王茜, 冯红春, 周凯. 资源化利用粉煤灰的混凝土强度预测模型[J]. 矿产综合利用, 2024, 45(4): 195-202. DOI: 10.3969/j.issn.1000-6532.2024.04.029
    引用本文: 王茜, 冯红春, 周凯. 资源化利用粉煤灰的混凝土强度预测模型[J]. 矿产综合利用, 2024, 45(4): 195-202. DOI: 10.3969/j.issn.1000-6532.2024.04.029
    WANG Qian, FENG Hongchun, ZHOU Kai. Prediction model for strength of fly ash concrete in resourceful utilization[J]. Multipurpose Utilization of Mineral Resources, 2024, 45(4): 195-202. DOI: 10.3969/j.issn.1000-6532.2024.04.029
    Citation: WANG Qian, FENG Hongchun, ZHOU Kai. Prediction model for strength of fly ash concrete in resourceful utilization[J]. Multipurpose Utilization of Mineral Resources, 2024, 45(4): 195-202. DOI: 10.3969/j.issn.1000-6532.2024.04.029

    资源化利用粉煤灰的混凝土强度预测模型

    Prediction Model for Strength of Fly Ash Concrete in Resourceful Utilization

    • 摘要: 这是一篇陶瓷及复合材料领域的论文。为粉煤灰的可资源化利用以及准确评估粉煤灰混凝土的抗压强度,基于机器学习建模技术,构建了三个混凝土抗压强度预测模型(传统线性回归模型、决策树模型和支持向量机模型),对其抗压性能进行建模预测和对比分析。首先建立了相应的实验数据库,输入参数为水泥、粉煤灰、减水剂、粗骨料、细骨料、水和养护龄期等七个参数,抗压强度为输出参数。基于10折交叉验证,通过均方根误差(RMSE)、平均绝对误差和相关系数评估了上述三个模型在训练集上的性能,并对比各个模型在测试集上的性能。结果表明:养护龄期与抗压强度存在较高的相关性(0.60),粉煤灰对抗压强度的相关性高于水泥。传统线性回归模型在训练集和测试集的RMSE分别为7.27和5.91,决策树模型分别为2.72和9.23,支持向量机模型分别为5.34和4.09。综合来看,支持向量机模型在预测粉煤灰混凝土抗压强度方面具有较好的准确性和稳健性能。研究可为采用粉煤灰的混凝土提供强度设计指导以及推进粉煤灰的可资源化利用。

       

      Abstract: This is an article in the field of ceramics and composites. To achieve the resource utilization of fly ash and accurately assess the compressive strength of fly ash concrete, three predictive models for compressive strength were constructed using machine learning modeling techniques, including traditional linear regression, decision tree, and support vector machine models. These models were utilized to model and analyze the compressive performance of the concrete. Firstly, a corresponding experimental database was established, with seven input parameters including cement, fly ash, water reducer, coarse aggregate, fine aggregate, water, and curing age, and the compressive strength as the output parameter. Based on 10-fold cross-validation, the performance of the three models on the training set was evaluated using root mean square error (RMSE), mean absolute error, and correlation coefficient, and their performance on the test set was compared. The results showed that curing age had a high correlation with compressive strength (0.60), and the correlation of fly ash with compressive strength was higher than that of cement. The traditional linear regression model exhibited an RMSE of 7.27 and 5.91 on the training and test sets, respectively. The decision tree model showcased an RMSE of 2.72 and 9.23 on the respective sets, while the support vector machine model yielded an RMSE of 5.34 and 4.09. Overall, the support vector machine model exhibited good accuracy and robust performance in predicting the compressive strength of fly ash concrete. This research can provide strength design guidance for concrete using fly ash and promote the resource utilization of fly ash.

       

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