基于高斯过程回归模型的电石渣激发煤矸石地聚合物强度响应预测与分析

Prediction and Analysis of Strength Response of Calcium Carbide Slag Excited Coal Gangue Geopolymer Based on Gaussian Process Regression Model

地聚合物的抗压强度是评估其能否代替水泥作为新型建筑材料的关键因素之一,但仅依靠大量试验测试强度,既浪费资源又增加成本。为了解决这一问题,通过早期试验收集的电石渣激发煤矸石地聚合物的强度数据,将不同配合比、水胶比、龄期作为输入参数,抗压强度作为输出结果,基于机器学习方法构建强度响应预测模型——高斯过程回归(GPR)模型,并利用模型对不同配合比及龄期的地聚合物强度进行预测,进而建立各组分掺量、水胶比、龄期对强度的影响曲线并探究原因。结果表明:GPR模型经过对样本数据的拟合,可以较好地预测地聚合物的强度,且误差为(-0.001 93~+0.001 83);利用受过训练的模型对未知抗压强度的地聚合物进行强度预测,通过预测结果分析各输入参数(电石渣掺量、煤矸石掺量、水胶比和养护龄期)对强度的影响,发现强度与上述变量均有密切关系,其中电石渣掺量、煤矸石掺量和养护龄期对强度的影响更显著。

The compressive strength of geopolymer is one of key factors in evaluating whether geopolymer can replace cement as a new building material,but relying only on many tests to test its strength wastes resources and improves costs.To solve this problem,the data of calcium carbide slag excited coal gangue geopolymer collected through early experiments,different mixing ratios,water-binder ratios,and ages were used as input parameters and compressive strength was used as output results.The strength response prediction model—Gaussian process regression(GPR)model was constructed based on machine learning methods.The geopolymer strength of different mixing ratios and ages was predicted by using the model,then the influence curves of each component content,water-binder ratio and age on the strength were established and the reasons were explored.The results show that the GPR model can predict the strength of geopolymer well after fitting the sample data,and the error is in the range of(-0.00193~+0.00183).The strength prediction of geopolymer with unknown compressive strength is made by the trained model,and the influences of each input parameters(calcium carbide slag content,coal gangue content,water-binder ratio,and curing age)on the strength were analyzed through the prediction results.It is found that the strength is closely related to the above variables,among which the calcium carbide slag content,coal gangue content and curing age have more influence on the strength.