从研究趋势到性能预测:对含有生物基相变材料的水泥糊进行元启发式机器学习优化
Leifa Li1, Wangwen Sun2, Lauren Y Gómez-Zamorano3
1Xinjiang Jiaotou Construction Management Co., Ltd., Urumchi 830000, China.
Polymers
|September 27, 2025
概括
这项研究使用机器学习来预测生物基相变材料的水泥性能. CatBoost-WOA模型准确地预测了导热率,潜热和压力强度.
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:
- 阶段变换材料 (PCM) 增强了水泥复合材料中的热能储存.
- 预测这些先进材料的热和机械性能是一项挑战.
- 对于用于水泥糊的生物基PCM的研究趋势需要进行系统的探索.
研究的目的:
- 用生物基PCM在水泥糊中的当前研究热点,使用文献识别分析来识别.
- 开发和优化机器学习模型,用于预测导热率 (Tc),隐性热容量 (LH) 和压力强度 (CS).
- 建立一个数据驱动的管道来评估含有生物基PCM的水泥糊的性能.
主要方法:
- 使用CiteSpace对5928篇文章进行了圣经计量分析,以绘制研究趋势的地图.
- 编制一个数据集,包含100个实验样本和9个输入变量.
- 优化四个机器学习算法 (SVR,RF,XGBoost,CatBoost) 使用五个元启发算法 (GA,PSO,WOA,GWO,FFA).
主要成果:
- CatBoost-WOA混合模型展示了卓越的预测性能.
- 在Tc,LH和CS方面获得了高R平方值 (0.927-0.955) 和低RMSE (0.0057-2.91).
- SVR-GWO和XGBoost-WOA模型也表现出强大的泛化能力.
结论:
- 综合的图库计量和机器学习方法有效地绘制研究趋势并预测材料性能.
- 优化的混合模型提供了一种可靠的方法,用于评估生物基PCM的水泥糊.
- 开发的建模管道促进了可持续建筑材料的设计和应用.
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