增强的高斯过程模型用于预测超高性能混凝土 (UHPC) 的压力强度
Zhipeng Zou1, Bin Peng1, Lianghai Xie1
1School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China.
Materials (Basel, Switzerland)
|January 8, 2025
概括
这项研究增强了高斯过程 (GP) 模型,用于预测超高性能混凝土 (UHPC) 的压力强度. 改进的模型在混合设计优化中提供了更高的准确性和可靠性.
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.
- 计算力学 计算力学 计算力学
背景情况:
- 超高性能混凝土 (UHPC) 具有卓越的机械性能,特别是压力强度.
- 准确的压力强度预测对于优化UHPC混合设计至关重要,但由于数据的变化和复杂性而受到阻碍.
- 现有的预测模型经常在有限的数据和复杂的物质相互作用中扎.
研究的目的:
- 增强高斯过程 (GP) 模型,以便更准确地预测UHPC压力强度.
- 为了应对数据分散,有限的可用性和UHPC中复杂的材料相互作用的挑战.
- 开发一个强大的工具来优化UHPC混合比例和降低实验成本.
主要方法:
- 纳入单值分解 (SVD) 以提高数据质量和特征提取.
- 整合卡尔曼过和光滑 (KF/KS) 以减少数据分散,并将预测与物理原理保持一致.
- 开发一个增强的GP模型,将SVD和KF/KS结合起来,用于压力强度预测.
主要成果:
- 与传统方法相比,增强的GP模型在预测UHPC压力强度方面表现出卓越的准确性.
- 单值分解 (SVD) 通过提取关键特征有效地提高了数据质量.
- 卡尔曼过和光滑 (KF/KS) 成功减少了数据分散,提高了预测可靠性.
结论:
- 增强的GP模型为UHPC压力强度预测提供了可靠和可解释的工具.
- 这种方法在准确性和稳定性方面明显优于人工神经网络 (ANN) 和回归模型.
- 开发的方法为优化UHPC混合设计,降低实验费用和确保结构完整性提供了有价值的解决方案.
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