Abstract
Abstract A data set of cemented sand and gravel (CSG) mix proportion and 28-day compressive strength was established, with outliers determined and removed based on the Boxplot. Then, the distribution law of compressive strength of CSG was analyzed using the skewness kurtosis and single-sample Kolmogorov-Smirnov tests. And with the help of Python software, a model based on Back Propagation neural network was built to predict the compressive strength of CSG according to its mix proportion. The results showed that the compressive strength follows the normal distribution law, the expected value and variance were 5.471 MPa and 3.962 MPa respectively, and the average relative error was 7.16%, indicating the predictability of compressive strength of CSG and its correlation with the mix proportion.
Highlights
A data set of cemented sand and gravel (CSG) mix proportion and 28-day compressive strength was established, with outliers determined and removed based on the Boxplot
The results showed that the compressive strength follows the normal distribution law, the expected value and variance were 5.471 MPa and 3.962 MPa respectively, and the average relative error was 7.16%, indicating the predictability of compressive strength of CSG and its correlation with the mix proportion
CSG dam was first proposed by Raphael J M and Londe P [2, 3], which was developed based on Roller Compacted Concrete (RCC) dam and Concrete Face Rockfill (CFR) dam
Summary
Abstract: A data set of cemented sand and gravel (CSG) mix proportion and 28-day compressive strength was established, with outliers determined and removed based on the Boxplot. Feng et al [11] conducted a research on the effect of sand ratio, water-binder ratio, cement content and fly ash content on the strength, and it was recommended that the sand ratio in actual projects should range from 18% to 32%, water-binder ratio should range from 0.7 to 1.3, and CSG minimum cement content should be not less than 30 kg/m3 These researches on CSG performance have achieved some positive results and provided theoretical guidance for the application of practical engineering, most of them are based on the analysis of the test results of variable factors, which does not reflect the potential law of CSG material performance from a statistical perspective, and lacks the multi-factor correlation analysis based on data prediction. To this end, based on the results of previous CSG tests, this paper conducted a study on CSG compressive strength performance using statistical and predictive methods, which provides reference for CSG’s mix proportion design and innovative applications
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