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基于实验调查和机器学习的重量级水泥泥的脆性指数的预测
Danial Fakhri1, Mohammad Mehdi Karbala2, Hamid Reza Nejati3
1Department of Applied Sciences, Université du Québec à Chicoutimi, 555 Boulevard de l'Université, Chicoutimi, QC, Canada.
Scientific reports
|November 25, 2025
概括
本研究引入了一种机器学习框架,用于预测水泥泥中的脆性指数 (BI),从而减少实验成本和时间. 新方法准确地模拟了脆性,改善了材料选择和故障预测.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算材料科学科学 计算材料科学
- 土木工程 土木工程是指土木工程.
背景情况:
- 脆性指数 (BI) 对于材料选择,故障预测和工程设计中的性能评估至关重要.
- 传统的BI确定是资源密集型的,涉及费力和昂贵的实验程序.
- 重型水泥泥需要准确的BI评估,以获得可靠的工程应用.
研究的目的:
- 开发一个全面的框架,整合实验数据和机器学习,用于建模水泥泥脆性.
- 为重型水泥泥的脆性指数 (BI) 创建一个预测模型.
- 为了减少与确定水泥材料的BI相关的时间和成本.
主要方法:
- 在十种水泥泥配方上进行了各种实验测试 (例如,Split Hopkinson压力杆,单轴压力,巴西测试).
- 编制了250个实验观测数据集,其中有14个独立的输入参数.
- 开发并比较了14个机器学习模型,包括高斯过程回归和支持向量回归,用于BI预测.
主要成果:
- 机器学习模型实现了高精度,使用所有输入变量,R2值从0.93到0.97不等.
- 具有特征选择 (八个输入变量) 的优化模型进一步提高了准确性,产生了从0.940到0.990.99的R2值.
- 确定EP (弹性/可塑性) 作为影响BI的最有影响力的因素,添加剂主要旨在减少脆性.
结论:
- 开发的机器学习框架为预测水泥泥的脆性指数 (BI) 提供了一种新,高效和具有成本效益的方法.
- 该研究展示了先进算法的潜力,以提高材料选择和性能分析中的决策能力.
- 使用机器学习准确的BI预测可以显著简化水泥材料的工程设计流程.
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