一种基于SVR-HGWO智能算法的水力混凝土的新介面参数反向分析方法
Qingshuai Zhu1, Yuling Wang2, Xing Li3
1China South-to-North Water Diversion Corporation Limited, Building 9, East Zone, No. 9 Yard, Linglong Road, Beijing 100036, China.
Materials (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种使用混合灰狼优化和支向量回归 (HGWO-SVR) 的新方法,以准确识别液压混凝土中的介视参数. 这种方法提高了用于材料故障分析的数值模拟的效率和可靠性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
- 土木工程 土木工程是指土木工程.
背景情况:
- 准确的中观测参数识别对于了解水力混凝土裂和故障至关重要.
- 传统的校准方法是低效的,缺乏复杂的材料行为强度.
研究的目的:
- 开发一种新,高效,强大的反转方法,用于液压混凝土中中视镜参数识别.
- 提高混凝土结构数值模拟的可靠性.
主要方法:
- 使用三点曲 (TPB) 试验的3D数值模型构建了一个中视镜模拟数据集.
- 开发了一个支持向量回归 (SVR) 替代模型,以映射介面学参数到负载-CMOD曲线.
- 采用混合灰狼优化 (HGWO) 算法来优化SVR的超参数和反转介面参数.
主要成果:
- 该HGWO-SVR模型显示了高的预测准确性 (R2 = 0.944,MAE = 1.220,MAPE = 0.041).
- 与传统的试错技术相比,拟议的方法显著提高了计算效率.
- 使用倒置参数的模拟与液压混凝土梁的实验负载-CMOD曲线密切匹配.
结论:
- HGWO-SVR方法提供了一个有前途且高效的工具,用于异质材料的中视镜参数识别.
- 这种方法提高了对水力混凝土裂和故障机制的理解.
- 经过验证的方法提高了工程应用的数值模拟的可靠性.
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