Abstract
An approach to identify damage of bridge utilizing modal flexibility and neural network optimized by particle swarm optimization (PSO) is presented. The method consists of two stages; modal flexibility indices are applied to damage localizing and neural network optimized by PSO is used to identify the damage severity. Numerical simulation of simply supported bridge is presented to demonstrate feasibility of the proposed method, while comparative analysis with traditional BP network is for its superiority. The results indicate that curvature of flexibility changes can identify damages with both single and multiple locations. The optimization of bias and weight for neural network by fitness function of PSO algorithm can realize favorable damage severity identification and possesses more satisfactory accuracy than traditional BP network.
Highlights
As important components of transportation infrastructure, bridges are essential for normal operation of transportation system
artificial neural networks (ANNs) are optimized by particle swarm optimization (PSO) for damage severity identification and the modal flexibility changes are treated as input variables
We have proposed a two-stage strategy based on modal flexibility and neural network optimized by PSO for damage location and severity identification
Summary
As important components of transportation infrastructure, bridges are essential for normal operation of transportation system. A number of methods have been proposed in the past two decades to detect and assess the damage condition of bridge Nondestructive methods such as ultrasonic waves, X-ray, and stress waves have been widely applied in practice considering their convenience and simplicity. Damage can be detected based on changes in natural frequencies and mode shapes, which can be regarded as global methods. Reynders and de Roeck [12] proposed a local flexibility-based approach which allowed determining the local stiffness variations directly from modal properties It was verified by numerical simulation of damaged isostatic and hyperstatic beam and experiments of a reinforced concrete beam. ANNs are optimized by PSO for damage severity identification and the modal flexibility changes are treated as input variables. Numerical simulation is used to verify its feasibility of the proposed method
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