机器学习的比较研究,用于预测贝水泥复合材料的压力
Jinwoong Kim1, Woosik Jang1, Sunho Kang1
1Department of Civil Engineering, Chosun University, 10 Chosundae 1-gil, Dong-Gu, Gwangju 61452, Republic of Korea.
Materials (Basel, Switzerland)
|December 11, 2025
概括
废弃的贝可以用于水泥复合材料,以保持压力强度. 机器学习模型,特别是LightGBM,准确地预测了这种强度,促进了可持续的建筑材料.
科学领域:
- 材料科学 材料科学 材料科学
- 环境工程 环境工程
- 土木工程 土木工程是指土木工程.
背景情况:
- 每年的产量会产生大量的贝废弃物,导致由于不适当的处置而导致环境问题.
- 从废物中开发可持续的建筑材料对于环境保护和资源管理至关重要.
研究的目的:
- 使用机器学习预测结合废弃贝的水泥复合材料的压力强度.
- 确定影响这些复合材料压力强度的最有影响的因素.
- 评估废弃的贝作为建筑中的替代材料的潜力.
主要方法:
- 收集并利用了336个数据集,包括实验结果和文献数据.
- 对比了各种机器学习算法:回归,支持向量回归,人工神经网络和随机森林.
- 使用GridSearchCV.使用优化集成算法 (XGBoost,AdaBoost,额外树,LightGBM)
主要成果:
- 随机森林实现了高预测性能 (R2 = 0.8411).
- 轻GBM表现出卓越的预测能力,其中R2=0.9042,MAE=3.1671,MSE=17.8054,RMSE=4.2196.这些数据均为R2=0.9042,MAE=3.1671,MSE=17.8054和RMSE=4.2196.
- SHAP分析确定了水与水泥的比率 (W/C) 和超级可塑剂含量作为关键变量. 贝与沙子呈负相关性,这表明它们作为部分替代品的作用.
结论:
- 废弃的贝可以有效地纳入水泥复合材料,以保持压力强度.
- 机器学习,特别是LightGBM,为这些可持续材料的性能提供了准确的预测.
- 这些发现支持使用贝复合材料作为环保建筑材料,并适当优化添加剂.
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