基于IPSO-BP神经网络的结构损伤识别

Structural damage identification based on IPSO-BP neural network

为了准确评估结构健康状况,将改进的粒子群算法与BP算法有机结合来训练人工神经网络,并用于结构损伤识别.以国际结构控制协会与美国土木工程学会(IASC-ASCE)提出的健康监测第二阶段Bench-mark模型结构为例,对4种不同损伤模式进行了损伤定位.研究结果表明,在模型误差、测量噪声等因素的影响下,该方法能够取得令人满意的损伤识别结果.

In order to accurately evaluate the structural health condition, the improved particle swarm optimization（IPSO）algorithm and BP algorithm integrated organically is applied to train the artificial neural network and is used for structural damage identification. A benchmark problem proposed by the International Association for Structural Control and the American Society of Civil Engineering （IASC-ASCE） Task Group on Structural Monito- ring is investigated. The damage location for four different cases is estimated. Under the influence of the model error, measurement noise etc. , the damage identification results show that the presented method is satisfactory.