利用机器学习预测地聚合物混凝土的抗压强度

IF 2.4 Q2 ENGINEERING, MULTIDISCIPLINARY
Priyank Gupta, N. Gupta, K. Saxena
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引用次数: 23

摘要

蟒蛇软件需要python代码来运行所使用的单个k最近邻(KNN)、随机森林回归(RFR)和线性回归(LR)模型。结果表明,在使用的数据集上,RFR机器学习(ML)技术在其他使用的模型中表现出最好的性能。本文的研究结果表明,采用该模型的数据集为FACC的设计和优化提供了一种可接受的算法。在目前的地聚合物混凝土(GPC)制备研究中,以养护、粉煤灰、煅烧粘土、添加水、高效增塑剂、粗骨料、采石场石粉、烧碱、水玻璃等相关变量作为输入参数。使用输入参数的描述性统计分析检查范围、模式、中位数、标准差和其他识别细节。使用RFR, LR和KNN ML技术预测FACC GPC压缩的强度,所有这些技术都基于Python编码。在预测方面,集成ML技术RFR优于单个ML技术KNN。RFR表明[公式:见文]的最大值为0.92,LR提供0.58,尽管KNN的准确性较低,其决定系数为0.56。RFR技术的误差下限、平均绝对误差(MAE)、均方误差(MSE)和均方根误差(RMSE)分别为1.99、7.17和2.67 MPa[公式见文]。误差的统计分析证实了RFR方法的良好准确性。养护温度、养护时间、NaOH的摩尔浓度和FACC的配比对FACC GPC的抗压强度(CS)有显著影响。研究结果表明,该模型在给定数据集的三种ML方法组合中使用RFR为FACC设计和优化提供了一种可接受的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting compressive strength of geopolymer concrete using machine learning
The anaconda software required python code in order to run the utilized individual K-nearest neighbor (KNN), random forest regression (RFR), and linear regression (LR) models. The results show that RFR machine learning (ML) technique out of the other utilized models shows the best performance for a used dataset. The findings of this article indicate that the dataset utilized proposed model provides an acceptable algorithm for FACC design and optimization. In the current study of preparation of geopolymer concrete (GPC), relevant variables such as curing, fly ash, calcined clay, added water, super plasticizer, coarse aggregate, quarry stone dust, caustic soda, and water glass were used as input parameters. The ranges, mode, median, standard deviation, and other identifying details were checked using descriptive statistical analysis for the input parameters. The strength due to the compression of FACC GPC was predicted using RFR, LR, and KNN ML techniques, all based on Python coding. The ensemble ML technique, RFR outperformed the individual ML technique, KNN, in terms of prediction. The RFR indicates that the maximum amount of [Formula: see text] is 0.92, and LR provides 0.58, although the KNN was less accurate, with a coefficient of determination of 0.56. The RFR technique’s lower values of errors, mean absolute error (MAE), MSE, and root mean square error (RMSE) yield 1.99, 7.17, and 2.67[Formula: see text]MPa, respectively. The excellent accuracy of the RFR methodology is confirmed by a statistical analysis of errors. Curing temperature, curing hours, molarity of NaOH, and FACC ratio significantly affect the compressive strength (CS) of FACC GPC. The findings indicate that the proposed model provides an acceptable algorithm for FACC design and optimization using RFR among the three combinations of ML methods for a given dataset.
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