使用深度学习和SHAP分析预测水泥稳定钢渣的强度
Zunqing Liu1,2,3, Yifei Wang1,2, Jian Sun1,2
1School of Traffic and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Materials (Basel, Switzerland)
|February 27, 2026
概括
本研究使用深度学习探索水泥稳定钢渣 (CSSS) 的机械性能. 最佳强度是在60%的钢含量下发现的,先进的模型准确地预测了性能.
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.
- 地质技术工程 地质技术工程
背景情况:
- 在建筑材料中利用钢对于可持续性至关重要.
- 了解水泥稳定钢 (CSSS) 的机械性能和微结构演变对于其有效应用至关重要.
- 需要CSSS强度的预测模型来优化混合设计并确保结构完整性.
研究的目的:
- 研究固化年龄,钢含量和分级对CSSS机械性能的影响.
- 阐明控制CSSS力量发展的微观结构机制.
- 开发和验证一个深度学习模型,用于预测CSSS无限制压力强度 (UCS) 和分裂拉伸强度 (STS).
主要方法:
- 实验分析包括SEM-EDS和XRD用于微结构特征.
- 开发和应用CNN-GRU-Attention深度学习模型用于强度预测.
- 统计分析 (SHAP,皮尔森相关性) 以确定关键的强度决定因素.
主要成果:
- 随着固化年龄的增长,CSSS的强度非线性地增加,其含量达到60%的钢渣.
- 钢渣的结合增强了AFt的形成和凝网络的密度,碳化和二次水化有助于长期强度.
- CNN-GRU-Attention模型在预测UCS和STS方面表现出高准确度 (R2>0.94) 和稳定性,表现优于基准模型.
- 水泥含量被确定为主要的强度驱动因素,钢含量显示值效应.
结论:
- 该研究提供了对CSSS机械行为和微观结构发展的全面了解.
- 开发的深度学习模型为预测CSSS强度提供了可靠的工具,有助于实际的工程应用.
- 研究结果强调了控制分级和明智的钢含量对于优化CSSS性能的重要性.
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