Marks Michał, Glinicki Michał A, Gibas Karolina
Research and Academic Computer Network,Wawozowa 18, Warsaw 02-796, Poland.
Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, Warsaw 02-106, Poland.
Materials (Basel). 2015 Dec 11;8(12):8714-8727. doi: 10.3390/ma8125483.
The aim of the study was to generate rules for the prediction of the chloride resistance of concrete modified with high calcium fly ash using machine learning methods. The rapid chloride permeability test, according to the Nordtest Method Build 492, was used for determining the chloride ions' penetration in concrete containing high calcium fly ash (HCFA) for partial replacement of Portland cement. The results of the performed tests were used as the training set to generate rules describing the relation between material composition and the chloride resistance. Multiple methods for rule generation were applied and compared. The rules generated by algorithm J48 from the Weka workbench provided the means for adequate classification of plain concretes and concretes modified with high calcium fly ash as materials of good, acceptable or unacceptable resistance to chloride penetration.
本研究的目的是使用机器学习方法生成预测高钙粉煤灰改性混凝土抗氯化物性能的规则。根据Nordtest方法Build 492进行的快速氯离子渗透试验，用于测定部分替代波特兰水泥的含高钙粉煤灰（HCFA）混凝土中氯离子的渗透情况。所进行试验的结果用作训练集，以生成描述材料组成与抗氯化物性能之间关系的规则。应用并比较了多种规则生成方法。由Weka工作台的J48算法生成的规则为普通混凝土和高钙粉煤灰改性混凝土作为抗氯离子渗透性能良好、可接受或不可接受的材料进行适当分类提供了方法。
