基于增强的机器学习模型的性能分析,用于预测生物煤-水泥复合材料的压力强度
Jinwoong Kim1, Daehee Ryu1, Heojeong Hwan1
1Department of Civil Engineering, Chosun University, 10 Chosundae 1-gil, Dong-Gu, Gwangju 61452, Republic of Korea.
Materials (Basel, Switzerland)
|January 28, 2026
概括
这项研究使用机器学习来预测使用生物炭 (一种可持续材料) 作为部分水泥替代品的水泥复合材料的压力强度. 这些发现支持生物炭.
科学领域:
- 材料科学 材料科学 材料科学
- 可持续建筑 可持续建筑
- 机器学习应用 机器学习应用
背景情况:
- 从烧化生物质中提取的生物炭提供碳捕获和作为可持续建筑材料的潜力.
- 在水泥复合材料中用生物炭部分替代水泥正在探索低碳建筑.
研究的目的:
- 为了预测水泥复合材料的压力强度,用生物炭部分取代水泥.
- 评估和优化机器学习模型,以准确预测强度.
主要方法:
- 分析了716个数据样本 (实验和文献衍生).
- 输入变量包括水与水泥的比率,生物炭含量和各种混合成分.
- 使用机器学习模型 (MLR,ENR,SVR,GBM,XGBoost,LightGBM,CatBoost,NGBoost) 并使用GridSearchCV和Optuna进行了优化.
主要成果:
- 梯度提升机 (GBM) 最初显示了高精度.
- 轻GBM实现了最好的预测性能,MAE=3.3258,RMSE=4.6673,MAPE=11.19%,R2=0.8271.1,其中MAE=3.3258,RMSE=4.6673,MAPE=11.19%,R2=0.8271.
- SHAP分析显示,水与水泥的比率和水泥含量是主要预测因素.
结论:
- 机器学习模型,特别是LightGBM,可以准确预测生物碳水泥复合材料的压力强度.
- 生物炭在开发低碳建筑材料方面显示出作为部分水泥替代品的巨大潜力.
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