Stay Cable Tension Estimation of Cable-Stayed Bridges Using Genetic Algorithm and Particle Swarm Optimization

Abstract

This study presents a methodology for estimating stay cable tensions of cable-stayed bridges using genetic algorithm (GA) and particle swarm optimization (PSO). At first, a comprehensive cable model was used to represent the dynamic behavior of the cables. Second, an error function corresponding to the difference between the experimentally measured natural frequencies of the cable and the analytical natural frequencies (obtained using the preferred cable model) was introduced. GA and PSO were then used for minimizing the error function to estimate the cable tension. Because of the stochastic nature of evolutionary algorithms (EAs), results of the minimization problem were reported statistically over multiple runs. Following the proposed methodology, bending stiffness of the cable, sag extensibility, and the effect of the cable crossties (that are used to suppress the wind- and rain-induced vibration of cables) were considered. The accuracy of the proposed method was evaluated using simulation results of a tensioned cable and experimental results of a cable-stayed bridge.