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首页> 外文期刊>International Journal of Materials Engineering Innovation >Adaptive neuro-fuzzy inference system in modelling damping performance of epoxy polymer concrete
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Adaptive neuro-fuzzy inference system in modelling damping performance of epoxy polymer concrete

机译：环氧聚合物混凝土阻尼性能建模的自适应神经模糊推理系统

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In this study the damping property of the epoxy polymer concrete is analysed in relation to its composition, i.e., percentage of epoxy and percentage of filler. Foundry sand having a mean particle size in 300 to 450 urn range is used as aggregate in polymer concrete. Twelve different compositions of polymer concrete are evaluated in this study considering the amount of epoxy resin and filler as variables. The testing of prepared samples is performed using dynamic mechanical analyser (DMA) technique. Damping of the prepared specimens has been evaluated at 10, 30 and 50 Hz. It is observed that resin percentage is most important factor affecting damping of epoxy polymer concrete. Adaptive neuro-fuzzy inference system (ANF1S) has been successfully used for modelling of damping behaviour of the epoxy polymer concrete. For all cases studied, an optimum selection of the training set for reliable modelling and elimination of the experimental cost was found to be between 80% and 70% of the available experimental data.
机译：在这项研究中，分析了环氧聚合物混凝土的阻尼性能及其组成，即环氧的百分比和填料的百分比。在聚合物混凝土中，使用平均粒径在300至450微米范围内的铸造砂作为骨料。本研究以环氧树脂和填料的量为变量，评估了十二种不同的聚合物混凝土成分。使用动态机械分析仪（DMA）技术对准备好的样品进行测试。所制备样品的阻尼已在10、30和50 Hz下进行了评估。观察到树脂含量是影响环氧聚合物混凝土阻尼的最重要因素。自适应神经模糊推理系统（ANF1S）已成功用于环氧聚合物混凝土的阻尼行为建模。对于所有研究的案例，用于可靠建模和消除实验成本的训练集的最佳选择被发现在可用实验数据的80％到70％之间。

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