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Fatigue of steel bridges is a major concern for bridge engineers. Previous fatigue-based weight-limiting method of steel bridges is founded on the Palmgren–Miner’s rule and S-N curves, which overlook the effect of existing cracks on the fatigue life of in-service steel bridges. In the present study, based on the theory of linear elastic fracture mechanics, a framework combining the artificial neural networks and Monte Carlo simulations is proposed to analyze the fatigue reliability of steel bridges with the effects of cracks and truck weight limits considered. Using the framework, a new method of setting the gross vehicle weight limit for in-service steel bridges with cracks is proposed. The influences of four key parameters, including the average daily truck traffic, the gross vehicle weight limit, the violation rate, and the detected crack size, on the fatigue reliability of a steel bridge are analyzed quantitatively with the new framework. Results show that the suggested framework can enhance the fatigue reliability assessment process in terms of accuracy and efficiency. The method of setting gross vehicle weight limits can effectively control the fatigue failure probability to be within 2.3% according to the desired remaining service time and the detected crack size.

ANN and LEFM-Based Fatigue Reliability Analysis and Truck Weight Limits of Steel Bridges after Crack Detection