基于机器学习的水泥基灌浆料强度预测方法

针对采用小直径芯样法准确预测水泥基灌浆料抗压强度的问题，使用压力试验机分别对水泥基灌浆料标准尺寸试块和小直径芯样进行抗压强度试验，并基于试验数据，采用支持向量机回归（SVR）和随机森林回归（RFR）对水泥基灌浆料抗压强度进行回归预测。结果表明：标准尺寸试块均呈正反相接的四角锥体破坏形态，而高径比为0.7和1.0的小直径芯样呈正反相接的圆锥体破坏形态，高径比为1.2的小直径芯样呈斜裂缝剪切破坏形态；标准尺寸试块和小直径芯样的抗压强度值均服从正态分布，且无离群值；随着龄期的增长，标准尺寸试块和小直径芯样的抗压强度提高，且具有早期强度较高的特性；直径46 mm芯样的抗压强度较小，且更易受加工精度的影响；在给定的龄期和直径下，高径比为0.7的芯样抗压强度值最大，抗压强度离散程度最小；RFR预测模型对水泥基灌浆料抗压强度的预测效果更优。所提方法可较准确预测水泥基灌浆料抗压强度，为水泥基灌浆料抗压强度的预测研究提供了参考。

Machine learning based strength prediction method for cement-based grouting material

In order to accurately predict the compressive strength of cement-based grouting material by small diameter core sample method, the compressive strength tests of cement-based grouting material standard size test blocks and small diameter core samples were carried out by pressure testing machine, and based on the test data, support vector machine regression (SVR) and random forest regression (RFR) were used to predict the compressive strength of cement-based grouting material. The results show that the standard size test blocks all show the failure pattern of the quadrangular cone with positive and negative continuation, while the small diameter core samples with a high diameter ratio of 0.7 and 1.0 show a cone failure form with positive and negative connections, and the small diameter core samples with a high diameter ratio of 1.2 show an oblique crack shear failure form; The compressive strength values of standard size test blocks and small diameter core samples all follow a normal distribution and have no outliers; As the age increases, the compressive strength of standard size test blocks and small diameter core samples increases, and they have the characteristics of higher early strength; The compressive strength of the core sample with a diameter of 46 mm is less and more susceptible to the influence of machining accuracy; At a given age and diameter, the compressive strength value of the core sample with a high diameter ratio of 0.7 is the largest, and the degree of dispersion of compressive strength is the smallest; The RFR prediction model has a better effect on the compressive strength of cement-based grouting material. The proposed method can accurately predict the compressive strength of cement-based grouting material, which provides some reference for the prediction and research of compressive strength of cement-based grouting material.