一种改进的支持向量回归的混凝土强度预测方法

硅酸盐通报 ›› 2021, Vol. 40 ›› Issue (1): 90-97.

所属专题：水泥混凝土

一种改进的支持向量回归的混凝土强度预测方法

曹斐¹, 周彧², 王春晓², 任梦宇¹, 周峰¹

1.中国地质大学(武汉)机械与电子信息学院,武汉 430074;
2.中建三局工程设计有限公司,武汉 430074

收稿日期:2020-08-07 修回日期:2020-09-07 出版日期:2021-01-15 发布日期:2021-02-07
通讯作者: 周峰,博士,副教授。E-mail:zhoufeng617@163.com
作者简介:曹斐(1998—),女,硕士研究生。主要从事人工智能方面的研究。E-mail:caofei@cug.edu.cn
基金资助:
国家自然科学基金(41974165);中建三局研发课题(CSCEC3B-2020-23)

An Improved Support Vector Regression Method forConcrete Strength Prediction

CAO Fei¹, ZHOU Yu², WANG Chunxiao², REN Mengyu¹, ZHOU Feng¹

1. School of Mechanical Engineering and Electronic Information,China University of Geosciences (Wuhan),Wuhan 430074,China;
2. Design and Technology Company,China Construction Third Engineering Bureau Co.,Ltd.,Wuhan 430074,China

Received:2020-08-07 Revised:2020-09-07 Online:2021-01-15 Published:2021-02-07

摘要/Abstract

摘要： 混凝土抗压强度是影响建筑质量的主要因素,根据一些主要参数事先预测其强度可作为现场施工的参考。以支持向量回归(SVR)为理论基础,提出一种基于马氏距离的加权型SVR(MWSVR)的人工智能算法对混凝土强度进行预测。不同于将训练样本统一看待的传统方法,该算法根据训练集和测试集自变量的距离来决定训练样本在求解SVR模型中的重要性,距离测试集近的训练样本权重更大,赋予其更高的惩罚因子,从而得到更优的回归超平面。通过与决策树、随机森林、BP神经网络、RBF神经网络和传统SVR方法的比较研究,发现该算法具有更低的均方根误差。

关键词: 混凝土强度, 支持向量回归, 机器学习, 强度预测, 人工智能算法, 马氏距离

Abstract: The compressive strength of concrete is an important indicator affecting construction quality.Predicting the concrete strength according to some key parameters serves as a reference for site construction.Based on support vector regression (SVR),a weighted SVR artificial intelligence algorithm about Mahalanobis distance (MWSVR) is proposed to predict concrete strength.Different from the traditional method,all training samples are treated uniformly,but the proposed algorithm assigns different importance to each training sample based on its distance from the test set.The training sample closer to the test set shows more importance,and thus the model assigns a higher penalty factor to obtain a better regression hyperplane.Compared with the methods of decision tree,random forest,BP neural network,RBF neural network,and conventional SVR,the proposed algorithm shows the lowest root-mean-square-error.

Key words: concrete strength, support vector regression, machine learning, strength prediction, artificial intelligence algorithm, Mahalanobis distance

中图分类号:

TU528.01

曹斐, 周彧, 王春晓, 任梦宇, 周峰. 一种改进的支持向量回归的混凝土强度预测方法[J]. 硅酸盐通报, 2021, 40(1): 90-97.

CAO Fei, ZHOU Yu, WANG Chunxiao, REN Mengyu, ZHOU Feng. An Improved Support Vector Regression Method forConcrete Strength Prediction[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2021, 40(1): 90-97.

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一种改进的支持向量回归的混凝土强度预测方法

An Improved Support Vector Regression Method forConcrete Strength Prediction

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