考虑土-结构相互作用和岩土参数不确定性的核电厂结构地震响应分析

针对核电厂结构,在考虑土-结构相互作用(SSI)的情况下进行随机地震反应分析,探讨地基岩土参数的不确定性对反应堆厂房楼层反应谱(FRS)的影响。运用ANSYS软件模块建立核电厂(NPP)结构有限元模型,通过设置边界弹簧单元和阻尼装置来考虑SSI效应;并且通过设置具有概率意义的弹簧刚度和阻尼系数,来模拟土特性参数的不确定性。随机响应分析与确定性分析的结果对比,揭示了岩性地基条件下SSI效应对核电厂FRS的影响以及地基岩土参数不确定性对FRS的影响程度。研究表明,在岩性地基条件下,亦不应忽略SSI效应;考虑SSI效应的随机分析模型同确定性模型相比,二者的分析结果较为接近,两方法都可用于NPP的FRS敏感性分析评估之中,并可进行相互比照。     

A simulation‐based framework for risk assessment and probabilistic sensitivity analysis of base‐isolated structures

A versatile, simulation‐based framework for risk assessment and probabilistic sensitivity analysis of base‐isolated structures is discussed in this work. A probabilistic foundation is used to address the various sources of uncertainties, either excitation or structural, and to characterize seismic risk. This risk is given, in this stochastic setting, by some statistics of the system response over the adopted probability models and stochastic simulation is implemented for its evaluation. An efficient, sampling‐based approach is also introduced for establishing a probabilistic sensitivity analysis to identify the importance of each of the uncertain model parameters in affecting the overall risk. This framework facilitates use of complex models for the structural system and the excitation. The adopted structural model explicitly addresses nonlinear characteristics of the isolators and of any supplemental dampers, and the effect of seismic pounding of the base to the surrounding retaining walls. An efficient stochastic ground motion model is also discussed for characterizing future near‐fault ground motions and relating them to the seismic hazard for the structural site. An illustrative example is presented that emphasizes the results from the novel probabilistic sensitivity analysis and their dependence on seismic pounding occurrences and on addition of supplemental dampers. Copyright © 2011 John Wiley & Sons, Ltd.     

Analytical stochastic solutions of saltwater/freshwater interface in coastal aquifers

Three analyitcal solutions of saltwater intrusion under uncertain hydrologic, hydrogeologic, and pumping conditions are presented. These solutions extends the existing deterministic, sharp interface solutions to stochastic ones. The randomness is represented in terms of statistical measures of mean, standard deviation and covariance. The analysis is based on perturbation using Taylor series expansion. Simulations based on probability distribution are conducted for verification.     

Analytical stochastic solutions of saltwater/freshwater interface in coastal aquifers

Three analyitcal solutions of saltwater intrusion under uncertain hydrologic, hydrogeologic, and pumping conditions are presented. These solutions extends the existing deterministic, sharp interface solutions to stochastic ones. The randomness is represented in terms of statistical measures of mean, standard deviation and covariance. The analysis is based on perturbation using Taylor series expansion. Simulations based on probability distribution are conducted for verification.     

Assessing environmental risks through fuzzy parameterized probabilistic analysis

A fuzzy parameterized probabilistic analysis (FPPA) method was developed in this study to assess risks associated with environmental pollution-control problems. FPPA integrated environmental transport modeling, fuzzy transformation, probabilistic risk assessment, fuzzy risk quantification into a general risk assessment framework, and was capable of handling uncertainties expressed as fuzzy-parameterized stochastic distributions. The proposed method was applied to two environmental pollution problems, with one being about the point-source pollution in a river system with uncertain water quality parameters and the other being concerned with groundwater contaminant plume from waste landfill site with poorly known contaminant physical properties. The study results indicated that the complex uncertain features had significant impacts on modeling and risk-assessment outputs; the degree of impacts of modeling parameters were highly dependent on the level of imprecision of these parameters. The results also implied that FPPA was capable of addressing vagueness or imprecision associated with probabilistic risk evaluation, and help generate risk outputs that could be elucidated under different possibilistic levels. The proposed method could be used by environmental managers to evaluate trade-offs involving risks and costs, as well as identify management solutions that sufficiently hedge against dual uncertainties.     

