带有固体废物的活性材料的基本设计,使用机器学习
Junfei Zhang1, Shenyan Shang1, Zehui Huo1
1School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China.
Materials (Basel, Switzerland)
|September 28, 2024
概括
机器学习使用飞灰 (FA) 和颗粒高炉渣 (GBFS) 准确预测活性材料 (AAM) 的强度. 水含量和固化期是开发高性能,可持续建筑材料的关键因素.
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.
- 计算材料科学科学 计算材料科学
背景情况:
- 使用飞灰 (FA) 和颗粒高炉渣 (GBFS) 的活性材料 (AAM) 对可持续建筑至关重要.
- 了解它们的强度发展对于有效的材料设计和应用至关重要.
研究的目的:
- 使用机器学习研究基于FA-GBFS的AAM的力量发展机制.
- 为AAMs的单轴压力强度 (UCS) 开发一个预测模型.
主要方法:
- 从FA-GBFS AAMs发表的文献中收集了616个UCS数据点.
- 训练并评估了四种基于树的机器学习模型,包括梯度增强回归 (GBR).
- 利用夏普利添加式解释 (SHAP) 来识别关键的影响变量.
主要成果:
- 梯度增强回归 (GBR) 实现了最高的预测准确性 (R值=0.970,RMSE=4.110 MPa).
- 水含量被确定为影响强度的最重要因素,其次是固化期.
- 建议采用最优的元素比率:Ca:Si ≈1.3,Na:Al < 1,Si:Al > 3. 这些比率是最合适的.
结论:
- 开发的机器学习模型准确地预测了AAM的强度.
- 这些发现为设计高性能,可持续的AAM提供了实际指导.
- 实验室验证证实了该模型的可靠性和实际应用的潜力.
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