基于改进特征筛选的随机森林算法对锂渣混凝土强度的预测研究

doi:10.11896/cldb.22050319

材料导报

2024, Vol. 38

Issue (9): 22050319-6 https://doi.org/10.11896/cldb.22050319

无机非金属及其复合材料

基于改进特征筛选的随机森林算法对锂渣混凝土强度的预测研究

魏令港, 黄靓^*, 曾令宏

湖南大学土木工程学院,长沙 410082

Using Random Forest with Improved Variable Selection to Predict the Compressive Strength of Concrete with Lithium Slag

WEI Linggang, HUANG Liang^*, ZENG Linghong

School of Civil Engineering, Hunan University, Changsha 410082, China

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摘要本工作提出了特征变量筛选结合特征变量相关性的方法,对构建的锂渣混凝土28 d抗压强度数据库进行优化,分别建立了随机森林模型和深度神经网络模型用于测试数据库,并以相关系数(R)、均方根误差(RMSE)和平均相对误差(MAE)三个指标对模型的预测结果进行对比分析。结果表明,预测锂渣混凝土的28 d抗压强度时,采取改进的特征变量筛选方法能够有效提高模型的预测效果,此外,特征变量筛选的前后随机森林(RF)模型的预测效果明显优于深度神经网络(DNN)模型。

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关键词: 随机森林(RF) 深度神经网络(DNN) 特征变量筛选锂渣混凝土抗压强度

Abstract: This work proposed the method of feature variable screening combined with feature variable correlation to optimize the constructed 28-day compressive strength database of lithium-slag concrete, and established random forest model and deep neural network model for testing the database, respectively, and compared the prediction results of the models with three indicators: correlation coefficient (R), root mean square error (RMSE) and mean relative error (MAE). The results show that the improved feature variable screening method can effectively improve the prediction effect of the model when predicting the 28-day compressive strength of lithium-slag concrete, and in addition, the prediction effect of the before-and-after random forest (RF) model with feature variable screening is significantly better than that of the deep neural network (DNN) model.

Key words: random forest (RF) deep neural network (DNN) variable selection concrete with lithium slag compressive strength

出版日期: 2024-05-10 发布日期: 2024-05-13

ZTFLH:	TU528
	TP312

基金资助: 2021年产业技术基础公共服务平台-重点原材料行业碳达峰、碳中和公共服务平台项目(2021-H029-1-1)

通讯作者: * 魏令港,湖南大学土木工程学院硕士研究生,主要从事固体废弃物资源化利用方向的研究。
黄靓,湖南大学土木工程学院教授(俄罗斯自然科学院外籍院士)、博士研究生导师。长期从事混凝土与砌体结构、固体废弃物资源化利用及建筑材料低碳技术的研究、教学和应用推广工作。主持国家重点研发计划课题1项、国家自然科学基金3项、省部级课题6项、长沙市科技计划8项,企业横向课题20余项。发表SCI/EI论文100余篇,获得国家发明专利10余项,为四本国家标准编委。huangliangstudy@126.com

作者简介: 魏令港,湖南大学土木工程学院硕士研究生,主要从事固体废弃物资源化利用方向的研究。

引用本文:

魏令港, 黄靓, 曾令宏. 基于改进特征筛选的随机森林算法对锂渣混凝土强度的预测研究[J]. 材料导报, 2024, 38(9): 22050319-6.
WEI Linggang, HUANG Liang, ZENG Linghong. Using Random Forest with Improved Variable Selection to Predict the Compressive Strength of Concrete with Lithium Slag. Materials Reports, 2024, 38(9): 22050319-6.

链接本文:

http://www.mater-rep.com/CN/10.11896/cldb.22050319 或 http://www.mater-rep.com/CN/Y2024/V38/I9/22050319

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