Investigation on Tensile and Flexural Behaviour of Fibre Reinforced Concrete Using Artificial Neural Network

Author:

Abstract

<p>The purpose of this study is to investigate the impact that using marble sludge powder as a partial replacement for cement in concrete can have. Experiments were conducted to investigate a variety of characteristics of fiber-reinforced concrete using both fresh concrete and concrete that had been allowed to solidify. In order to determine the split tensile and flexural property of FRC, two water binder ratios, such as 0.35 and 0.40, as well as percentage re-placements of 0%, 5%, 10%, 15%, 20%, and 25% of marble sludge powder and 0.5% of polypropylene 3S fibre were used. After curing for 7, 14, 28, and 56 days, the samples were put through a battery of mechanical tests to evaluate their qualities. The flexural strength and split tensile strength of the material were evaluated over the course of this investigation. In the end, an artificial neural network, also known as an ANN, was utilised in order to create a prediction model for split tensile and flexural strength. We displayed the experimentally obtained Split Tensile Strength and Flexural Strength against the regression analysis strength after 56 days for ANN. This was done so that we could compare the two. According to the findings of the experiments, using powder made from marble waste might lessen the damage that concrete causes to the environment while also providing economic benefits. In this study, dependable mechanical strength was developed by the use of a feed-forward back-propagation neural network, which consisted of eight input neurons, two hidden neurons, and one output neuron. According to the findings, it was discovered that the mechanical properties of concrete might be improved by using dry marble sludge powder as a substitute for up to 15% of the normal aggregate.</p>

Publisher

University of the Aegean

Subject

General Environmental Science

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3