边坡稳定性的神经网络估计

NEURAL NETWORK ESTIMATION OF SLOPE STABILITY

应用神经网络理论，本文提出了圆弧破坏和楔体破坏的边坡安全系数估计的新方法。为解决安全系数估计的知识的学习问题，提出了一种推广学习算法。用它对收集到的边坡实例进行学习，然后进行推广，预测出新边坡的安全系数。与极限平衡法和极大似然法的估计结果进行了比较，可以看出，神经网络方法具有推广预测精度高、自学习功能强、考虑不确定性能力强等特点。

With application of neural network theory, a new method has been proposed for direct estimation of the safety factor for circular and wedge failure of slopes. Furthermore, a new algorithm called generalized learning algorithm is designed to learn better knowledge of estimation of the safety factors of slopes from cases. The learned knowledge is then used for extrapolating prediction of the safety factor of new slope, Compared with the safety factors predicted by the limit equilibrium and maximum likelihood method, the neural network method has high accouracy of extrapolating prediction and high ability of self-learning and processing uncertainty.