为研究不同石粉参数对混凝土力学性能的影响,应用正交试验法开展了石粉混凝土抗压强度和抗折强度试验,并通过SEM和MIP试验对石粉影响下混凝土的微观结构进行测试分析,最后基于BP神经网络对混凝土28 d抗压强度进行预测分析和变参数扩展分析。结果表明:石粉掺量对混凝土抗压强度和抗折强度影响最大,其次是石粉岩性,石粉细度影响最小。混凝土强度随石粉掺量的增加先增大后减小,掺量为10%时混凝土抗压强度较基准组提高了18.21%,抗折强度提高了17.91%;3种石粉混凝土强度的大小依次为凝灰岩粉>石灰石粉>红砂岩粉;随着石粉细度的增加强度逐渐增大。基于BP神经网络建立的石粉混凝土强度预测模型平均相对误差为2.33%,通过对试验结果的变参数扩展分析,得到预测值与试验结果的变化规律一致。
Abstract
The aim of the study is to investigate the influences of different stone powder parameters on the mechanical propertices of concrete. Compressive strength and flexural strength tests of stone powder concrete were carried out by orthogonal test method. The microstructure of concrete under the influence of stone powder was tested and analyzed by SEM and MIP test. Based on BP neural network, the 28 d compressive strength of concrete was predicted and further analyzed with variable parameters. Results show that the strength of concrete is most affected by the content of stone powder,less by the lithology,and least by the fineness. The strength of concrete first increases and then decreases with the increase of stone powder content.When the content of stone powder is 10%,the compressive strength of concrete increases by 18.21% and the flexural strength increases by 17.91% compared with the reference group.The strength of tuff powder concrete is larger than that of limestone powder concrete and red sandstone powder concrete in sequence.With the increase of the fineness of stone powder,the strength of concrete gradually increases.The average relative error of the model established based on the BP neural network is 2.33%.According to the extended analysis of variable parameters, the change rule of predicted values is consistent with experimental result.
关键词
混凝土 /
力学性能 /
石粉参数 /
正交试验法 /
SEM /
MIP试验 /
BP神经网络
Key words
concrete /
mechanical properties /
stone powder parameters /
orthogonal test method /
SEM /
MIP test /
BP neural network
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基金
国家自然科学基金项目(51509200,52008336)
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