Optimising intelligent control of a highway bridge with magnetorheological dampers

The back-propagation neural network is the most commonly used neural network for designing intelligent control systems for highway bridges. To improve the stability of an intelligent controller for a bridge with magnetorheological dampers and to reduce energy consumption, optimal performance was achieved by rationalising the number of hidden-layer elements and using a genetic algorithm (GA). Simulation results showed that a GA can achieve better optimisation results than those of simply optimised hidden-layer elements and can reduce energy consumption effectively.