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清华大学学报(自然科学版)  2023, Vol. 63 Issue (7): 1041-1049    DOI: 10.16511/j.cnki.qhdxxb.2023.26.026
  论文 本期目录 | 过刊浏览 | 高级检索 |
刘康1, 刘昭伟1, 陈永灿1,2, 马芳平1,3, 王皓冉4, 黄会宝3, 谢辉4
1. 清华大学 水沙科学与水利水电工程国家重点实验室, 北京 100084;
2. 西南石油大学 土木工程与测绘学院, 成都 610500;
3. 国能大渡河流域水电开发有限公司, 成都 610041;
4. 清华四川能源互联网研究院, 成都 610042
Dynamic Bayesian network model for the safety risk evaluation of a diversion tunnel structure
LIU Kang1, LIU Zhaowei1, CHEN Yongcan1,2, MA Fangping1,3, WANG Haoran4, HUANG Huibao3, XIE Hui4
1. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China;
2. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China;
3. China National Energy Dadu River Hydropower Development Co., Ltd., Chengdu 610041, China;
4. Sichuan Energy Internet Research, Tsinghua University, Chengdu 610042, China
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摘要 在运行期内,引水隧洞结构安全的影响因素较多且会随时间发生变化,因此,需要对引水隧洞结构安全进行分析评价。该文建立了一个动态Bayes网络(dynamic Bayesian network,DBN)模型评估引水隧洞的结构安全。首先,通过文献调研和专家咨询,确定引水隧洞结构安全的影响因素;然后,基于模糊层次分析法进行专家咨询,确定条件概率表(conditional probability table,CPT);最后,利用智能机器人巡检结果获得先验概率,并根据隧洞寿命的指数分布假设确定指标的转移概率。将模型应用于中国大渡河流域某水电站引水隧洞的结构安全评价,前向推理结果表明:引水隧洞整体风险较低,约在40 a后风险值增加至0.800,需要采取修补措施。反向传播结果显示:在不同的安全期,引水隧洞需关注不同的安全指标。该模型的评价结果与工程判断一致,表明模型具有较好的准确性和实用性。
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关键词 动态Bayes网络层次分析法风险评价引水隧洞    
Abstract:[Objective] A diversion tunnel is an important part of a water conservancy project. Many factors influence the safety of a diversion tunnel structure, and the risk situation of these factors changes with time during the operating period. Analysis and evaluation of the safety of a diversion tunnel structure are important for ensuring its normal operation. However, the influence factors are complex, and the detection and evaluation of structural safety remain challenging. [Methods] In this paper, a dynamic Bayesian network model for the safety evaluation of a diversion tunnel structure was established. First, a three-level influencing index system of tunnel structure safety was determined through literature research and expert consultation, combined with the world's current tunnel safety standards. The index system included 7 aspects and 26 specific indices, such as crack length, crack width, and pH value. The risk situation of each index was divided into five levels (from A to E), with each level corresponding to a specific risk probability and risk value, aiming to quantify the risk of the diversion tunnel structure. Second, index weights were assigned through expert consultation, and the conditional probability was determined based on the fuzzy analytic hierarchy process. Finally, the prior probability was obtained through the inspection results of intelligent robots, and the transfer probability was determined according to the exponential distribution hypothesis of tunnel life. The time slice interval was set as 1 year, and the safety situation and future development trend of the diversion tunnel structural risk were calculated. In addition, by setting the overall risk level of the tunnel structure, the most likely risk probability distribution of each index was obtained through backpropagation. [Results] The model was applied to the structural safety evaluation of the diversion tunnel of a hydropower station in China, and the assessment results showed that: (1) According to forward inference, the overall risk value of the diversion tunnel was 0.230, which was very low, but lining cracks and lining spalling were structural safety problems that need attention. The evaluation results of the model were consistent with the engineering judgment. (2) The prediction of the development trend of structural risk indicated that this risk increased to 0.800 after approximately 40 years, requiring remedial action. (3) The backpropagation of risk revealed that different safety indices should receive attention in different safety periods of diversion tunnel operation. The risk influencing the degree of the lining spalling and operating environment risk was higher when the diversion tunnel was in a relatively safe state, but when the diversion tunnel was in a relatively dangerous state, the lining deformation, lining crack, and material deterioration were the main risk factors. [Conclusions] The proposed dynamic Bayesian network model performs with good accuracy and practicability for the risk assessment of a diversion tunnel structure. Furthermore, the model can predict the development trend of the structural risk and identify the key influencing index, which is important for diversion tunnel operation and maintenance.
Key wordsdynamic Bayesian network    the analytic hierarchy process    risk evaluation    diversion tunnel
收稿日期: 2022-12-23      出版日期: 2023-06-27
通讯作者: 刘昭伟,教授,     E-mail:
作者简介: 刘康(2000-),男,博士研究生。
刘康, 刘昭伟, 陈永灿, 马芳平, 王皓冉, 黄会宝, 谢辉. 引水隧洞结构安全风险评价的动态Bayes网络模型[J]. 清华大学学报(自然科学版), 2023, 63(7): 1041-1049.
LIU Kang, LIU Zhaowei, CHEN Yongcan, MA Fangping, WANG Haoran, HUANG Huibao, XIE Hui. Dynamic Bayesian network model for the safety risk evaluation of a diversion tunnel structure. Journal of Tsinghua University(Science and Technology), 2023, 63(7): 1041-1049.
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