土石坝安全风险分析方法研究
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摘要
风险分析和安全评估是土石坝工程中一个有难度而富有挑战性的重要研究领域。过去的许多统计数据显示土石坝漫坝、渗流是土石坝破坏的最主要因素。因此本研究探讨三种主要土石坝失效模式:洪水漫坝、渗透破坏和坝坡失稳,并在此基础上对土石坝失事综合风险进行了评估。本文的主要研究内容有:
(1)采用故障树分析方法对土石坝失事破坏类型进行了分析,将土石坝失事模式分为3种主要失事模式,建立了各失事模式风险率的计算模型。建立了土石坝系统综合失事风险率计算公式,提出了土石坝系统综合失事风险率计算步骤。
(2)利用拉丁超立方抽样–蒙特卡罗((Latin hypercube sampling-Monte Carlo,LHS-MC)方法评估了洪水和风浪作用下大坝的漫坝风险。采用LHS代替MC的随机抽样过程,生成洪峰流量和风速等随机变量样本,建立了土石坝在洪水和波浪共同作用下的漫坝风险模型。以土石坝漫坝风险问题为例说明了LHS-MC方法的有效性。
(3)提出了考虑土体参数空间变异性及库水位变化的坝坡风险分析的框架及具体的实现方法。在极限平衡法的基础上,将拉丁超立方抽样(LHS)与蒙特卡罗(MC)方法相结合,考虑库水位变化、坝坡土性参数的变异性和相关性,计算坝坡稳定可靠度指标及坝坡失稳概率。
(4)在详细分析贝叶斯网络特点的基础上,将贝叶斯网络应用于土石坝系统综合风险评估,建立了基于贝叶斯网络的土石坝系统综合风险分析模型。将贝叶斯网络方法和故障树方法进行了比较,并给出了故障树向贝叶斯网络转化的基本步骤。以土石坝为例,根据土石坝的失效机理建立了土石坝风险分析的贝叶斯网络模型,利用基于随机推论的贝叶斯网络对土石坝的风险进行了计算,并将计算结果与精确推理的计算结果进行了对比,随机推论计算的风险值误差较小。
Overtopping, seepage, and slope instability are the main causes of dam failures. This study takes Wohushan Dam as a case study of three potential failure modes.
Based on the theory of risk analysis,this study develops a LHS–MC method to evaluate dam overtopping probability that accounts for the uncertainties arising from wind speed and peak flood. For complex problems, the Monte Carlo (MC) simulation is the most used method in risk analysis. But the conventional MC sampling method is not computationally efficient for rare event problems. Latin hypercube sampling (LHS) is suggested as a tool to improve the efficiency of MC random sampling method. LHS method is used to generate samples of peak flow rate and wind speed especially for rare events. One example of dam overtopping risk analysis is presented to demonstrate the validity and capability of the proposed method. It is shown that LHS method is more efficient than MC simulation,which tends to convergence within relative few simulation times. Reservoir routing, which incorporates operation rules, wind setup, and run-up, is used to evaluate dam overtopping probability.
Fault tree analysis (FAT) is used to analysis the failure types of earth-rock dam, and failure modes of earth-rock dam are classified into three main failure mode: overtopping failure mode, seepage failure mode and slope instability failure mode. Risk assessment models for each failure mode are established, and accordingly performance function of each failure mode of dam is proposed.
Risk analysis of slope instability in earth dams is presented using LHS–MC method. Latin hypercube sampling (LHS) is suggested as a tool to improve the efficiency of MC random sampling method. Based on the limit equilibrium method,a combination of LHS and MC method is used to evaluate the slope instability risk accounting for the uncertainties arising from soil properties and water level. One example is presented to demonstrate the validity and capability of the proposed method. By means of numerical example, it is shown that about 30% of the calculations can be saved by using LHS–MC method instead of simple MC. The exact savings, however, are dependent on details in the use of LHS and on the shape of the failure surfaces of the problems.
