通过后传神经网络和微分方程预测混凝土裂的开放
Feifei Sun1, Zhonghua Xia2, Weiqian Feng2
1China Water Resources Beifang Investigation, Design and Research Co., Ltd., 60 Dongting Road, Hexi District, Tianjin, 300222, China. f556sun@163.com.
Scientific reports
|July 14, 2025
概括
结合反向传播神经网络 (BPNN) 和微分方程 (DE) 的新方法改善了混凝土裂开口 (CCO) 预测. 这种方法通过更好地模拟温度效应来增强液压工程维护.
科学领域:
- 液压工程 液压工程 液压工程
- 计算力学 计算力学 计算力学
- 环境监测 环境监测
背景情况:
- 混凝土裂开口 (CCO) 对于液压结构维护至关重要.
- 准确的CCO预测对于积极的工程干预至关重要.
研究的目的:
- 开发和评估使用BPNN和DE的每日CCO的新型预测方法.
- 评估DE在CCO建模中的贡献,与单一的BPNN方法形成对比.
主要方法:
- 用历史数据对BPNN模型进行校准,以获得残留值.
- 使用粒子群优化来校准DE系数.
- 整合BPNN和DE (BPNN-DE-2TD) 进行增强的CCO预测.
主要成果:
- 唯一的BPNN模型提供了合理的CCO预测.
- BPNN-DE-2TD模型显著提高了预测准确性 (例如,JB-3的KGE增加了37%).
- DE有效地模拟了季节性和线性温度趋势,而BPNN捕捉了非线性水位和降水效应.
结论:
- 结合的BPNN-DE方法提高了CCO预测的准确性.
- 这种方法为液压工程中的全球CCO预测提供了宝贵的见解.
- 该研究强调了整合数据驱动和基于物理的模型的协同效益.
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