Serviceability reliability analysis of prestressed concrete bridges

Abstract

An effective method for serviceability reliability analysis of prestressed concrete bridges is presented in this paper. The method integrates the advantages of the Artificial Neural Network (ANN) method, First Order Reliability Method (FORM) and the importance sampling updating method. A distinctive feature of the present method is the introduction of an explicit approximate limit state function. The explicit formulation of the approximate limit state function is derived by using the parameters of the developed ANN model. Once the explicit limit state function is obtained, the failure probability can be easily estimated by using a hybrid reliability method consisting of the FORM and the importance sampling updating method. The accuracy and efficiency of the robust method is demonstrated through two numerical examples. As a practical engineering example, the serviceability reliability of a prestressed concrete bridge is illustrated. The obtained results clearly show the applicability and merits of the present method.

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