A method for generating fully non-stationary and spectrum-compatible ground motion vector processes

Earthquake ground motion spatial variability can influence significantly the response of certain structures. In order to accurately evaluate probabilistic characteristics of the seismic response of structures, the Monte Carlo simulation technique is still the only universal method of analysis when strong nonlinearities and input uncertainties are involved. Consequently, realizations of ground motion time histories taking into account both time and spatial variability need to be generated. Furthermore, for some design applications, the generated time histories must also satisfy the provision imposed by certain seismic codes stating that they have to be also response-spectrum-compatible. For these purposes, a spectral-representation-based methodology for generating fully non-stationary and spectrum-compatible ground motion vector processes at a number of locations on the ground surface is proposed in this paper. The simulated time histories do not require any iterations on the individual generated sample functions so that Gaussianity and prescribed coherence are suitably preserved. The methodology has also the advantage of providing the fully non-stationary and spectrum-compatible cross-spectral density matrix of the ground motion time-histories that can be used for reliability studies in an analytic stochastic fashion.     

The Influence of Ground-Motion Variability in Earthquake Loss Modelling

Earthquake loss models are subject to many large uncertainties associated with the input parameters that define the seismicity, the ground motion, the exposure and the vulnerability characteristics of the building stock. In order to obtain useful results from a loss model, it is necessary to correctly identify and characterise these uncertainties, incorporate them into the calculations, and then interpret the results taking account of the influence of the uncertainties. An important element of the uncertainty will always be the aleatory variability in the ground-motion prediction. Options for handling this variability include following the traditional approach used in site-specific probabilistic seismic hazard assessment or embedding the variability within the vulnerability calculations at each location. The physical interpretation of both of these approaches, when applied to many sites throughout an urban area to assess the overall effects of single or multiple earthquake events, casts doubts on their validity. The only approach that is consistent with the real nature of ground-motion variability is to model the shaking component of the loss model by triggering large numbers of earthquake scenarios that sample the magnitude and spatial distributions of the seismicity, and also the distribution of ground motions for each event as defined by the aleatory variability.     

Probabilistic sensitivity and modeling of two-dimensional transport in porous media

A reliability approach is used to develop a probabilistic model of two-dimensional non-reactive and reactive contaminant transport in porous media. The reliability approach provides two important quantitative results: an estimate of the probability that contaminant concentration is exceeded at some location and time, and measures of the sensitivity of the probabilistic outcome to likely changes in the uncertain variables. The method requires that each uncertain variable be assigned at least a mean and variance; in this work we also incorporate and investigate the influence of marginal probability distributions. Uncertain variables includex andy components of average groundwater flow velocity,x andy components of dispersivity, diffusion coefficient, distribution coefficient, porosity and bulk density. The objective is to examine the relative importance of each uncertain variable, the marginal distribution assigned to each variable, and possible correlation between the variables. Results utilizing a two-dimensional analytical solution indicate that the probabilistic outcome is generally very sensitive to likely changes in the uncertain flow velocity. Uncertainty associated with dispersivity and diffusion coefficient is often not a significant issue with respect to the probabilistic analysis; therefore, dispersivity and diffusion coefficient can often be treated for practical analysis as deterministic constants. The probabilistic outcome is sensitive to the uncertainty of the reaction terms for early times in the flow event. At later times, when source contaminants are released at constant rate throughout the study period, the probabilistic outcome may not be sensitive to changes in the reaction terms. These results, although limited at present by assumptions and conceptual restrictions inherent to the closed-form analytical solution, provide insight into the critical issues to consider in a probabilistic analysis of contaminant transport. Such information concerning the most important uncertain parameters can be used to guide field and laboratory investigations.     

