基于威布尔分布的大坝位移预警指标拟定

大坝预警指标的拟定是实现大坝安全监控的重要手段。基于系统可靠度原理,在充分阐述威布尔(Weibull)分布三参数估计超过概率权重矩方法的基础上,建立大坝系统位移预警指标估计的Weibull分布模型,并与伽玛(Gamma)分布模型和对数正态(Log-normal)分布模型相比较,得到Weibull分布模型所拟定的大坝位移预警指标更加安全可靠。     

基于POT 模型建立大坝服役性态预警指标

借助预警指标实时辨识大坝服役性态是大坝安全监控的重要手段。传统多依据大坝服役性态效应量测值序列的年极值为分析子样来估计预警指标。本文基于极值理论中的POT(Peaks over Threshold)模型,通过阈值的设定,以超限数据序列作为建模分析的对象,利用广义帕累托分布拟合超限数据子样,结合大坝的失事概率实现预警指标的估计。该方法不研究效应量测值序列的整体分布情况,只关注序列的超限值分布情况;充分考虑了所有较大测值出现的可能,更好地体现了数据样本的分布特征,因此得到的预警指标能更客观地反映工程实际。     

大坝服役性态预警指标拟定的POT模型

借助预警指标实时辨识大坝服役性态是大坝安全监控的重要手段。传统多依据大坝服役性态效应量测值序列的年极值为分析子样来估计预警指标。本文基于极值理论中的POT(Peaks over Threshold)模型，通过门限值的设定，以超限数据序列作为建模分析的对象，利用广义帕累托分布拟合超限数据子样，结合大坝的失事概率实现预警指标的估计。该方法不研究效应量测值序列的整体分布情况，只关注序列的超限值分布情况；充分考虑了所有较大测值出现的可能，更好地体现了数据样本的分布特征，因此得到的预警指标能更客观地反映工程实际。     

基于POT模型的大坝位移预警指标实时估计

大坝服役过程中,对其预警指标进行实时估计具有重要意义。基于极值理论,针对大坝位移历史监测序列,拟定合理的阈值,利用广义帕累托分布(GPD)对超阈值序列进行刻画,结合大坝失事概率,建立位移预警指标估计超阈值(POT)模型。通过实时更新建模序列,完成对位移预警指标的实时估计。针对某混凝土重力拱坝29号坝段某测点在典型时间段2007年~2009年、2007年~2010年和2007年~2011年的位移监测数据,利用POT和传统区间极值(BMM)模型分别完成对2010年、2011年和2012年位移预警指标的估计,验证了POT模型比传统区间极值(BMM)模型更加安全合理。     

混凝土坝服役性态监测效应量安全监控指标拟定方法

表征混凝土坝服役性态变化的监测效应量,其变化客观反映了大坝的工作性态变化,为确保大坝的安全运行,需对相应的监测效应量拟定安全监控指标,传统的方法在假定一定失效概率的前提下拟定监控指标,人为因素影响较大。经对传统方法不足的分析,基于典型小概率方法和层次分析法的思想,提出了混凝土坝服役性态评价等级属性不等区间的划分方法,在此基础上,综合运用最大熵原理,构建反映混凝土坝服役性态变化的监测效应量概率密度函数,提出拟定对应大坝服役性态评价不同等级属性的监测效应量安全监控指标的方法,并通过工程实例验证了所提出方法的可行性和有效性。     

大朝山大坝水平位移安全监控指标的拟定

在对大朝山大坝特点及位移监测资料分析研究的基础上,采用统计分析模型为主的方法,选取温度、库水位和时效等影响因子构建大坝安全监控模型,在模型拟合效果较好的基础上,提出用置信区间法和典型小概率法拟定大朝山大坝典型河床坝段坝顶顺河向水平位移的安全监控指标,为大坝安全运行管理提供准确的预报和预警,也可用来判断大坝工作性态。     

基于RFM模型的大坝实测性态自适应综合评价方法

针对当前大坝安全监控连续时空监测能力弱、单测点馈控范围小的不足,在充分挖掘大坝原型监测数据的基础上,发展了一种弱化主观干扰的RFM(Recency Frequency Magnitude)自适应大坝性态评价模型。首先,模型结合大坝行为的强周期性时序特征,提出“中层型”和“底层型”监测序列的概念;其次,引入K-means聚类算法实现自适应划分监测序列类别;最后,基于RFM指标评分体系,明确各类别所表征的工程健康状态,建立大坝性态的安全评价体系。以某大坝水平位移监测资料为例,详细展示了所提出的大坝运行性态评价模型的应用流程。工程实例表明,该模型评价合理,客观反映了大坝服役状态,有效减少了评价过程中的经验性活动。     

