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.

用于外部加固的预应力电缆和用于拱桥的悬挂电缆可能会因钢丝的腐蚀或断裂而受损。在这些情况下，电缆的有效面积将减小，但是电缆力将保持几乎恒定，这限制了使用传统频域分析检测这种损坏的能力。由于缺乏对此类电缆的时域和频域特性，损伤指标和损伤量化的了解，因此可能无法及时发现隐藏的电缆损伤，从而可能威胁桥梁安全。为了解决这些问题，设计了一系列针对预应力电缆的性能实验。这些电缆对各种损坏程度的动态响应信号，获得了电缆力和电缆长度，并在时域，频域和能量域进行了分析。取决于测试，小波包总能量的变化率（确定RWE（）对电缆损坏敏感，并被选作电缆的损坏指标。通过带RWE的神经网络算法对损伤程度进行量化，最终建立了电缆损伤的预测程序。本文提出的外部电缆损伤检测方法将有助于电缆支撑桥梁的损伤评估和长期监测。