Open AccessStrength prediction of seawater sea sand concrete based on artificial neural network in python
Author(s) -
Hailing Yu,
Jinju Zheng,
Lin Qiu
Publication year - 2022
Publication title -
materials research express
Language(s) - English
Resource type - Journals
ISSN - 2053-1591
DOI - 10.1088/2053-1591/ac5957
Subject(s) - artificial neural network , compressive strength , seawater , python (programming language) , correlation coefficient , cement , approximation error , geotechnical engineering , water–cement ratio , geology , computer science , mathematics , machine learning , materials science , statistics , composite material , oceanography , operating system
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.