliability indexes of beams and cables generally decrease with increasing years of service. Specially, beam reliability decreases significantly as the distance from the tower increases, while cable reliability remains relatively consistent. The system reliability indexes indicate that the bridge is relatively safe before degradation and relatively dangerous after 20 years’ service. Therefore, it is imperative to develop an appropriate maintenance strategy based on failure patterns of key components.