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- Title
SAP 内养护机制砂混凝土力学性能 及其 BP 神经网络预测.
- Authors
刘荣桂; 陈 浩; 崔钊玮; 陈业强; 张邵峰; 闫乾勋
- Abstract
The machine-made sand concrete was mixed with different amounts of super absorbent polymer (SAP) and stone powder, and the compressive strength and flexural strength tests were carried out to investigate the influence of SAP on the mechanical properties of machine-made sand concrete with different amounts of stone powder. Back propagation (BP) neural network was used to predict the compressive strength. The results show that the compressive strength of machine-made sand concrete at various ages is increased first with latter decreasing by increasing SAP content. When SAP content is 0.08%, the compressive strength is the highest. The addition of 0.08% SAP can significantly increase the compressive strength with the mixing of various stone powders. The compressive strength of concrete mixed with 9% stone powder has the best effect. The flexural strength of concrete with different stone powder content is decreased first with latter increasing and decreasing by increasing the SAP content. When SAP content is 0.16%, the flexural strength reaches the maximum value, and the optimal mixing amount is 0. 16% SAP with 6% stone powder compound. According to the prediction of 48 sets of compressive strength test data, the predicted value is well consistent with the test value.
- Subjects
FLEXURAL strength testing; POLYMERIC sorbents; COMPRESSIVE strength; BACK propagation; FLEXURAL strength
- Publication
Journal of Jiangsu University (Natural Science Edition) / Jiangsu Daxue Xuebao (Ziran Kexue Ban), 2023, Vol 44, Issue 3, p367
- ISSN
1671-7775
- Publication type
Article
- DOI
10.3969/j.issn.1671-7775.2023.03.017