基于支持向量机的单桩竖向极限承载力预测

STUDY ON PREDICTION METHOD OF VERTICAL ULTIMATE BEARING CAPACITY OF SINGLE PILE BASED ON SUPPORT VECTOR MACHINE

提出了一种基于支持向量机理论的单桩竖向极限承载力预测方法,该方法以统计学理论为基础,避免了神经网络结构设计的盲目性和局部最优等非线性优化问题。仿真试验表明,它比基于混沌优化-神经网络的收敛速度快,预测精度高。

A new method based on support vector machine for vertical ultimate bearing capacity of single pile is presented. SVM algorithm is based on statistical theory and avoids the blindness of framework designs for neural network and the problem of nonlinear optimizations, such as local optimization. Analysis of the experimental results shows that the convergence speed of support vector machine is faster and its prediction result is more accurate than that of chaos optimization-neural network.