Probabilistic sensitivity and modeling of two-dimensional transport in porous media

A reliability approach is used to develop a probabilistic model of two-dimensional non-reactive and reactive contaminant transport in porous media. The reliability approach provides two important quantitative results: an estimate of the probability that contaminant concentration is exceeded at some location and time, and measures of the sensitivity of the probabilistic outcome to likely changes in the uncertain variables. The method requires that each uncertain variable be assigned at least a mean and variance; in this work we also incorporate and investigate the influence of marginal probability distributions. Uncertain variables includex andy components of average groundwater flow velocity,x andy components of dispersivity, diffusion coefficient, distribution coefficient, porosity and bulk density. The objective is to examine the relative importance of each uncertain variable, the marginal distribution assigned to each variable, and possible correlation between the variables. Results utilizing a two-dimensional analytical solution indicate that the probabilistic outcome is generally very sensitive to likely changes in the uncertain flow velocity. Uncertainty associated with dispersivity and diffusion coefficient is often not a significant issue with respect to the probabilistic analysis; therefore, dispersivity and diffusion coefficient can often be treated for practical analysis as deterministic constants. The probabilistic outcome is sensitive to the uncertainty of the reaction terms for early times in the flow event. At later times, when source contaminants are released at constant rate throughout the study period, the probabilistic outcome may not be sensitive to changes in the reaction terms. These results, although limited at present by assumptions and conceptual restrictions inherent to the closed-form analytical solution, provide insight into the critical issues to consider in a probabilistic analysis of contaminant transport. Such information concerning the most important uncertain parameters can be used to guide field and laboratory investigations.     

Impact of model free parameters and sea-level rise uncertainties on 20-years shoreline hindcast: the case of Truc Vert beach (SW France)

Shoreline change is driven by various complex processes interacting at a large range of temporal and spatial scales, making shoreline reconstructions and predictions challenging and uncertain. Despite recent progress in addressing uncertainties related to the physics of sea-level rise, very little effort is made towards understanding and reducing the uncertainties related to wave-driven shoreline response. To fill this gap, the uncertainties associated with the long-term modelling of shoreline change are analysed at a high-energy cross-shore transport dominated site. Using the state-of-the-art LX-Shore shoreline change model, we produce a probabilistic shoreline reconstruction, based on 3000 simulations over the past 20 years at Truc Vert beach, southwest France, whereby sea-level rise rate, depth of closure and three model free parameters are considered uncertain variables. We further address the relative impact of each source of uncertainty on the model results performing a Global Sensitivity Analysis. This analysis shows that the shoreline changes are mainly sensitive to the three parameters of the wave-driven model, but also that the sensitivity to each of these parameters is strongly modulated seasonally and interannually, in relation with wave energy variability, and depends on the time scale of interest. These results have strong implications on the model skill sensitivity to the calibration period as well as for the predictive skill of the model in a context of future climate change affecting wave climate and extremes. © 2020 John Wiley & Sons, Ltd.     

Probabilistic site-dependent non-linear spectra

This paper presents a probabilistic approach to the estimation of lateral strengths required to provide an adequate control of inelastic deformations in structures during severe earthquake ground motions. In contrast to a deterministic approach, the approach presented herein accounts explicitly for the variability of the response of non-linear systems due to the inherent uncertainties in the intensity and characteristics of the input excitation by considering the probability distribution of maximum inelastic strength demands. This study is based on the computation of non-linear strength demands of single-degree-of-freedom (SDOF) systems experiencing different levels of inelastic deformation when subjected to 124 recorded earthquake ground motions. Using empirical cumulative distribution functions site-dependent probabilistic non-linear spectra were computed for six probabilities of exceedance of different levels of inelastic deformation. It is concluded that the lateral strength required to control displacement ductility demands is significantly affected by the maximum tolerable inelastic deformation, the system's period of vibration, the local site conditions and the level of risk in exceeding the maximum tolerable deformations.     

Effect of travelling waves on stochastic seismic response and dynamic reliability of a long-span bridge on soft soil

It is generally known that the variability of earthquake ground motion is mainly in time and space. To investigate the impact of this variability on the seismic performance of a long-span flexible structure, we discuss the seismic dynamic responses of a real bridge subjected to stochastic seismic ground motion. We incorporate the effect of wave passage by means of the method of probability density evolution based on dynamic time-history analysis from the perspective of stochastic dynamics. First, we introduce the theory of probability density evolution and a category of stochastic seismic model. We then conduct a series of deterministic seismic dynamic analyses of the bridge to establish the probability density equation. Eventually, we obtain the probability information at the level of the probability density function of the seismic response by solving the probability-density evolution equation. The results show that the impact of travelling waves on a long-span structure is related to the characteristics of the earthquake ground motion and the structure, and that travelling waves increase the variability of the seismic response.     