The basic principle of the Bayesian network is briefly introduced and compared with the fault tree analysis. A procedure to transform a fault tree into a Bayesian network is presented. Then the Bayesian network is applied to evaluate the reliability of earth-rock dams. The results indicate that the reliability of earth-rock dams can be evaluated using the Bayesian network in a more rational way.
(1)采用故障树分析方法对土石坝失事破坏类型进行了分析,将土石坝失事模式分为3种主要失事模式,建立了各失事模式风险率的计算模型。建立了土石坝系统综合失事风险率计算公式,提出了土石坝系统综合失事风险率计算步骤。
(2)利用拉丁超立方抽样–蒙特卡罗((Latin hypercube sampling-Monte Carlo,LHS-MC)方法评估了洪水和风浪作用下大坝的漫坝风险。采用LHS代替MC的随机抽样过程,生成洪峰流量和风速等随机变量样本,建立了土石坝在洪水和波浪共同作用下的漫坝风险模型。以土石坝漫坝风险问题为例说明了LHS-MC方法的有效性。
(3)提出了考虑土体参数空间变异性及库水位变化的坝坡风险分析的框架及具体的实现方法。在极限平衡法的基础上,将拉丁超立方抽样(LHS)与蒙特卡罗(MC)方法相结合,考虑库水位变化、坝坡土性参数的变异性和相关性,计算坝坡稳定可靠度指标及坝坡失稳概率。
(4)在详细分析贝叶斯网络特点的基础上,将贝叶斯网络应用于土石坝系统综合风险评估,建立了基于贝叶斯网络的土石坝系统综合风险分析模型。将贝叶斯网络方法和故障树方法进行了比较,并给出了故障树向贝叶斯网络转化的基本步骤。以土石坝为例,根据土石坝的失效机理建立了土石坝风险分析的贝叶斯网络模型,利用基于随机推论的贝叶斯网络对土石坝的风险进行了计算,并将计算结果与精确推理的计算结果进行了对比,随机推论计算的风险值误差较小。
Overtopping, seepage, and slope instability are the main causes of dam failures. This study takes Wohushan Dam as a case study of three potential failure modes.
Based on the theory of risk analysis,this study develops a LHS–MC method to evaluate dam overtopping probability that accounts for the uncertainties arising from wind speed and peak flood. For complex problems, the Monte Carlo (MC) simulation is the most used method in risk analysis. But the conventional MC sampling method is not computationally efficient for rare event problems. Latin hypercube sampling (LHS) is suggested as a tool to improve the efficiency of MC random sampling method. LHS method is used to generate samples of peak flow rate and wind speed especially for rare events. One example of dam overtopping risk analysis is presented to demonstrate the validity and capability of the proposed method. It is shown that LHS method is more efficient than MC simulation,which tends to convergence within relative few simulation times. Reservoir routing, which incorporates operation rules, wind setup, and run-up, is used to evaluate dam overtopping probability.
Fault tree analysis (FAT) is used to analysis the failure types of earth-rock dam, and failure modes of earth-rock dam are classified into three main failure mode: overtopping failure mode, seepage failure mode and slope instability failure mode. Risk assessment models for each failure mode are established, and accordingly performance function of each failure mode of dam is proposed.
Risk analysis of slope instability in earth dams is presented using LHS–MC method. Latin hypercube sampling (LHS) is suggested as a tool to improve the efficiency of MC random sampling method. Based on the limit equilibrium method,a combination of LHS and MC method is used to evaluate the slope instability risk accounting for the uncertainties arising from soil properties and water level. One example is presented to demonstrate the validity and capability of the proposed method. By means of numerical example, it is shown that about 30% of the calculations can be saved by using LHS–MC method instead of simple MC. The exact savings, however, are dependent on details in the use of LHS and on the shape of the failure surfaces of the problems.
The basic principle of the Bayesian network is briefly introduced and compared with the fault tree analysis. A procedure to transform a fault tree into a Bayesian network is presented. Then the Bayesian network is applied to evaluate the reliability of earth-rock dams. The results indicate that the reliability of earth-rock dams can be evaluated using the Bayesian network in a more rational way.
引文
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