Chen Shuzhao, Zhou Mengmeng, Shi Xuyang, Huang Jiandong
School of Mines, China University of Mining and Technology, Xuzhou 221116, China.
School of Civil Engineering, Guangzhou University, Guangzhou 510006, China.
Gels. 2023 May 24;9(6):434. doi: 10.3390/gels9060434.
Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a modified beetle antennae search (MBAS) algorithm and random forest (RF) algorithm was developed in this study to model the CS of geopolymer concrete, in which MBAS was employed to adjust the hyperparameters of the RF model. The performance of the MBAS was verified by the relationship between 10-fold cross-validation (10-fold CV) and root mean square error (RMSE) value, and the prediction performance of the MBAS and RF hybrid machine learning model was verified by evaluating the correlation coefficient (R) and RMSE values and comparing with other models. The results show that the MBAS can effectively tune the performance of the RF model; the hybrid machine learning model had high R values (training set R = 0.9162 and test set R = 0.9071) and low RMSE values (training set RMSE = 7.111 and test set RMSE = 7.4345) at the same time, which indicated that the prediction accuracy was high; NaOH molarity was confirmed as the most important parameter regarding the CS of geopolymer concrete, with the importance score of 3.7848, and grade 4/10 mm was confirmed as the least important parameter, with the importance score of 0.5667.
在混凝土中使用凝胶替代一定量的水泥有利于绿色混凝土行业，而测试地质聚合物混凝土的抗压强度（CS）需要大量的精力和费用。为了解决上述问题，本研究开发了一种改进的甲虫触角搜索（MBAS）算法和随机森林（RF）算法的混合机器学习模型，用于对地质聚合物混凝土的CS进行建模，其中MBAS用于调整RF模型的超参数。通过10折交叉验证（10-fold CV）与均方根误差（RMSE）值之间的关系验证了MBAS的性能，并通过评估相关系数（R）和RMSE值并与其他模型进行比较，验证了MBAS和RF混合机器学习模型的预测性能。结果表明，MBAS可以有效地调整RF模型的性能；混合机器学习模型同时具有较高的R值（训练集R = 0.9162，测试集R = 0.9071）和较低的RMSE值（训练集RMSE = 7.111，测试集RMSE = 7.4345），这表明预测精度较高；NaOH摩尔浓度被确认为影响地质聚合物混凝土CS的最重要参数，重要性评分为3.7848，而4/10 mm级被确认为最不重要的参数，重要性评分为0.5667。