Stochastic 1D site response analysis at a site in central Italy

Site response analysis is strongly influenced by the uncertainty associated to the definition of soil properties and model parameters. Deterministic, or even parametric analyses are unable to systematically assess such uncertainty, since the site characterisation can hardly be sufficiently accurate for a deterministic prediction of site response and alternative approaches are hence needed. A fully stochastic procedure for estimating the site amplification of ground motion is proposed and applied to a case study in central Italy. The methodology allows to take into account the record-to-record variability in an input ground motion and the uncertainty in dynamic soil properties and in the definition of the soil model. In particular, their effect on response spectra at the ground surface is evaluated.     

Capacity, fragility and damage in reinforced concrete buildings: a probabilistic approach

The main goals of this article are to analyze the use of simplified deterministic nonlinear static procedures for assessing the seismic response of buildings and to evaluate the influence that the uncertainties in the mechanical properties of the materials and in the features of the seismic actions have in the uncertainties of the structural response. A reinforced concrete building is used as a guiding case study. In the calculation of the expected spectral displacement, deterministic nonlinear static methods are simple and straightforward. For not severe earthquakes these approaches lead to somewhat conservative but adequate results when compared to more sophisticated procedures involving nonlinear dynamic analyses. Concerning the probabilistic assessment, the strength properties of the materials, concrete and steel, and the seismic action are considered as random variables. The Monte Carlo method is then used to analyze the structural response of the building. The obtained results show that significant uncertainties are expected; uncertainties in the structural response increase with the severity of the seismic actions. The major influence in the randomness of the structural response comes from the randomness of the seismic action. A useful example for selected earthquake scenarios is used to illustrate the applicability of the probabilistic approach for assessing expected damage and risk. An important conclusion of this work is the need of broaching the fragility of the buildings and expected damage assessment issues from a probabilistic perspective.     

Deterministic and probabilistic representation of near-field pulse-like ground motion

The response and damage assessment of engineering structures under near-field ground motions is currently of great interest. Near-field ground motion with directivity focusing or fling effects produces pulse-like ground motion that has characteristics different from those of ordinary records. This paper develops simple deterministic and probabilistic models for near-field pulse-like ground motions. These models belong to the class of engineering models that aim to replicate some of the gross features observed in near-field records. The ground velocity is expressed as a steady-state function or a stationary random process modulated by an envelope function. Both models account for the non-stationarity and the multiple pulses in the ground velocity. While the deterministic model is similar to some of the models developed earlier, the probabilistic model facilitates handling uncertainties in the ground motion and variability in the structure's properties. For instance, this model combined with structural reliability methods can be used for reliability assessment of structures under near-field random ground motion. The reduction of the structural response by adding supplemental dampers is also investigated.     

Generating probabilistic estimates of hydrological response for Irish catchments using a weather generator and probabilistic climate change scenarios

In accounting for uncertainties in future simulations of hydrological response of a catchment, two approaches have come to the fore: deterministic scenario‐based approaches and stochastic probabilistic approaches. As scenario‐based approaches result in a wide range of outcomes, the role of probabilistic‐based estimates of climate change impacts for policy formulation has been increasingly advocated by researchers and policy makers. This study evaluates the impact of climate change on seasonal river flows by propagating daily climate time series, derived from probabilistic‐based climate scenarios using a weather generator (WGEN), through a set of conceptual hydrological models. Probabilistic scenarios are generated using two different techniques. The first technique used probabilistic climate scenarios developed from statistically downscaled scenarios for Ireland, hereafter called SDprob. The second technique used output from 17 global climate models (GCMs), all of which participated in CMIP3, to generate change factors (hereafter called CF). Outputs from both the SDprob and the CF approach were then used in combination with WGEN to generate daily climate scenarios for use in the hydrological models. The range of simulated flow derived with the CF method is in general larger than those estimated with the SDprob method in winter and vice versa because of the strong seasonality in the precipitation signal for the 17 GCMs. Despite this, the simulated probability density function of seasonal mean streamflow estimated with both methods is similar. This indicates the usefulness of the SDprob or probabilistic approach derived from regional scenarios compared with the CF method that relies on sampling a diversity of response from the GCMs. Irrespective of technique used, the probability density functions of seasonal mean flow produced for four selected basins is wide indicating considerable modelling uncertainties. Such a finding has important implications for developing adaptation strategies at the catchment level in Ireland. Copyright © 2011 John Wiley & Sons, Ltd.     

