高心墙堆石坝筑坝料本构模型参数反演策略对比研究
Comparative study of inversion strategies for constitutive model parameters of materials in high core wall rockfill dams
苗梦瑶 1马刚 2艾志涛 3程欣悦 1汪家伟 3周伟2
作者信息
- 1. 武汉大学 水资源工程与调度全国重点实验室,武汉 430072
- 2. 武汉大学 水资源工程与调度全国重点实验室,武汉 430072;武汉大学 水工程科学研究院,武汉 430072;武汉大学 水工岩石力学教育部重点实验室,武汉 430072
- 3. 武汉大学 水资源工程与调度全国重点实验室,武汉 430072;武汉大学 水工程科学研究院,武汉 430072
- 折叠
摘要
随着堆石坝高度从200 m级向300 m级迈进,大坝变形协调控制变得尤为重要,基于参数反演的堆石坝有限元模拟是进行变形性态评估的常用手段之一.为探讨不同参数反演策略在高心墙堆石坝中的适用性,本文进行了参数联合反演与解耦反演策略对比研究.采用神经网络构建有限元正算的代理模型,采用群体智能优化算法进行迭代寻优,反演得到静力、流变和湿化模型参数.相较于人工干预少、效率高的参数联合反演方法,参数解耦反演方法通过分阶段、分区域的材料参数反演,能够更合理地反映填筑蓄水过程对堆石坝变形的影响.基于解耦反演参数计算的坝体变形与实测值吻合良好,更有效地揭示了坝体在填筑、蓄水及长期运行过程中的变形空间分布和演变规律.
Abstract
As rockfill dams advance in height from the 200-meter to 300-meter class,a coordinated control of dam deformation becomes particularly important,and their deformation behaviors are commonly assessed using parametric inversion-based finite element stress-deformation analysis.To investigate the applicability of different parametric inversion strategies to high-core-wall rockfill dams,this paper presents a comparative study of the two strategies-parametric joint inversion and decoupled inversion.We use the neural network to construct an agent model for the analysis by FEM,and adopt a population-intelligent iterative optimization algorithm to calculate the inversion of static,rheological,and humidification model parameters.Compared with the parameter joint inversion that features with less manual intervention and high efficiency,the decoupled inversion reflects more reasonably the influence of the filling and impounding process on rockfill dam deformation through a staged and zoned inverting procedure.It produces better model parameters and dam body deformation calculations in good agreement with measurements,thus achieving better effects in revealing deformation evolution and its spatial distribution trends in a dam body during filling,impounding and long-term operation.
关键词
高心墙堆石坝/监测数据/参数反演/智能优化算法/解耦反演Key words
high core wall rockfill dam/monitoring datum/parameter inversion/intelligent optimization algorithm/decoupled inversion引用本文复制引用
出版年
2025
国家科技期刊平台
NSTL
万方数据