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首页> 外文期刊>Proceedings of the institution of mechanical engineers >Application of adaptive neuro-fuzzy inference system for strength prediction of rubberized concrete containing silica fume and zeolite
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Application of adaptive neuro-fuzzy inference system for strength prediction of rubberized concrete containing silica fume and zeolite

机译：自适应神经模糊推理系统在含硅粉和沸石的橡胶混凝土强度预测中的应用

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摘要

Rubberized concrete containing waste tire rubber, silica fume, and zeolite cured in different curing conditions has been investigated in this paper. For this purpose, coarse aggregates were partially replaced by different percentages of waste rubber chips, namely 10% and 15%, and silica fume and zeolite were incorporated into the binder to replace 10% of cement mass. Different mixes were made and cured in two different conditions, namely in water and air with relative humidity of 100% and 50%, respectively. Compressive strengths of mixes were measured at different ages as 3, 7, 28, and 42 days. In order to simulate and predict the compressive strength of the rubberized cement composite, the influencing parameters were considered as cement content, silica fume, zeolite, rubber percentage, relative humidity, and age of the samples. Then, adaptive neuro-fuzzy inference system was employed to develop a prediction model for compressive strength of the concrete. Six variables were introduced into the adaptive neuro-fuzzy inference system model as inputs and the compressive strength was considered as the output. Prediction results and performance criteria were determined for various datasets including training, validation, testing, and all data. Parametric study of the adaptive neuro-fuzzy inference system models was also conducted to investigate the effect of each variable on the compressive strength of the rubberized concrete. Based on the correlations and errors obtained from the model, it was found that the proposed adaptive neuro-fuzzy inference system model can be a robust tool for predicting the behavior of complex composite materials such as rubberized concrete.
机译：本文研究了在不同固化条件下固化的含废轮胎橡胶，硅粉和沸石的橡胶混凝土。为此目的，粗骨料被不同百分比的废橡胶碎片（即10％和15％）部分替代，并将硅粉和沸石掺入粘合剂中以替代10％的水泥质量。在两种不同的条件下，即分别在相对湿度分别为100％和50％的水和空气中，制作并固化了不同的混合物。在3、7、28和42天的不同年龄下测量混合物的抗压强度。为了模拟和预测橡胶化水泥复合材料的抗压强度，将影响参数考虑为水泥含量，硅粉，沸石，橡胶百分比，相对湿度和样品的老化时间。然后，采用自适应神经模糊推理系统建立混凝土抗压强度预测模型。将六个变量作为输入引入到自适应神经模糊推理系统模型中，并将抗压强度视为输出。确定了各种数据集的预测结果和性能标准，包括训练，验证，测试和所有数据。还对自适应神经模糊推理系统模型进行了参数研究，以研究各个变量对橡胶混凝土抗压强度的影响。基于从模型获得的相关性和误差，发现所提出的自适应神经模糊推理系统模型可以作为预测复杂复合材料（如橡胶混凝土）行为的可靠工具。

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    Texas A&M Univ Zachry Dept Civil Engn 199 Spence St College Stn TX 77843 USA|Texas A&M Univ Syst Texas A&M Transportat Inst Bryan TX USA;

    Texas A&M Univ Zachry Dept Civil Engn 199 Spence St College Stn TX 77843 USA|Texas A&M Engn CIR Bryan TX USA;

    Texas A&M Univ Zachry Dept Civil Engn 199 Spence St College Stn TX 77843 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Rubberized concrete; compressive strength; adaptive neuro-fuzzy inference system; prediction model;

    机译：橡胶混凝土抗压强度;自适应神经模糊推理系统预测模型;

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