Comparison of artificial neural network (ANN) and response surface methodology (RSM) in predicting the compressive and splitting tensile strength of concrete prepared with glass waste and tin (Sn) can fiber

Q1 Chemical Engineering
Sourav Ray, Mohaiminul Haque, Tanvir Ahmed, Taifa Tasnim Nahin
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引用次数: 48

Abstract

Amidst a world of never-ending waste production and waste disposal crises, scientists have been working their way to come up with solutions to serve the earth better. Two such commonly found trash deteriorating the environment are glass and tin can waste. This study aims to investigate the comparative suitability of response surface methodology (RSM) and artificial neural network (ANN) in predicting the mechanical strength of concrete prepared with fine glass aggregate (GFA) and condensed milk can (tin) fibers (CMCF). An experimental scheme has been designed in this study with two input variables as GFA and CMCF, and two output variables compressive and splitting tensile strength. The results show that both variables influenced the compressive and splitting tensile strength of concrete at 7, 28, and 56 days (p < 0.01). The maximum compressive and splitting tensile strength was found at 20% GFA with 1% CMCF and 10% GFA with 0.5% CMCF, respectively. The model predicted values in both techniques were in close agreement with corresponding experimental values in all cases. The results of different statistical parameters in terms of coefficient of correlation, coefficient of determination, chi-square, mean square error, root mean square error, mean absolute error, and standard error prediction indicate the functionality of both modeling approaches for concrete strength prediction. However, RSM models yield better accuracy in simulating the compressive and splitting tensile strength of concrete than ANN models.

人工神经网络(ANN)与响应面法(RSM)预测玻璃废料与锡(Sn) can纤维配制混凝土抗压和劈裂抗拉强度的比较
在一个无休止的废物生产和废物处理危机的世界里,科学家们一直在努力寻找更好地为地球服务的解决方案。两种常见的破坏环境的垃圾是玻璃和锡罐垃圾。本研究旨在研究响应面法(RSM)和人工神经网络(ANN)在预测细玻璃骨料(GFA)和炼乳罐(锡)纤维(CMCF)制备的混凝土力学强度方面的比较适用性。本研究设计了一个实验方案,其中两个输入变量为GFA和CMCF,两个输出变量为抗压强度和劈拉强度。结果表明,这三个变量在第7天、第28天和第56天对混凝土的抗压强度和劈拉强度都有影响(p<0.01)。在20%GFA和10%GFA中,1%的CMCF和0.5%的CMCF分别具有最大抗压强度和最大劈拉强度。两种技术中的模型预测值在所有情况下都与相应的实验值非常一致。不同统计参数在相关系数、决定系数、卡方、均方误差、均方根误差、平均绝对误差和标准误差预测方面的结果表明了这两种建模方法在混凝土强度预测中的功能。然而,RSM模型在模拟混凝土抗压强度和劈拉强度方面比ANN模型具有更好的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of King Saud University, Engineering Sciences
Journal of King Saud University, Engineering Sciences Chemical Engineering-Fluid Flow and Transfer Processes
CiteScore
12.10
自引率
0.00%
发文量
87
审稿时长
63 days
期刊介绍: Journal of King Saud University - Engineering Sciences (JKSUES) is a peer-reviewed journal published quarterly. It is hosted and published by Elsevier B.V. on behalf of King Saud University. JKSUES is devoted to a wide range of sub-fields in the Engineering Sciences and JKSUES welcome articles of interdisciplinary nature.
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