基于神经网络的损伤构件及损伤程度识别

确定损伤构件及其损伤程度是分阶段损伤识别的最后一步,同时又是进一步制定结构安全运行决策的前提和基础。研究了在确定了结构损伤区域的条件下,应用反向传播(BP)神经网络同时实现对具体损伤构件及其损伤程度识别的方法。探讨了针对上述神经网络训练数据的构造和训练策略。应用提出的方法对汲水门斜拉桥桥面结构进行了损伤识别仿真模拟。基于模态参数对损伤的灵敏度分析,选取了12个自振频率和损伤区域附近的6个振型分量作为构造网络输入的基本数据。网络的输出向量同时指示了损伤构件及其损伤程度。就模拟的10种损伤情况,当损伤程度达到60%以上时,有9种实现了正确的构件识别,半数以上给出了可以接受的损伤程度描述。

IDENTIFICATION OF DAMAGED MEMBERS AND DAMAGE EXTENT IN BRIDGE DECK BY NEURAL NETWORK

Determination of a damaged structural member and its damage extent is the last step of structural damage identification, and it is also the fundamental work for further decision making for structural safety. A method for identifying the damaged member and damage extent simultaneously by a back-propagation neural network is investigated. The training data construction and training strategy for the network are proposed. By taking the cable-stayed Kap Shui Mun bridge as an example, the method is demonstrated. On the basis of sensitivity analysis of modal parameters to damage, 12 natural frequencies and 6 components of mode shapes are selected as the basic data to configure the input vector of the network. The output vector of the network is the indicator of both damaged members and damage extent. When the damage extent is larger than 60%, 9 of 10 cases simulated are identified correctly for damaged member and more than half of cases are quantified acceptably for damage extent.