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中文摘要：
目前对大坝裂缝的监控主要是应用统计模型，然而在实际应用中，其分析精度易受因子相关性的影响．本研究提出了基于RS—BP耦合的裂缝监控模型，该方法首先应用RS对裂缝监控信息进行属性和样本集约简，以提高网络的泛化分析能力；然后用BP神经网络对约简的样本集进行模式训练；最后根据训练好的网络对裂缝进行分析，以掌握裂缝的演变性态．计算结果表明，该方法计算效率和精度均较高，适用于分析裂缝等高度非线性问题．
英文摘要：
Crack is one of the main damages of concrete dams. Statistical model is the main monitoring model for crack nowadays, but its precision is easy to be interfered by associated factors in actual application. A new crack monitoring model was put forward based on weak coupling of rough set （RS） and back propagation neural network （BP-NN）. Firstly, RS was used to reduce samples and attributions of crack observation data so as to improve the analysis ability of NN. Secondly, the reduced samples were trained by BP-NN. Lastly, the trained NN was utilized to analyze the crack evolution and grasp its behavior. A case study showed that the proposed method is better in efficiency and precision, and is suitable for the analysis of highly nonlinear problems such as crack, etc.
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