机器学习驱动的流动性和质性质预测新鲜的基于水泥的材料
Yi Liu1,2, Zeyad M A Mohammed1,2, Jialu Ma1,2
1School of Civil Engineering, Central South University, Changsha 410075, China.
Materials (Basel, Switzerland)
|November 27, 2024
概括
人工智能 (AI) 优化了基于水泥的材料可加工性,减少了混合设计时间. 机器学习模型可以预测流动性,收益应力和粘度,以实现高效的建筑材料开发.
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:
- 控制基于水泥的材料可加工性至关重要,但耗时.
- 人工智能 (AI) 为高效的混合设计优化提供了潜力.
- 预测早期可操作性指标有助于快速材料开发.
研究的目的:
- 应用人工智能和机器学习 (ML) 来预测基于水泥的材料可加工性.
- 开发优化的混合ML模型,以提高预测准确度.
- 用SHAP和PDP分析混合组件对可操作性的影响.
主要方法:
- 实验测试以生成233个基于水泥的材料样本的数据集.
- 使用ML算法,包括优化的混合模型,如基于粒子优化 (PSO) 的CatBoost和XGBoost.
- 采用沙普利增量解释 (SHAP) 和部分依赖图 (PDP) 进行变量影响分析.
主要成果:
- 建立了流动性,动态产应力和塑料粘度的准确预测模型.
- 经过优化后的混合模型显示,可行性指标的预测能力得到了改善.
- 通过SHAP和PDP分析确定了影响可行性的关键输入变量.
结论:
- 人工智能和机器学习显著提高了基于水泥的材料混合设计的效率.
- 优化的混合模型为预测可行性提供了一个强大的框架.
- 这项研究为快速准确地控制基于水泥的材料可加工性提供了宝贵的参考资料.
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