Estimation of cable safety factors of suspension bridges using artificial neural network-based inverse reliability method

The design of the main cables of suspension bridges is based on the verification of the rules defined by standard specifications, where cable safety factors are introduced to ensure safety. However, the current bridge design standards have been developed to ensure structural safety by defining a target reliability index. In other words, the structural reliability level is specified as a target to be satisfied by the designer. Thus, calibration of cable safety factors is needed to guarantee the specified reliability of main cables. This study proposes an efficient and accurate algorithm to solve the calibration problem of cable safety factors of suspension bridges. Uncertainties of the structure and load parameters are incorporated in the calculation model. The proposed algorithm integrates the concepts of the inverse reliability method, non-linear finite element method, and artificial neural networks method. The accuracy and efficiency of this method with reference to an example long-span suspension bridge are studied and numerical results have validated its superiority over the conventional deterministic method or inverse reliability method with Gimsing’s simplified approach. Finally, some important parameters in the proposed method are also discussed. Copyright q 2006 John Wiley & Sons, Ltd