An Artificial Neural Networks Model for Compressive Strength of Self Compacting Concrete

机译：一种人工神经网络，用于自压力混凝土抗压强度模型

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An experimental program was undertaken to evaluate the compressive strength of self-compacting concrete using commercial mathematic program. Sample variation was monitored using an experimental cylinder of concrete measuring 150 mm in diameter and 300 mm in height. This research examined various mixture designs in the laboratory tests with the goal of creating mixtures with desirable flow specification that did not require additional vibration yet provided adequate compressive strength. After 28 days, compressive strength of cylinder concrete determination, a model of Artificial Neural Networks (ANNs) was designed for this research and the results were obtained in this model of ANN. Both experimental tests and mix design program data was analyzed with statistical package software. The result of statistical analysis has been done in 96.82 percent of confidence interval. It has been seen that the ANN can be used as reliable modelling method for similar experiment.

机译：采用实验计划评估使用商业数学计划进行自我压实混凝土的抗压强度。使用直径为150 mm的混凝土的实验缸监测样品变化，高度为300mm。该研究检查了实验室测试中的各种混合物设计，目的是产生具有所需流量规格的混合物，该规格不需要额外的振动，但却提供了足够的抗压强度。 28天后，气缸混凝土测定的抗压强度，设计了一种人工神经网络（ANNS）模型，为本研究设计了，在该ANN模型中获得了结果。使用统计包软件分析了实验测试和混合设计程序数据。统计分析的结果已以96.82％的置信区间进行。已经看到，ANN可用作类似实验的可靠性建模方法。

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《International Conference on Rehabilitation and Maintenance in Civil Engineering》|2016年|1(CD-ROM)|共5页
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AKHMAD Suryadi; QOMARIAH; M. Sarosa;
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中图分类 TU5-53;
关键词
Artificial Neural Networks; Self Compacting Concrete (SCC); Compressive strength;

机译：人工神经网络;自压力混凝土（SCC）;抗压强度;

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4. An Artificial Neural Networks Model for Compressive Strength of Self Compacting Concrete [C] . AKHMAD Suryadi, QOMARIAH, M. Sarosa International Conference on Rehabilitation and Maintenance in Civil Engineering . 2016

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An Artificial Neural Networks Model for Compressive Strength of Self Compacting Concrete

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