基于可解释机器学习的纳米改造混凝土的多目标优化
Yue Gu1, Ruyan Fan1, Yikun Li2
1College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China.
Nanomaterials (Basel, Switzerland)
|September 26, 2025
概括
机器学习准确地预测了纳米改性混凝土 (NSC) 的强度. 使用NSGA-II的优化框架平衡了可持续混凝土混合设计的强度,成本和碳排放.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 计算科学 计算科学
背景情况:
- 纳米改性混凝土 (NSC) 在现代工程中至关重要.
- 对于NSC来说,传统的混合比例设计是低效的,需要大量的时间和资源.
研究的目的:
- 开发精确的机器学习模型来预测NSC压力强度.
- 为可持续的NSC混合设计建立一个多目标优化框架,考虑强度,成本和碳足迹.
主要方法:
- 四个机器学习模型 (XGBoost,CatBoost,随机森林,AdaBoost) 被训练来预测压力强度.
- 用NSGA-II算法基于表现最佳的模型进行多目标优化.
- 进行了特征重要性分析,以确定关键影响因素.
主要成果:
- XGBoost 显示出优异的预测准确性 (R2 = 0.99,RMSE = 1.80 MPa).
- 纳米含量显著影响强度 (0.82) 和成本 (0.85).
- NSGA-II算法产生了帕雷托最佳解决方案,说明了压力强度,成本和碳排放之间的权衡.
结论:
- 综合机器学习和优化方法提高了NSC混合比例设计的效率.
- 这种方法为开发可持续且具有成本效益的具体配方提供了有价值的参考.
- 该研究成功地减少了实验工作量,同时促进了环保工程实践.
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