金坑水库浆砌石拱坝变形成因分析

根据金坑拱坝近20a时间的变形监测资料，通过拱坝变形的时间变化规律、空间分布规律、统计模型分析以及三维有限元变形仿真分析，对大坝安全性态作出评价。结果表明，大坝变形性态总体正常，但右坝段的一测点时效位移有向上游位移的趋势，应加强观测，同时也为今后大坝安全运行和管理提供了依据。     

飞来峡大坝坝顶水平位移安全监控指标拟定

根据飞来峡混凝土坝原型监测资料,选取每年最不利工况时坝顶水平位移监测效应量为计算序列,应用大坝典型监测量的小概率法,对大坝典型坝段坝顶水平位移的安全监控指标进行了初步研究,得出了坝顶水平位移的安全监控指标值,并结合2010年的实测值,分析了大坝的运行状况。结果表明,该坝2010年的水位、气温等环境量均未超过2000—2009年最不利工况时的环境量,并且2010年典型坝段坝顶水平位移的实测值均小于安全监控指标。     

基于概率—模糊—区间混合模型和改进分枝限界法的重力坝可靠性分析方法

借助传统概率模型评估重力坝服役安全需明确参数的概率分布,而非概率区间模型所得结论难以准确度量重力坝服役可靠程度,因此,本文提出了基于概率-模糊-区间混合模型和改进分枝限界法的重力坝可靠性分析方法。基于原型、室内试验成果与安全监测资料,结合参数时变模型和区间反演分析方法,建立综合考虑随机变量、模糊变量和区间变量的重力坝可靠性分析混合模型;利用信息熵法和Karush-Kuhn-Tucker(KKT)最优化条件解耦混合模型,通过基于当量正态化法的验算点法(JC法)计算可靠指标;对传统分枝限界法加以改进搜索主要失效模式,采用Ditlevsen窄界限法计算体系可靠度,综合评估重力坝整体服役安全。工程实例分析表明,本文方法可求解多种不确定因素共存的重力坝可靠性分析问题,适用范围较广;计算结果仍为概率可靠指标,表明在符合重力坝运行规律的前提下所选定坝段存在滑动失稳的可能性,与大坝的实际服役情况相吻合。此外,建立的可靠性分析混合模型,经一定的改进和拓展后,亦可用于其他结构工程的可靠性分析。     

A new early warning method for dam displacement behavior based on non-normal distribution function

Traditional methods for early warning of dam displacements usually assume that residual displacements follow a normal distribution. This assumption deviates from the reality, thereby affecting the reliability of early warning results and leading to misjudgments of dam displacement behavior. To solve this problem, this study proposed an early warning method using a non-normal distribution function. A new early warning index was developed using cumulative distribution function (CDF) values. The method of kernel density estimation was used to calculate the CDF values of residual displacements at a single point. The copula function was used to compute the CDF values of residual displacements at multiple points. Numerical results showed that, with residual displacements in a non-normal distribution, the early warning method proposed in this study accurately reflected the dam displacement behavior and effectively reduced the frequency of false alarms. This method is expected to aid in the safe operation of dams.     

基于小概率法的大坝多源监测预警阈值研究

针对大坝预警指标阈值确定方法不完善、监控指标指导作用不强等问题,本文基于霍尔三维结构,系统阐述了大坝预警理论,构建了预警指标体系。通过将大坝监测数据正态分布计算转换成查询柯尔莫哥洛夫分布表,提出了基于小概率法的大坝多源监测预警阈值拟定方法。结合某水库观测数据,拟定该大坝表面沉降及渗流量的预警阈值,分析结果表明,该方法能够有效评估监测指标与历年来抵御不利工况的能力,更好的反映大坝运行状况,极大地提高了监测信息的预警价值。     

