通过基于机器学习的方法预测各种水泥复合材料的水化热量
Liqun Lu1,2,3, Yingze Li1,4, Yuncheng Wang1,4
1School of Materials Science and Engineering, Southeast University, Nanjing 211189, China.
Materials (Basel, Switzerland)
|April 9, 2024
概括
这项研究引入了一种机器学习模型来预测水泥水化热,为普通波特兰水泥和飞灰复合材料提供了比传统方法更快的替代方案. 优化的人工神经网络准确预测热量发展,提高水泥研究效率.
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:
- 水化热在水泥复合材料中至关重要,但传统的测量方法是劳动和时间密集的.
- 在各种水泥材料中量化水化热的现有技术存在重大局限性.
研究的目的:
- 开发和验证基于机器学习的方法,用于预测水化热在水泥复合材料.
- 建立一个有效的方法来评估水合热量,减少对传统测试的依赖.
主要方法:
- 一个人工神经网络 (ANN) 模型使用三种水泥复合材料类型的数据集进行了优化和训练.
- 该模型的预测性能在一个独立的数据集上进行了评估,以确保准确性和概括性.
主要成果:
- 优化的ANN模型在预测特定时间点的水化热方面表现出高准确度.
- 机器学习方法在普通波特兰水泥糊,飞灰水泥糊和飞灰-甲化水泥复合材料中被证明是有效的.
结论:
- 机器学习为测量水化水泥热提供了更高效,更少劳动密集型的替代方案.
- 这种预测方法可以加速水泥研究和生产,并可能应用于其他水泥复合材料的性能.
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