土石坝料压实特性改进多输出预测模型研究北大核心CSCD

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。

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

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.