基于各向异性混合核函数高斯过程回归的RC柱概率抗剪承载力模型
TU375.3; 针对钢筋混凝土(RC)柱抗剪承载力传统预测模型的非线性逼近能力不足且无法合理描述不确定性所存在的缺陷,提出一种基于各向异性混合核函数高斯过程回归的RC柱概率抗剪承载力预测模型.首先,基于核函数相加性和自动相关性,构造出一种新型的各向异性混合核函数;然后,结合高斯过程回归原理和各向异性混合核函数,建立了RC柱的概率抗剪承载力模型;进而采用极大似然估计法,确定了RC柱概率抗剪承载力模型的超参数;最后,基于91组剪切破坏RC柱的试验数据,通过与传统核函数形式和传统模型进行对比分析,验证了该模型的有效性.结果表明:与传统核函数相比,各向异性混合核函数的确定性预测指标均方根误差RMSE...
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| Published in | 工程科学与技术 Vol. 57; no. 1; pp. 287 - 295 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
中国电建集团 河南省电力勘测设计院有限公司,河南 郑州 450000%广西大学 土木建筑工程学院,广西 南宁 530004
2025
广西大学 土木建筑工程学院,广西 南宁 530004 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2096-3246 |
| DOI | 10.12454/j.jsuese.202300328 |
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| Abstract | TU375.3; 针对钢筋混凝土(RC)柱抗剪承载力传统预测模型的非线性逼近能力不足且无法合理描述不确定性所存在的缺陷,提出一种基于各向异性混合核函数高斯过程回归的RC柱概率抗剪承载力预测模型.首先,基于核函数相加性和自动相关性,构造出一种新型的各向异性混合核函数;然后,结合高斯过程回归原理和各向异性混合核函数,建立了RC柱的概率抗剪承载力模型;进而采用极大似然估计法,确定了RC柱概率抗剪承载力模型的超参数;最后,基于91组剪切破坏RC柱的试验数据,通过与传统核函数形式和传统模型进行对比分析,验证了该模型的有效性.结果表明:与传统核函数相比,各向异性混合核函数的确定性预测指标均方根误差RMSE和平均绝对误差MAE分别降低约16%和19%,概率性预测值指标负对数预测密度NLPD和平均标准化对数损失MSLL分别降低约15%和23%;与传统机器学习模型相比,本文模型的均方根误差RMSE和平均绝对误差MAE分别降低约38%和39%;根据所提出的概率模型能够建立概率密度函数曲线和置信区间,从而合理描述抗剪承载力的不确定性并校准分析传统模型的预测精度. |
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| AbstractList | TU375.3; 针对钢筋混凝土(RC)柱抗剪承载力传统预测模型的非线性逼近能力不足且无法合理描述不确定性所存在的缺陷,提出一种基于各向异性混合核函数高斯过程回归的RC柱概率抗剪承载力预测模型.首先,基于核函数相加性和自动相关性,构造出一种新型的各向异性混合核函数;然后,结合高斯过程回归原理和各向异性混合核函数,建立了RC柱的概率抗剪承载力模型;进而采用极大似然估计法,确定了RC柱概率抗剪承载力模型的超参数;最后,基于91组剪切破坏RC柱的试验数据,通过与传统核函数形式和传统模型进行对比分析,验证了该模型的有效性.结果表明:与传统核函数相比,各向异性混合核函数的确定性预测指标均方根误差RMSE和平均绝对误差MAE分别降低约16%和19%,概率性预测值指标负对数预测密度NLPD和平均标准化对数损失MSLL分别降低约15%和23%;与传统机器学习模型相比,本文模型的均方根误差RMSE和平均绝对误差MAE分别降低约38%和39%;根据所提出的概率模型能够建立概率密度函数曲线和置信区间,从而合理描述抗剪承载力的不确定性并校准分析传统模型的预测精度. |
| Abstract_FL | A probabilistic shear strength model for reinforced concrete(RC)columns is proposed based on Gaussian process regression(GPR)with an anisotropic mixed kernel function to address the limitations of traditional models,which often exhibit low nonlinear approximation ability and poor generalization performance.A new anisotropic mixed kernel function is developed using the additivity and autocorrelation properties of the Matern and Rational Quadratic kernel functions,while the automatic relevance determination function is introduced to account for the effects of various feature parameters.The probabilistic shear strength model for RC columns is established by integrating the anisotropic mixed kernel function with the Gaussian process regression algorithm.The posterior distribution of model hyperparameters is obtained using Bayesian infer-ence,and the hyperparameters of the probabilistic shear strength model are determined through the maximum likelihood estimation method.The effectiveness of the proposed model is validated by comparing it with traditional kernel functions,machine learning models,and mechanical mod-els using 91 sets of experimental data.The analysis results indicated that the deterministic prediction indices RMSE and MAE of the proposed mod-el are reduced by approximately 16%and 19%,respectively,compared to traditional kernel functions.In contrast,the probabilistic prediction in-dices NLPD and MSLL are reduced by about 15%and 23%,respectively.Compared to traditional machine learning models,the RMSE and MAE of the proposed model are decreased by 38%and 39%,respectively.The proposed model demonstrated high prediction accuracy and generalization per-formance and effectively quantified the uncertainty in the shear strength of RC columns. |
| Author | 喻泽成 李启明 余波 张鹏飞 |
| AuthorAffiliation | 广西大学 土木建筑工程学院,广西 南宁 530004;中国电建集团 河南省电力勘测设计院有限公司,河南 郑州 450000%广西大学 土木建筑工程学院,广西 南宁 530004 |
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| Author_FL | LI Qiming ZHANG Pengfei YU Bo YU Zecheng |
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| DocumentTitle_FL | Probabilistic Shear Strength Model of RC Columns Based on Gaussian Process Regression with Anisotropic Compound Kernel Function |
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| Keywords | 高斯过程回归 probabilistic shear strength model 钢筋混凝土柱 各向异性混合核函数 不确定性 概率抗剪承载力模型 reinforced concrete columns anisotropic compound kernel function Gaussian process regression uncertainty |
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| Publisher | 中国电建集团 河南省电力勘测设计院有限公司,河南 郑州 450000%广西大学 土木建筑工程学院,广西 南宁 530004 广西大学 土木建筑工程学院,广西 南宁 530004 |
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| Title | 基于各向异性混合核函数高斯过程回归的RC柱概率抗剪承载力模型 |
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