混凝土坝安全监控最小二乘支持向量机模型

Monitoring Model for Concrete Dam Safety Using Least Square Support Vector Machine

传统的混凝土坝安全监控模型难以精确反映大坝变形的非线性变化规律,一定程度上影响模型的预测效果。基于统计学习理论的支持向量机,采用结构风险最小化准则,具有结构简单、理论完备、适应性强、全局优化、训练时间短、泛化性能好等优点。将最小二乘支持向量机应用于大坝安全监控领域,建立了混凝土坝的支持向量机监控模型。工程案例证明,该模型精度较高,具有广泛的实用性。

Traditional safety monitoring model cannot accurately hence affecting the forecast result. The support vector machine is based on the statistical learning theory and adopts the structural risk minimization principle which has advantages of simple structure, complete theory, good adapta-bility, global optimization, short training time, and good generalization performance. Least square support vector machine （LS-SVM） was applied to concrete dam safety monitoring, and a corresponding monitoring model was built. Engineering cases prove that the model is of high precision and practicality.