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Abstract:
Based on the artificial neural network method, the nonlinear mapping between the 28d compressive strength of seawater sea sand concrete and concrete water-cement ratio, cement content, and the sand ratio was established in Python. The results showed that with reasonable network settings, the fitting of the model training was good, and the prediction results were satisfactory. The mean relative error of prediction results was 3.16%, and the correlation coefficient was 0.974. Therefore, it is possible to use an artificial neural network to set up a compressive strength prediction model for seawater sea sand concrete. Compared with the traditional mix design method, the artificial neural network design method can decrease the number of mixing proportion adjustments and reduce the waste of labor, time, and materials.
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MATERIALS RESEARCH EXPRESS
ISSN: 2053-1591
Year: 2022
Issue: 3
Volume: 9
2 . 3
JCR@2022
2 . 3 0 0
JCR@2022
ESI Discipline: MATERIALS SCIENCE;
ESI HC Threshold:66
JCR Journal Grade:3
CAS Journal Grade:4
Cited Count:
WoS CC Cited Count: 5
SCOPUS Cited Count: 6
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 9
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