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Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network
Advances in Materials Science and Engineering ( IF 2.098 ) Pub Date : 2020-06-01 , DOI: 10.1155/2020/2608231 Kraiwut Tuntisukrarom 1 , Raungrut Cheerarot 1
Advances in Materials Science and Engineering ( IF 2.098 ) Pub Date : 2020-06-01 , DOI: 10.1155/2020/2608231 Kraiwut Tuntisukrarom 1 , Raungrut Cheerarot 1
Affiliation
The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA (PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all four considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good pozzolanic material. The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer and one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined using the optimal ANN model closely followed the trend lines and surface plots of the experimental results.
中文翻译:
用人工神经网络预测底灰混凝土的抗压强度性能。
这项工作的目的是通过使用人工神经网络来检查底灰(GBA)混凝土的抗压强度性能。对于此预测,考虑了四个输入参数,特别是水与粘合剂的比率(WB),GBA的替代百分比(PR),GBA的中值粒径(PS)和混凝土的使用年限(AC)。结果表明,所有四个考虑的参数都影响混凝土的强度发展,高细度的GBA可以作为良好的火山灰材料。最佳的ANN模型具有一个包含两个隐藏层的体系结构,其中第一个隐藏层包含六个神经元,第二个隐藏层包含一个神经元。所提出的基于ANN的显式方程表示一个高精度的预测模型,其R 2的统计值高于0.996。此外,使用最佳ANN模型确定的抗压强度行为紧随实验结果的趋势线和表面图。
更新日期:2020-06-01
中文翻译:
用人工神经网络预测底灰混凝土的抗压强度性能。
这项工作的目的是通过使用人工神经网络来检查底灰(GBA)混凝土的抗压强度性能。对于此预测,考虑了四个输入参数,特别是水与粘合剂的比率(WB),GBA的替代百分比(PR),GBA的中值粒径(PS)和混凝土的使用年限(AC)。结果表明,所有四个考虑的参数都影响混凝土的强度发展,高细度的GBA可以作为良好的火山灰材料。最佳的ANN模型具有一个包含两个隐藏层的体系结构,其中第一个隐藏层包含六个神经元,第二个隐藏层包含一个神经元。所提出的基于ANN的显式方程表示一个高精度的预测模型,其R 2的统计值高于0.996。此外,使用最佳ANN模型确定的抗压强度行为紧随实验结果的趋势线和表面图。