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| 前期决策阶段土石坝坝料弹性模量系数快速估计方法 |
| Rapid Estimation Method for Elastic Modulus Coefficient of Rockfill Materials in Preliminary Design Stage of Embankment Dams |
| 投稿时间：2025-06-05 修订日期：2025-07-01 |
| DOI： |
| 中文关键词: 贝叶斯网络 土石料 变形参数估计 安全评价 前期决策阶段 |
| 英文关键词:bayesian network earth-rock materials assessment of deformation parameter safety evaluation determination prophase |
| 基金项目:国家重点研发计划课题(2024YFF1700504，2024YFF1700505);中国水科院基本科研业务费专项项目(GE0145B052021);中国水科院科技成果转化基金专项项目(GE121003A0032022，GE121003A0032024)。 |
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| 中文摘要: |
| 大坝变形分析是土石坝结构分析和安全评价的重要内容。坝料变形特性参数的确定是大坝变形分析的基础。针对在大坝前期决策阶段（规划、预可行性研究和可行性研究阶段初期）多不具备通过试验确定坝料变形特性参数的问题，基于贝叶斯网络，提出了坝料变形控制参数K（弹性模量系数）的快速估计方法，为前期开展大坝变形分析和安全评价提供了取参范围。主要研究成果和结论为：（1）从地震荷载、坝料物理特性和大坝结构特性三个方面，确定了8个贝叶斯网络节点，建立了土石料变形特性网络模型架构；（2）基于建立的土石坝统计数据库，提出了基于贝叶斯网络的土石料弹性模量系数K快速估计方法；（3）通过对西部某土石坝坝壳料弹性模量系数K进行取参范围预估，验证了本文所提出的快速估计方法在实际工程中的适用性，研究成果可为同类型工程前期决策阶段变形安全评价中土石坝坝料弹性模量系数K的取值提供参考依据。 |
| 英文摘要: |
| Deformation monitoring and analysis of dams constitutes a key component in the structural integrity analysis and safety assessment of embankment dams. The determination of deformation behavior parameters for dam construction materials forms the basis of deformation analysis in embankment dams. Addressing the common unavailability of experimentally determined deformation behavior parameters for dam construction materials during early-stage decision-making phases (planning, pre-feasibility, and initial feasibility studies), this study proposes a Bayesian network-based rapid estimation framework for the deformation modulus factor K. This approach provides preliminary value ranges for deformation analysis and safety assessment of embankment dams in project planning stages. The principal research findings and conclusions are presented as follows: (1) Eight Bayesian network nodes were identified across three domains—seismic loading, material-specific physical properties, and structural characteristics of embankment dams—establishing a computational framework for modeling deformation behavior characteristics of earth-rockfill materials. (2) Leveraging the compiled embankment dam statistical database, a Bayesian network-based framework was developed for rapid estimation of the elastic modulus coefficient K in earth-rockfill materials. (3) Validation of the proposed rapid estimation method was achieved through predicting value ranges of the elastic modulus coefficient K for rockfill zone materials in an embankment dam located in western China. This case study confirms the method's practical applicability, establishing reference benchmarks for deformation parameter selection in safety evaluations of similar projects during early decision-making phases. |
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