土石坝料压实特性改进多输出预测模型研究

Study on improved multi-output prediction model for compaction characteristics of earth-rock dam materials

土石坝料压实特性对保证大坝施工质量至关重要。然而,当前坝料压实特性预测主要是对物理、力学和渗透压实特性的单输出回归预测,缺乏对各压实特性目标间相关性的考虑。针对上述问题,提出土石坝料压实特性的改进多输出高斯过程回归(IMO-GPR)预测模型。采用具有噪声的基于密度的聚类方法构建目标特定特征,对多输出高斯过程回归(MO-GPR)模型原始输入空间进行特征扩展,提高模型高维特征空间复杂映射关系解耦能力;同时,结合MO-GPR模型中的输出协方差系数矩阵,实现对多输出压实特性目标间相关性的有效考虑,以最终实现多输出压实特性精确预测。相比传统的高斯过程回归(GPR)、多输出极限学习机(MO-ELM)和MOGPR模型,所提IMO-GPR模型的预测精度分别提高了24%、20%和17%,且对噪声干扰、数据异常、数据量少等情况具有更强的鲁棒性,为土石坝料压实特性分析提供了新思路。

The characteristics of earth-rock dam compaction are crucial to construction quality.Previous predictions mainly focused on the single-output regression of the physical,mechanical and seepage compaction characteristics,lacking consideration of the correlation among the objectives of different compaction characteristics.To address this issue,we develop an improved multi-output Gaussian process regression(IMO-GPR)model that builds target-specific features using density-based spatial clustering of applications with noise and extends the MO-GPR model input space to improve its decoupling capability of complicated mapping in the high-dimensional feature space.This improved model considers effectively the correlation between multi-output compaction characteristic objectives through combining with the output covariance coefficient matrix used by MO-GPR,and can realize accurate predictions of multi-output dam material compaction characteristics.Compared with traditional GPR,MO-ELM,and MO-GPR models,its prediction accuracy is 24%,20%and 17%higher respectively,and it has stronger robustness in the cases of noise interference,abnormal data,and insufficient data.