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首页> 外文期刊>Expert Systems with Application >Prediction of load-displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network
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Prediction of load-displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network

机译:人工神经网络预测复合纤维(钢和聚合物)增强混凝土的荷载-位移曲线

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摘要

Within the framework of studies on FRC, series of tests were undertaken in the laboratory in order to bet-ter understand the behavior of FRC and composite fibers to characteristic loading. The results obtained in the tests vary according to the percentage of the fibers, the water content, the size of grains (grains size distribution) and percentage of composite fibers. Therefore, it is important to estimate the deformation of concrete corresponding to the applied load according to available data and in the case of lacking of enough experimental data. For this purpose, neural network technique was used to predict the load-dis-placement curve and also compressive strength of concrete based on mix proportions. At first, the results of experimental tests carried out in PWUT laboratory on fiber reinforced concrete specimens are pre-sented and then the missing experimental data and gaps in load-displacement curve trend are predicted by back-propagation method in neural network. It is worth mentioning that it can also be used to study the different types of fibers and also orientation of the fibers which will be presented in future works.
机译:在有关FRC的研究框架内,在实验室进行了一系列测试,以便更好地了解FRC和复合纤维对特征载荷的行为。在测试中获得的结果根据纤维的百分比,含水量,晶粒尺寸(粒度分布)和复合纤维的百分比而变化。因此,重要的是根据可用的数据并在缺乏足够的实验数据的情况下,估算与施加的载荷相对应的混凝土变形。为此,使用神经网络技术根据配合比预测混凝土的荷载-位移曲线以及抗压强度。首先,首先在PWUT实验室对纤维混凝土试样进行了实验测试,然后通过神经网络的反向传播方法预测了缺失的实验数据和载荷-位移曲线趋势的差距。值得一提的是,它还可用于研究不同类型的纤维以及纤维的取向,这将在以后的工作中介绍。

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