Experiment and Lasso regression model of the macroscopic performance of metakaolin-slag geopolymer paste
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摘要: 设计120组偏高岭土-矿渣地聚物净浆试验,探讨了碱激发剂浓度、模数、液固比这三个变量对地聚物净浆抗压强度、流动度和凝结时间的影响规律。基于获得的试验数据,建立Lasso多元回归模型预测了偏高岭土-矿渣地聚物净浆7天和28天抗压强度、流动度、初凝及终凝时间。试验结果表明:(1) 抗压强度随碱激发剂浓度的增大而提高,随液固比增大而降低,随模数的增大先提高后降低;(2) 液固比增大,凝结时间延长;而模数和浓度对凝结时间的影响由碱激发剂的硅含量和碱含量决定;(3) 流动度主要与碱激发剂的黏稠程度和液固比有关。模型验证结果表明:采用Lasso算法对回归模型进行正则化,避免了回归系数过大而导致的过拟合现象,提出的回归模型能准确预测偏高岭土-矿渣地聚物净浆各项宏观性能,测试集数据中的预测值与试验值的相关性系数均大于0.92。Abstract: 120 tests of metakaolin-slag geopolymer paste were designed, and the effects the alkali activator concentration, modulus and liquid-solid ratio on the compressive strength, fluidity and setting time of geopolymer paste were discussed. Based on the obtained experimental data, a Lasso multiple regression model was established to predict the 7 days and 28 days compressive strength, fluidity and initial and final setting time of metakaolin-slag geopolymer paste. Experimental results show that: (1) The compressive strength of geopolymer increases with the increase of the concentration of alkaline activator, decreases with the increase of the liquid-solid ratio, and first increases and then decreases with the increase of the modulus. (2) With the increase of liquid-solid ratio, the setting time is prolonged. The influence of the modulus and concentration on the setting time of geopolymer is determined by the silicon content and alkali content of the alkaline activator. (3) The fluidity is mainly related to the viscosity and the liquid-solid ratio of the alkaline activator. The verification results of the model show that: The Lasso algorithm is used to regularize the regression model, which avoids the overfitting phenomenon caused by excessive regression coefficient. The proposed regression model can accurately predict the macroscopic properties of metakaolin-slag geopolymer paste, and the correlation coefficient between the predicted value and the test value in the test set data is greater than 0.92.
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Key words:
- alkali-activated /
- compressive strength /
- fluidity /
- setting time /
- machine learning /
- geopolymer
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表 1 偏高岭土(MK)和粒化高炉矿渣(GGBFS)的粒度指标
Table 1. Particle size index of metakaolin (MK) and ground granulated blast furnace slag (GGBFS)
Material D10/μm D50/μm D90/μm MK 0.516 0.819 3.742 GGBFS 1.389 4.447 10.554 Note: D10, D50, D90—10%, 50%, 90% cumulative particle size distribution. 表 2 MK和GGBFS的化学组成
Table 2. Chemical composition of MK and GGBFS
Mass fraction of chemical
composition/wt%MK GGBFS SiO2 52.53 30.23 Al2O3 45.42 13.72 CaO 0.26 44.06 MgO – 5.58 SO3 0.04 3.16 TiO2 0.97 1.79 K2O 0.18 0.50 Fe2O3 − 0.41 Others 0.60 0.55 表 3 偏高岭土-矿渣地聚物强度和工作性能影响因素及水平
Table 3. Influencing factors and levels of compressive strength and working performance of metakaolin-slag geopolymer pastes
Factors Levels Concentration C 0.23, 0.27, 0.31, 0.35 0.39 Modulus M 1.0, 1.2, 1.4, 1.6, 1.8 1.0, 1.2, 1.4, 1.6 Liquid-to-solid ratio 0.9, 1.1, 1.3, 1.5, 1.7 0.9, 1.1, 1.3, 1.5, 1.7 表 4 数据集中各变量的统计指标值
Table 4. Statistical measurements of the variables in the dataset
Variable x1 x2 x3 σ7d/MPa σ28d/MPa f/mm Ti/min Tf/min Minimum 0.23 1.00 0.90 9.90 11.49 88.00 59.00 65.00 Maximum 0.39 1.80 1.70 75.40 76.40 300.00 337.00 340.00 Mean 0.31 1.40 1.30 33.66 38.92 193.80 143.45 149.88 Standard deviation 0.06 0.29 0.28 16.30 16.22 53.39 68.15 68.26 Notes: x1—Concentration of alkaline activator; x2—Modulus of alkaline activator; x3—Liquid-solid ratio of alkaline activator; σ7d and σ28d—7 days compressive strength and 28 days compressive strength of metakaolin-slag geopolymer pastes; f—Fluidity of metakaolin-slag geopolymer pastes; Ti and Tf—Initial setting time and final setting time of metakaolin-slag geopolymer pastes. 表 5 偏高岭土-矿渣地聚物净浆预测值与试验值的相关系数
Table 5. Correlation coefficients between predicted values and experimental values of metakaolin-slag geopolymer pastes
Macroscopic properties Correlation coefficients (COD) Training
procedureTest
procedureCompressive strength (7 days) 0.980 0.946 Compressive strength (28 days) 0.940 0.929 Fluidity 0.984 0.989 Initial setting time 0.943 0.926 Final setting time 0.943 0.926 -
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