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.

中文翻译：

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水泥砂石抗压强度的统计规律及预测分析

摘要 建立了水泥砂砾(CSG)配合比和28天抗压强度数据集，并基于Boxplot确定并去除了异常值。然后，利用偏度峰态和单样本Kolmogorov-Smirnov检验分析了CSG的抗压强度分布规律。并借助Python软件，建立了基于反向传播神经网络的模型，根据其混合比例预测CSG的抗压强度。结果表明，抗压强度服从正态分布规律，期望值和方差分别为5.471 MPa和3.962 MPa，平均相对误差为7.16%，说明南玻网抗压强度的可预测性及其与配合比的相关性.