Cao Rongchuan, Fang Zheng, Jin Man, Shang Yu
School of Civil Engineering, Wuhan University, Wuhan 430072, China.
School of Civil Engineering and Architecture, Henan University, Kaifeng 475000, China.
Materials (Basel). 2022 Mar 24;15(7):2400. doi: 10.3390/ma15072400.
Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R) was used to measure how accurate the results were, which usually ranged from 0 to 1. The results show that the XGB was a more accurate model, indicating an R value of 0.98, as opposed to SVM (0.91) and MLP (0.88). The statistical checks and k-fold cross-validation (CV) also confirm the high precision level of the XGB model. The lesser values of the errors for the XGB approach, such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were noted as 1.49 MPa, 3.16 MPa, and 1.78 MPa, respectively. These lesser values of the errors also indicate the high precision of the XGB model. Moreover, the sensitivity analysis was also conducted to evaluate the parameter's contribution towards the anticipation of C-S of GPC. The use of ML techniques for the prediction of material properties will not only reduce the effort of experimental work in the laboratory but also minimize the cast and time for the researchers.
基于粉煤灰（FA）的地质聚合物混凝土（GPC）正在作为一种可能的替代解决方案进行研究，其对环境的影响比波特兰水泥混合物更低。然而，强度预测的准确性仍有待提高。本研究基于对各种机器学习（ML）方法的调查，以预测GPC的抗压强度（C-S）。采用了支持向量机（SVM）、多层感知器（MLP）和XGBoost（XGB）技术来检验GPC的C-S实验结果与预测结果之间的差异。决定系数（R）用于衡量结果的准确程度，其通常在0到1之间。结果表明，XGB是一个更准确的模型，R值为0.98，而SVM为0.91，MLP为0.88。统计检验和k折交叉验证（CV）也证实了XGB模型的高精度水平。XGB方法的误差值较小，如平均绝对误差（MAE）、均方误差（MSE）和均方根误差（RMSE），分别为1.49MPa、3.16MPa和1.78MPa。这些较小的误差值也表明了XGB模型的高精度。此外，还进行了敏感性分析，以评估参数对GPC的C-S预测的贡献。使用ML技术预测材料性能不仅会减少实验室的实验工作量，还会为研究人员减少成本和时间。
