CFG桩复合地基承载力预测的高斯过程模型

Gaussian process model for forecasting bearing capacity of composite foundation with CFG pole

高斯过程是新近发展的一种机器学习方法,对处理复杂非线性问题具有很好的适应性。针对CFG桩复合地基承载力难以合理确定的问题,提出了基于高斯过程的CFG桩复合地基承载力预测模型。该模型通过对少量训练样本的学习,就可以建立CFG桩复合地基承载力与其影响因素之间的复杂非线性映射关系。将模型应用于工程实例,研究结果表明,CFG桩复合地基承载力预测的高斯过程模型是科学可行的。高斯过程模型的预测精度高,适用性强,具有算法参数自适应化的特点且易于实现,具有良好的工程应用前景。

Gaussian Process（GP） is a newly developed machine learning technology and has become a power tool for solving highly nonlinear problems.Aiming at the fact that it is still difficult to reasonably determine the bearing capacity of composite foundation with Cement-flyash-gravel（CFG） pile,the model based on GP is proposed for forecasting bearing capacity of composite foundation with CFG pile.According to few training samples,the nonlinear mapping relationship between bearing capacity of composite foundation with CFG pile and influencing factors is established by GP model.GP model is applied to a real engineering.The results of case study show that GP method is feasible,effective and simple to implement.It has merits of self-adaptive parameters determination and excellent capacity for solving non-linear small samples problems.The good performance of GP model makes it very attractive for application in the foundation engineering.