一个混合预测和多目标优化框架,用于石灰石烧焦粘土水泥混凝土混凝土混合物设计
Xi Chen1, Weiyi Chen2, Zongao Li3
1School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
Scientific reports
|July 2, 2025
概括
石灰石化粘土水泥 (LC3) 是普通波特兰水泥的可持续替代品,可以使用机器学习和多目标优化进行优化. 这种方法设计了具有成本效益的,环保的LC3混合物,其碳含量减少.
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.
- 环境科学 环境科学
背景情况:
- 石灰石烧焦粘土水泥 (LC3) 的机械性能与普通波特兰水泥 (OPC) 相似,碳足迹明显较低.
- 与OPC相比,大规模的LC3实施可以将体现的二氧化碳排放量减少30%以上.
- 开发具有成本效益和可持续的LC3混合物对于广泛采用至关重要.
研究的目的:
- 提出一种混合框架,将机器学习 (ML) 和多目标优化 (MOO) 结合起来,用于设计LC3混合物.
- 预测LC3混合物的压力强度,并确定关键影响因素.
- 优化LC3混合设计,以降低成本和体现碳,同时满足性能要求.
主要方法:
- 用387个LC3标本的数据集来训练ML模型进行压力强度预测.
- 链式方程-极端梯度提升 (MICE-XGBoost) 模型的多变量推算实现了高预测准确度 (R2 = 0.928).
- SHAP分析确定了影响强度的关键因素,并使用非主导排序遗传算法-II进行多目标优化.
主要成果:
- MICE-XGBoost模型准确地预测了LC3的压力强度.
- 确定了影响强度的关键因素,如水与粘合剂的比率和高酸盐含量.
- 多目标优化产生了帕雷托前线,证明了成本 (13.06%) 和体现碳 (14.83%) 的平衡降低.
结论:
- 混合ML-MOO框架有效地设计了可持续和成本效益的LC3混合物.
- 巴雷托前沿的拐点为选择最佳低中等等级LC3配方提供了指导.
- 该研究提供了可持续LC3混合设计的实际解决方案,有助于减少建筑中的环境影响.
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