考虑门限效应的面板堆石坝变形特性改进支持向量机预测模型研究

Improved Support Vector Machine Prediction Model for Deformation Behavior of Concrete Face Rockfill Dams Considering Threshold Effect

为了支撑大坝优化设计和安全评价,面板堆石坝设计和建设过程中通常要求准确评估大坝的变形特性.变形预测和控制是面板堆石坝建设过程中面临的关键问题.结合门限回归和改进支持向量机算法,建立考虑多因素的面板堆石坝典型变形特性智能预测模型.首先收集87个面板堆石坝工程实例实测数据.在统计综述大坝典型变形规律的基础上,基于多元线性回归理论阐明大坝3个典型变形指标与6个影响因素之间的相互关系,揭示影响面板堆石坝变形的主要因素.考虑实例数据非线性突变和离散性特点,采用门限回归理论按照坝高对实例变形数据进行区间聚类划分.在此基础上,构造一种自适应混合核函数,采用粒子群智能优化算法确定支持向量机主要参数,在不同坝高聚类区间内建立改进支持向量机预测模型.该模型与已有预测模型结果对比分析表明,该模型具有较高的预测精度,可以实现面板堆石坝典型变形特性的准确预测.

In order to support dam safety evaluation and optimization design,deformation behaviors are usually required to be quickly estimated in the design and construction process of the concrete face rockfill dam(CFRD).Deformation evaluation and control are key issues in the CFRDs construction.This paper combines threshold regression(TR)and improved support vector machine(SVM)algorithm to establish intelligent prediction models for CFRDs typical deformation behaviors.Firstly,the measured data of 87 CFRDs are collected.Based on the statistical review of the typical deformation behavior,the mathematical relationship between three typical deformation indexes and six influencing factors is established adopting multiple linear regression theory.The main influence factors are analyzed.Considering the characteristics of non-linear mutation and discreteness of the case data,multivariate TR theory is used to cluster the deformation data according to the dam height.A hybrid weight coefficient is introduced to construct an adaptive hybrid kernel function and the particle swarm optimization algorithm is used to determine main parameters of the SVM.Then the improved SVM prediction model is established in the clustering interval of different dam heights.The comparative analysis of the prediction results of the established model and the existing prediction models shows that the model has good prediction accuracy and can be used to accurately predict CFRD typical deformation behaviors.