基于相关向量机模型的混凝土综合性能预测

硅酸盐通报 ›› 2022, Vol. 41 ›› Issue (1): 118-125.

所属专题：水泥混凝土

基于相关向量机模型的混凝土综合性能预测

张研^1,2, 王鹏鹏², 吴哲康²

1.广西岩土力学与工程重点实验室,桂林 541004;
2.桂林理工大学土木与建筑工程学院,桂林 541004

收稿日期:2021-08-11 修订日期:2021-11-12 出版日期:2022-01-15 发布日期:2022-02-10
作者简介:张研(1983—),男,博士,副教授。主要从事结构工程、岩土工程方面的研究。E-mail:yanzi22858@126.com
基金资助:
国家自然科学基金(52068016);广西自然科学基金(2020GXNSFAA297118,2020GXNSFAA159125);水利工程岩石力学广西高等学校高水平创新团队及卓越学者计划(202006);广西岩土力学与工程重点实验室(桂科20-Y-XT-01)

Comprehensive Performance Prediction of Concrete Based on Relevance Vector Machine Model

ZHANG Yan^1,2, WANG Pengpeng², WU Zhekang²

1. Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering, Guilin 541004, China;
2. School of Civil and Architecture Engineering, Guilin University of Technology, Guilin 541004, China

Received:2021-08-11 Revised:2021-11-12 Online:2022-01-15 Published:2022-02-10

摘要/Abstract

摘要： 为快速获取及评价混凝土的综合性能,选取影响混凝土综合性能的6个主要因素为输入数据,混凝土综合性能(28 d强度、坍落扩展度及表观密度)为输出数据,建立基于相关向量机(RVM)的混凝土综合性能预测模型,对14组学习样本进行拟合训练,并对其余5组预测样本进行预测。结果表明:在相同的样本条件下,与BP神经网络模型进行对比,RVM模型预测精度更高,离散性更小;同时,与实际值相比,RVM模型预测的混凝土综合性能指标的平均相对误差均明显小于BP神经网络模型预测得到的平均相对误差,进一步验证了RVM模型对混凝土综合性能预测的可靠性,具有较好的推广价值。

Abstract: In order to quickly obtain and evaluate the comprehensive performance of concrete, a prediction model of the comprehensive performance of concrete was established based on the relevance vector machine (RVM), in which six main factors affecting the comprehensive performance of concrete were selected as input data and the comprehensive performances (28 d strength, slump extension and apparent density) of concrete were selected as the output data. Then the model was used to predict 5 groups of predicting samples through fitting traning of 14 groups of learning samples. The results show that under the same sample conditions, compared with BP neural network model, RVM model has higher prediction accuracy and less discreteness. Compared with the actual value, the average relative error of concrete comprehensive performance index predicted by RVM model is obviously smaller than that predicted by BP neural network model, which further verifies the reliability of RVM model to predict the comprehensive performance of concrete, and has good promotion value.

Key words: relevance vector machine, concrete, influence factor, mean relative error, comprehensive performance, predictive model

中图分类号:

TU528

张研, 王鹏鹏, 吴哲康. 基于相关向量机模型的混凝土综合性能预测[J]. 硅酸盐通报, 2022, 41(1): 118-125.

ZHANG Yan, WANG Pengpeng, WU Zhekang. Comprehensive Performance Prediction of Concrete Based on Relevance Vector Machine Model[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2022, 41(1): 118-125.

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基于相关向量机模型的混凝土综合性能预测

Comprehensive Performance Prediction of Concrete Based on Relevance Vector Machine Model

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