Zhou Ji, Su Zhanlin, Hosseini Shahab, Tian Qiong, Lu Yijun, Luo Hao, Xu Xingquan, Chen Chupeng, Huang Jiandong
College of Civil and Environmental Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China.
Shandong Energy Group Xinwen Mining Co., Ltd., Taian 271233, China.
Math Biosci Eng. 2024 Jan;21(1):1413-1444. doi: 10.3934/mbe.2024061. Epub 2022 Dec 27.
The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring the compressive strength of geo-polymer concretes (CSGPoC) needs a significant amount of work and expenditure. Therefore, the best idea is predicting CSGPoC with a high level of accuracy. To do this, the base learner and super learner machine learning models were proposed in this study to anticipate CSGPoC. The decision tree (DT) is applied as base learner, and the random forest and extreme gradient boosting (XGBoost) techniques are used as super learner system. In this regard, a database was provided involving 259 CSGPoC data samples, of which four-fifths of is considered for the training model and one-fifth is selected for the testing models. The values of fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, gravel 10/20 mm, water/solids ratio, and NaOH molarity were considered as input of the models to estimate CSGPoC. To evaluate the reliability and performance of the decision tree (DT), XGBoost, and random forest (RF) models, 12 performance evaluation metrics were determined. Based on the obtained results, the highest degree of accuracy is achieved by the XGBoost model with mean absolute error (MAE) of 2.073, mean absolute percentage error (MAPE) of 5.547, Nash-Sutcliffe (NS) of 0.981, correlation coefficient (R) of 0.991, R of 0.982, root mean square error (RMSE) of 2.458, Willmott's index (WI) of 0.795, weighted mean absolute percentage error (WMAPE) of 0.046, Bias of 2.073, square index (SI) of 0.054, p of 0.027, mean relative error (MRE) of -0.014, and a of 0.983 for the training model and MAE of 2.06, MAPE of 6.553, NS of 0.985, R of 0.993, R of 0.986, RMSE of 2.307, WI of 0.818, WMAPE of 0.05, Bias of 2.06, SI of 0.056, p of 0.028, MRE of -0.015, and a of 0.949 for the testing model. By importing the testing set into trained models, values of 0.8969, 0.9857, and 0.9424 for R were obtained for DT, XGBoost, and RF, respectively, which show the superiority of the XGBoost model in CSGPoC estimation. In conclusion, the XGBoost model is capable of more accurately predicting CSGPoC than DT and RF models.
绿色混凝土行业受益于利用凝胶替代混凝土中的部分水泥。然而，测量地质聚合物混凝土（CSGPoC）的抗压强度需要大量的工作和费用。因此，最好的办法是高精度地预测CSGPoC。为此，本研究提出了基础学习器和超级学习器机器学习模型来预测CSGPoC。决策树（DT）用作基础学习器，随机森林和极端梯度提升（XGBoost）技术用作超级学习器系统。在这方面，提供了一个包含259个CSGPoC数据样本的数据库，其中五分之四用于训练模型，五分之一用于测试模型。将粉煤灰、磨细粒化高炉矿渣（GGBS）、Na2SiO3、NaOH、细集料、4/10mm砾石、10/20mm砾石、水/固体比和NaOH摩尔浓度的值作为模型的输入来估计CSGPoC。为了评估决策树（DT）、XGBoost和随机森林（RF）模型的可靠性和性能，确定了12个性能评估指标。根据所得结果，XGBoost模型实现了最高的精度，训练模型的平均绝对误差（MAE）为2.073，平均绝对百分比误差（MAPE）为5.547，纳什-萨特克利夫（NS）为0.981，相关系数（R）为0.991，R为0.982，均方根误差（RMSE）为2.458，威尔莫特指数（WI）为0.795，加权平均绝对百分比误差（WMAPE）为0.046，偏差为2.073，平方指数（SI）为0.054，p为0.027，平均相对误差（MRE）为-0.014，a为0.983；测试模型的MAE为2.06，MAPE为6.553，NS为0.985，R为0.993，R为0.986，RMSE为2.307，WI为0.818，WMAPE为0.05，偏差为2.06，SI为0.056，p为0.028，MRE为-0.015，a为0.949。通过将测试集导入训练好的模型，DT、XGBoost和RF的R值分别为0.8969和0.9857和0.9424，这表明XGBoost模型在CSGPoC估计方面的优越性。总之，XGBoost模型比DT和RF模型能够更准确地预测CSGPoC。