APPLICATION OF MAXIMUM ENTROPY PRINCIPLE METHOD TO THE STUDY OF WAVE CLIMATE STATISTICAL CHARACTERISTICS

The Maximum Entropy Principle (MEP) method is elaborated, and the corresponding probability density evaluation method for the random fluctuation system is introduced, the goal of the article is to find the best fitting method for the wave climate statistical distribution. For the first time, a kind of new maximum entropy probability distribution (MEP distribution) expression is deduced in accordance with the second order moment of a random process. Different from all the fitting methods in the past, the MEP distribution can describe the probability distribution of any random fluctuation system conveniently and reasonably. If the moments of the random signal is limited to the second order, that is, the ratio of the root-mean-square value to the mean value of the random variable is obtained from the random sample, the corresponding MEP distribution can be computed according to the deduced expression in this essay. The concept of the wave climate is introduced here, and the MEP distribution is applied to fit the probability density distributions of the significant wave height and spectral peak period. Take the Mexico Gulf as an example, three stations at different locations, depths and wind wave strengths are chosen in the half-closed gulf,the significant wave height and spectral peak period distributions at each station are fitted with the MEP distribution, the Weibull distribution and the Log-normal distribution respectively, the fitted results are compared with the field observations, the results show that the MEP distribution is the best fitting method, and the Weibull distribution is the worst one when applied to the significant wave height and spectral peak period distributions at different locations, water depths and wind wave strengths in the Gulf. The conclusion shows the feasibility and reasonability of fitting wave climate statistical distributions with the deduced MEP distributions in this essay, and furthermore proves the great potential of MEP method to the study of wave statistical properties.     

边坡位移预警指标的实时估计与诊断

边坡预警指标的实时估计与诊断是实现边坡安全监控、预防边坡演变为滑坡的重要手段。安全预警注重考虑极端事件,其关键在于能否准确进行效应量序列分位数分析及刻画其尾部特征。基于极值理论中超阈值(Peaks over Threshold, POT)模型,分时段考虑边坡位移监测序列,借助Hill图法拟定合理的阈值,再利用广义帕累托分布(Generalized Pareto Distribution, GPD)对超阈值序列进行拟合分析,渐进地刻画位移序列分布的尾部特征,分析其抵御已经历荷载的能力。假定边坡失事概率,得到位移预警指标实时估计序列,挖掘或评估出该边坡面临可能发生极端荷载时的抵御能力。最后基于突变理论中尖点突变模型对位移预警指标实时估计序列进行突变诊断,结合具体边坡工程实例,验证了POT-突变理论混合模型的合理性。     

基于非连续变形分析的重力坝变形预警指标

传统的确定大坝安全监控指标的方法都采用小变形和连续性假设,不能把重力坝的整体抗滑稳定与大坝变形有机地结合起来。在研究影响混凝土重力坝安全的因素和预警指标体系的基础上,引入基于块体理论的非连续变形分析,考虑坝体和坝基整体抗滑稳定,采用强度折减系数法分析了三峡大坝左岸厂房3#坝段的整体抗滑稳定性,得到了安全系数与大坝位移之间的内在联系。根据失稳判据和坝体位移与强度折减系数之间的关系曲线确定了大坝的结构性态,并估计出大坝弹性状态和承载极限状态的变形预警指标。     

Nonstationary Flood Frequency Analysis for Annual Flood Peak and Volume Series in Both Univariate and Bivariate Domain

Flood frequency analysis for practical application is traditionally based on the assumption of stationarity, but this assumption has been open to doubt in recent years. A number of studies have focused on the nonstationary flood frequency analysis, and the associated causes of nonstationarity. In this study, the annual maximum flood peak and flood volume of Wangkuai reservoir watershed were used, and several univariate and bivariate models were established to investigate the nonstationary flood frequency, with the distribution parameters changing over the climate indices (NPO, Niño3) and the check dam indices (CDIp, CDIv). In the univariate models, the Weibull distribution performed best and exhibited an undulate behavior for both flood peak and volume, which tended to describe the nonstationarity reasonably well. The bivariate models were constructed using copulas, of which the optimal Weibull distribution in the univariate flood frequency analysis was considered as marginal distributions within the joint distribution. The results showed that the Gumbel-Hougaard copula offered the best joint distribution, and most of the probability isolines crossed each other, which demonstrated the possibility that the occurrence of combinations of the flood peak and volume may be the same under multiple effects of phase changes in the climate patterns and certain human activities (i.e. soil and water conservation). The most likely events were elaborated in diagrams, and the associated combinations of the flood peak and volume were smaller than that estimated by the fixed parameters (i.e. stationary condition) during most of the study period, while it was the opposite in 1956, 1959 and 1963. The results highlight the necessity of nonstationary flood frequency analysis under various conditions in both univariate and multivariate domains.     

功果桥大坝坝顶水平位移安全监控指标拟定

大坝监测控制指标关乎其运行安全,对运行期监测数据进行指标拟定具有重要意义。以功果桥大坝为工程依托,通过典型小概率法和统计模型的置信区间法分别计算了典型坝段的坝顶水平位移监控指标,结合设计值与历史极值对监控指标进行了综合拟定。结果表明:典型小概率法计算结果和历史数据较为吻合,置信区间法计算极值范围相对较宽,反映了其变形余量,需结合设计值及外在条件的变化进行综合拟定和修正。研究成果对计算坝体其他监测数据的监控指标具有参考意义,可为大坝运行管理和状态评估提供依据。