Experimental Investigation on Strengthening of Latex Treated Coconut Fiber in Concrete

J. Sahaya Ruben, G. Baskar
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Abstract

Coconut fibers have the highest toughness amongst natural fibers. The experiment has carried out to investigate the behavioural study of coconut fibre in concrete member. The Coconut fibre is treated using natural latex before using in concrete, so that it is not be affected by moisture content presented in concrete. In this experimental study 28 days of the compressive strength is carried out using different coconut fibre length of 20mm, 25mm and 30mm respectively with a different percentage as 0.5%, 0.75% and 1%. The selected input variables include the length of the fiber, percentage of the fiber, and maximum load of the specimen. In this paper, Back-Propagation Neural Network (BPNN) model has been developed to predict the Compressive strength of Concrete members. A parametric study is carried out using BPNN to study the influence of each parameter affecting the characteristic compressive strength of the concrete. The results of this study indicate that BPNN provide good predictions which are better than those from other available methods. These models can serve as reliable and simple predictive tools for the prediction of compressive strength of the members.
乳胶处理椰子纤维在混凝土中的强化试验研究
椰子纤维在天然纤维中具有最高的韧性。对椰子纤维在混凝土构件中的性能进行了试验研究。椰子纤维在混凝土中使用前使用天然乳胶处理,因此它不受混凝土中水分含量的影响。本试验研究采用椰纤维长度分别为20mm、25mm和30mm,添加比例分别为0.5%、0.75%和1%,进行了28天的抗压强度试验。所选择的输入变量包括纤维的长度、纤维的百分比和试样的最大载荷。本文建立了反向传播神经网络(BPNN)模型来预测混凝土构件的抗压强度。利用bp神经网络进行参数化研究,研究各参数对混凝土特性抗压强度的影响。研究结果表明,bp神经网络提供了较好的预测效果,优于其他可用的方法。这些模型可作为构件抗压强度预测的可靠、简便的预测工具。
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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