优化材料组成以改善飞-基地质聚合物的机械性能:一种深度学习方法
Hang Yu1,2, Yongqi Zhou1,2, Wenjing Xia1,2
1College of Civil Engineering, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, Jiangsu, China.
Langmuir : the ACS journal of surfaces and colloids
|April 9, 2025
概括
一个新的深度学习模型MK-CNN-GRU准确地预测了基于飞渣的地质聚合物 (FASGG) 的机械性能. 这种环保的水泥替代品.
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.
- 人工智能的人工智能
背景情况:
- 地质聚合物为传统水泥提供了一个环保的替代品.
- 准确预测地聚合物机械性能对于优化混合物设计至关重要.
- 基于飞灰渣的地质聚合物 (FASGG) 需要改进预测模型.
研究的目的:
- 开发一种新的FASGG机械性能预测模型.
- 为FASGG优化材料组成和混合设计.
- 为了提高预测压力和屈曲强度的准确性.
主要方法:
- 利用七个关键参数作为预测的输入变量.
- 应用了深度学习技术,特别是一个新的MK-CNN-GRU模型.
- 集成最大信息系数-K-中位数,卷积神经网络和门式循环单元算法.
- 使用600个实验数据集进行训练和预测.
主要成果:
- 与基准模型相比,MK-CNN-GRU模型表现出优异的预测性能.
- 确定了影响压力强度的关键参数,包括固化年龄和化学比率.
- 揭示了FASGG曲和压力强度之间的指数关系.
- 该模型显示了良好的概括能力和适用于曲强度预测的适用性.
结论:
- MK-CNN-GRU模型有效地预测了FASGG的机械性能,提高了估计准确度.
- 该研究为完善FASGG组成和混合设计提供了一个理论框架.
- 深度学习为材料科学预测提供了一种强大的方法.
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