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

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中文摘要：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.

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

外文摘要：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(-0.001 93～+0.001 83).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.

外文关键词：

calcium carbide slagcoal ganguegeopolymerGaussian process regressioncompressive strength predictionstrength influencing factor

作者：

宁慧员、张菊、闫长旺、白茹

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作者单位：

内蒙古工业大学土木工程学院,呼和浩特 010051

内蒙古工业大学资源与环境工程学院,呼和浩特 010051

内蒙古工业大学矿产资源绿色开发重点实验室,呼和浩特 010051

生态型建筑材料与装配式结构内蒙古自治区工程研究中心,呼和浩特 010051

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关键词：

电石渣煤矸石地聚合物高斯过程回归抗压强度预测强度影响因素

基金：

国家自然科学基金国家自然科学基金中央引导地方科技发展专项鄂尔多斯市重点研发计划内蒙古自治区直属高校基本科研业务费专项内蒙古自治区直属高校基本科研业务费专项内蒙古自治区直属高校基本科研业务费专项内蒙古工业大学博士基金科学研究项目

项目编号：

52068059523680362022ZY0160YF20232358JY20220009JY20230117JY20220179BS2021049

出版年：

2024

硅酸盐通报

中国硅酸盐学会中材人工晶体研究院

硅酸盐通报

CSTPCD北大核心

影响因子：0.698

ISSN：1001-1625

年,卷(期)：2024.43(3)

参考文献量24