Damage detection method for cables based on the change rate of wavelet packet total energy and a neural network

The prestressed cables used for external strengthening and the suspender cables used for arch bridges may suffer damage resulting from the corrosion or fracture of steel wires. Under these scenarios, the effective areas of the cables will decrease, but the cable forces will remain almost constant, which limits the ability to detect this damage with traditional frequency domain analysis. Due to the lack of understanding of the time and frequency domain characteristics, damage indexes and damage quantification for this kind of cable, hidden cable damage may not be detected in time, which can threaten bridge safety. To solve these problems, a series of performance experiments for prestressed cables were designed. The dynamic response signals of these cables to various damage levels, cable forces and cable lengths were obtained and analysed in the time domain, frequency domain and energy domain. Depending on the test, the change rate of wavelet packet total energy (RWE) was determined to be sensitive to cable damage and was chosen as the damage index for the cables. The damage level was quantified by a neural network algorithm with RWE, and a prediction procedure for cable damage was finally established. The damage detection method for external cables proposed in this paper will aid in the damage assessment and long-term monitoring of cable-supported bridges.