基于Stacking集成学习的铜矿尾矿水泥基材料抗压强度预测与性能优化研究

Prediction and performance optimization of compressive strength for copper tailings cementitious materials based on stacking ensemble learning

摘要

【目的】由于环境问题日益严重以及资源日趋紧缺，铜尾矿砂在水泥基材料中的可持续利用受到了广泛关注。然而，由于材料成分之间的复杂相互作用，准确预测掺入铜尾矿砂的水泥基材料的抗压强度仍然是一项挑战。利用Stacking集成学习方法构建高精度预测模型，并优化混合设计，以提高材料的力学性能。【方法】通过试验研究不同铜尾矿砂掺量(0%、5%、10%、15%和20%)以及不同水胶比(0.35和0.45)对水泥基材料抗压强度的影响。为增强模型的泛化能力，采用数据融合方法，将试验数据与公开的混凝土抗压强度数据集相结合，建立包含698组样本的数据集。基于K近邻回归、支持向量回归、决策树和随机森林构建Stacking集成学习模型，并以RF作为次级学习器。此外，利用贝叶斯优化方法对模型超参数进行调优，以提升模型的预测性能。通过均方根误差、标准差、平均绝对百分比误差以及决定系数等指标对Stacking模型的预测效果进行评估，并与单一机器学习模型进行对比。【结果】试验结果表明，随着铜尾矿砂掺量的增加，水泥基材料的抗压强度整体呈下降趋势，尤其当掺量超过15%时，强度下降较为显著。28 d龄期时，试验样品的抗压强度最高，表明水化反应效果良好。Stacking集成学习模型在抗压强度预测任务中表现最佳，RMSE=0.37,SD=0.16,MAPE=0.91%,R²=0.991,显著优于单一机器学习模型。在单一模型中，RF表现最佳(RMSE=2.57,R²=0.977),而KNN预测性能最差(R²=0.967)。【结论】构建了一种基于Stacking集成学习的铜尾矿砂水泥基材料抗压强度预测模型，并通过贝叶斯优化进一步提升了模型的预测精度。研究结果表明，优化水胶比及铜尾矿砂掺量对于改善水泥基材料的力学性能至关重要。所提出的Stacking预测模型可为水泥基材料的配合比优化提供可靠的数据支持，推动铜尾矿砂在建筑材料中的可持续应用。

Abstract

[Objective] Due to increasingly severe environmental issues and resource depletion, the sustainable utilization of copper tailings in cementitious materials has attracted widespread attention. However, accurately predicting the compressive strength of cementitious materials incorporating copper tailings remains a challenge due to the complex interactions among material components. A high-precision predictive model was developed using a Stacking ensemble learning approach and optimize mix design to enhance the mechanical properties of the materials. [Methods] Experiments were conducted to investigate the effects of different copper tailings replacement levels(0%, 5%, 10%, 15%, and 20%) and water-to-binder ratios(0.35 and 0.45) on the compressive strength of cementitious materials. To improve the generalization capability of the model, a data fusion method was employed by integrating experimental data with a publicly available concrete compressive strength dataset, [Results] ing in a dataset containing 698 samples. A Stacking ensemble learning model was constructed based on k-nearest neighbors, support vector regression, decision trees, and random forests, with RF serving as the meta-learner. Additionally, Bayesian optimization was applied to fine-tune the hyperparameters of the model to enhance predictive performance. The predictive performance of the Stacking model was evaluated using root mean square error, standard deviation, mean absolute percentage error, and coefficient of determination and was compared with that of individual machine learning models. [Results] The experimental result showed that the compressive strength of cementitious materials generally decreased with increasing copper tailings content, with a significant drop observed when the replacement level exceeded 15%. At the curing age of 28 days, the specimens exhibited the highest compressive strength, indicating a well-developed hydration reaction. The Stacking ensemble learning model demonstrated the best performance in predicting compressive strength, achieving RMSE=0.37, SD=0.16, MAPE=0.91%, and R²=0.991, significantly outperforming individual machine learning models. Among the individual models, RF showed the best performance(RMSE= 2.57, R²=0.977), while KNN exhibited the lowest predictive accuracy(R²=0.967). [Conclusion] A Stacking ensemble learning-based predictive model was developed for the compressive strength of copper tailings cementitious materials and further enhanced its predictive accuracy through Bayesian optimization. The findings indicate that optimizing the water-to-binder ratio and copper tailings content is crucial for improving the mechanical properties of cementitious materials. The proposed Stacking-based predictive model provides reliable data support for mix design optimization, promoting the sustainable application of copper tailings in construction materials.

关键词

铜尾矿砂 / 抗压强度 / 预测 / 机器学习 / Stacking集成 / 影响因素

Key words

copper tailings / compressive strength / prediction / machine learning / stacking ensemble / influencing factors

基金资助

国家自然科学基金(62272336)