中文摘要：

目的为有效利用监测系统大量冗余、互补数据，对结构的工作状态展开评估．方法运用粗集进行属性约简达到海量数据的降维工作，进而提取有效的特征参数，运用概率神经网络（PNN）良好的处理噪声等不确定信息及概率推理能力，进行推理计算和损伤识别．结果对某12层钢筋混凝土框架不同噪声水平下的三种损伤模式进行了识别，识别精度均在85％以上，并与PNN损伤识别方法进行了比较，其识别精度高于PNN．结论提出了一种基于粗集与PNN的结构损伤识别新方法，该方法不仅可以降低数据的空间维数，减少冗余属性和不确定性，而且可以提高损伤识别精度．

英文摘要：

How to make full use of the redundant and complementarg information and thus assess on structural work conditions has heen a difficult problem for researchers home and abroad, In order to efficiently solve this problem, a structural damage identification method based on rough and probabilistic neural network （PNN） was proposed in this paper. In this method, rough set was used to reduce attributes so as to decrease spatial dimensions of data and extract effective features;then PNN was utilized to process uncertain data and proceed probabilistic reasoning by PNN;and analysis and damage identification were achieved finally. To validate the efficiency of the proposed method, multi - damage patterns from a 12 - story reinforced concrete frame were identified, with all IA above 85 %, and a comparison was made between the proposed method and a PNN classifier without data processing by rough set. The results show that the proposed method can not only reduce data spatial dimension, redundant attributes and uncertainty but also improve damage identification accuracy.