Non‐parametric–parametric model for random uncertainties in non‐linear structural dynamics: application to earthquake engineering

This paper deals with the transient response of a non‐linear dynamical system with random uncertainties. The non‐parametric probabilistic model of random uncertainties recently published and extended to non‐linear dynamical system analysis is used in order to model random uncertainties related to the linear part of the finite element model. The non‐linearities are due to restoring forces whose parameters are uncertain and are modeled by the parametric approach. Jayne's maximum entropy principle with the constraints defined by the available information allows the probabilistic model of such random variables to be constructed. Therefore, a non‐parametric–parametric formulation is developed in order to model all the sources of uncertainties in such a non‐linear dynamical system. Finally, a numerical application for earthquake engineering analysis is proposed concerning a reactor cooling system under seismic loads. Copyright © 2003 John Wiley & Sons, Ltd.     

Stochastic dynamic analysis of a layered half-space

A stochastic thin-layer method is developed for the analysis of wave propagation in a layered half-space. A random field of shear moduli in the layered system is considered in terms of multiple correlated random variables. Expanding the random moduli and uncertain responses by means of Hermite polynomial chaos expansions and applying the Galerkin method in the spatial as well as stochastic domains, stochastic versions of thin-layer methods for a layered half-space in plane strain and antiplane shear are obtained. In order to represent the infinite half-space, continued-fraction absorbing boundary conditions are included in the thin-layer models of the half-space. Using these stochastic methods, dynamic responses of a layered half-space subjected to line loads are examined. Means, coefficients of variance, and probability density functions of the half-space responses with a varying correlation coefficient of the shear moduli are computed and verified by comparison with Monte Carlo simulations. It is demonstrated that accurate probabilistic dynamic analysis is possible using the developed stochastic thin-layer methods for a layered half-space.     

A fuzzy-Markov-chain-based analysis method for reservoir operation

In this study, a fuzzy-Markov-chain-based stochastic dynamic programming (FM-SDP) method is developed for tackling uncertainties expressed as fuzzy sets and distributions with fuzzy probability (DFPs) in reservoir operation. The concept of DFPs used in Markov chain is presented as an extended form for expressing uncertainties including both stochastic and fuzzy characteristics. A fuzzy dominance index analysis approach is proposed for solving multiple fuzzy sets and DPFs in the proposed FM-SDP model. Solutions under a set of α-cut levels and fuzzy dominance indices can be generated by solving a series of deterministic submodels. The developed method is applied to a case study of a reservoir operation system. Solutions from FM-SDP provide a range of desired water-release policies under various system conditions for reservoir operation decision makers, reflecting dynamic and dual uncertain features of water availability simultaneously. The results indicate that the FM-SDP method could be applicable to practical problems for decision makers to obtain insight regarding the tradeoffs between economic and system reliability criteria. Willingness to obtain a lower benefit may guarantee meeting system-constraint demands; conversely, a desire to acquire a higher benefit could run into a higher risk of violating system constraints.     

Model updating using noisy response measurements without knowledge of the input spectrum

A new probabilistic model identification methodology is proposed using measured response time histories only. The proposed approach requires that the number of independent measurements is larger than the number of independent excitations. Under this condition, no input measurements or any information regarding the stochastic model of the input is required. Specifically, the method does not require the response to be stationary and does not assume any knowledge of the parametric form of the spectral density of the input. Therefore, the method has very wide applicability. The proposed approach allows one to obtain not only the most probable values of the updated model parameters but also their associated uncertainties using only one set of response data. It is found that the updated probability distribution can be well approximated by a Gaussian distribution centered at the most probable values of the parameters. Examples are presented to illustrate the proposed method. Copyright © 2004 John Wiley & Sons, Ltd.