展望建筑物震害预测的发展方向

本文回顾了我国 建筑物震害预测研究和应用的发展过程，评论了各种预测方法的优缺点，指出了各家建议预测方法存在的不确定性，针对存在的问题，提出了减少消除物震害预测方法中不确定性的途径。最后，展望了建筑物震害预测在的实用前景和发展方向。     

基于不同破坏指数的建筑物震害预测不确定性分析

结构地震破坏指数是将结构地震破坏程度进行量化的指标,其应用领域十分广泛。本文针对建筑结构震害预测工作,选取了7种典型的破坏指数,分别以5层和17层钢筋混凝土结构为模型,计算了在给定地震动作用下,结构模型对应于每个破坏指数的结构震害等级。计算结果表明:由于选取的破坏指数以及相应的破坏等级划分不同,建筑物的震害预测结果会存在较大的差异,尤其在接近设防烈度的地震作用下,建筑物的震害预测结果存在很大的不确定性。     

地下结构震害预测研究综述

目前城市建筑物震害预测的研究对象主要集中在地面结构。随着地下空间的大量开发利用,地下结构工程数量急剧增多,其震害预测也越来越受到重视。首先对地下结构进行基本分类,并对其地震破坏形式及震害机理进行系统介绍;然后通过梳理近年来地下结构震害预测的研究成果,总结当前地下结构震害预测的常用方法：震害调查统计方法、数值模拟方法、振动台试验方法以及概率分析方法,并指出各类方法的优缺点;最后分析地下结构震害预测研究的发展趋势,为地下结构工程防震减灾工作提供参考。     

芦山7.0级地震单层砖柱排架厂房震害特征及抗震分析

单层砖柱排架厂房是我国中小企业生产用房的主要形式,目前此类结构仍广泛存在于经济欠发达的地区。历次地震震害资料表明,单层砖柱排架厂房在地震中比较容易损坏。本文通过对芦山地震中单层砖柱排架厂房的震害现场调查,总结了单层砖柱排架厂房的震害特征并分析其破坏原因;统计了几次地震中单层砖柱排架厂房的震害资料,给出了修正的未设防单层砖柱排架厂房地震易损性矩阵;最后,采用了逐步回归法,柱顶位移角法及模糊震害指数法分别对单层砖柱排架厂房进行震害预测,并将各种方法的震害预测结果与实际震害情况进行了对比分析。本文研究对认识该类结构的易损性、震害机理和抗震薄弱部位,指导抗震加固有着重要意义。     

桥梁群体震害预测方法的研究

本文研究了桥梁群体震害预测方法的理论，并采用模糊数学、灰色系统理论和概率分析方法，建立了中、小型桥梁群体震害预测的数学模型，在对江南一带城市地震震害预测工作中桥梁抽样单体震害预测经验法的计算基础上，给出了公路桥梁在不同地震烈度影响耻的单因素评价矩阵，为区域性的地震震害预测工作提供了一个有力的实用工具。     

地下管线震害预测实用方法研究

为了更合理地预测地下管线工程在预期地震作用下的破坏情况,对原有的震害预测方法及其实际应用进行了分析,结合地下管线工程震害现场调查及损失评估工作需求,指出了传统方法存在的不足之处。基于前人给出的地下管线地震破坏经验统计研究成果,以及汶川地震中地下管线工程的震害资料,给出了现役不同材质地下管线的震害率,提出了与震害宏观表现相符、与地震经济损失评估及恢复重建资金评估相衔接的地下管线震害预测实用方法。     

基于BP神经网络模型的多层砖房震害预测方法

针对传统的基于地震烈度的建筑物震害预测方法的不足,本文以地震动峰值加速度作为建筑物震害预测的地震动指标,结合几次大地震中多层砖房的震害实例,提出了一种基于BP神经网络模型的建筑物震害预测方法,模型的输入为反映结构抗震性能的各类物理参数,输出为给定地震动峰值加速度下建筑物破坏状态的概率。研究表明:基于BP网络模型的多层砖房的震害预测结果与震害实例的实际情况比较吻合,本文的思路和方法可推广于其他不同类型的建筑结构的震害预测。     

烟台市震害预测方法讨论会概况

介绍了1983年6月26日至30日在山东省邹县召开的“烟台市震害预测方法讨论会”的概况。运用模糊数学方法进行城市地震灾害的预测是这次会议的新动向,这是对震害预测新途径的一种探索。会上有5个单位的代表作了有关震害预测方法的报告,本文就会议报告的主要内容作了概括介绍,内容包括以模糊数学近似推断方法建立震害预测总体模型、运用综合评判方法进行某类建筑结构地震破坏的预测、用多元统计分析方法进行震害预测。     

黄土生土建筑震害预测研究

通过对黄土地区生土建筑民房的现场调查和测试，分析了各种类型生土建筑的结构特点和自振特性，归纳总结了影响生土建筑震害的主要因素和目前存在的主要问题，在此基础上提出了生土建筑震害预测方法，并给出了初步预测结果。     

基于支持向量机的砌体结构震害预测新方法研究

应用简便、可靠的震害预测方法对我国大量存在的砌体结构进行抗震性能评估，是防震减灾工作的重要举措。基于支持向量机(support vector machine, SVM)理论提出了砌体结构震害预测新方法。首先，详细阐述了基于SVM的砌体结构震害预测新方法的基本原理及步骤；其次，确定了砌体结构的震害影响因子及量化值，建立了震害样本数据库及预测模型；最后，将SVM预测结果分别与实际震害结果和BP神经网络预测结果进行对比分析。结果表明，基于SVM模型的砌体结构震害预测方法步骤简单。结果可靠，在样本数据有限的情况下相对BP神经网络算法有较大的优势，可以用于砌体结构的震害预测。     

Comparison of regional index flood estimation procedures based on the extreme value type I distribution

 A comparison of different methods for estimating T-year events is presented, all based on the Extreme Value Type I distribution. Series of annual maximum flood from ten gauging stations at the New Zealand South Island have been used. Different methods of predicting the 100-year event and the connected uncertainty have been applied: At-site estimation and regional index-flood estimation with and without accounting for intersite correlation using either the method of moments or the method of probability weighted moments for parameter estimation. Furthermore, estimation at ungauged sites were considered applying either a log-linear relationship between at-site mean annual flood and catchment characteristics or a direct log-linear relationship between 100-year events and catchment characteristics. Comparison of the results shows that the existence of at-site measurements significantly diminishes the prediction uncertainty and that the presence of intersite correlation tends to increase the uncertainty. A simulation study revealed that in regional index-flood estimation the method of probability weighted moments is preferable to method of moment estimation with regard to bias and RMSE.     

Determination of flood seasonality from hydrological records / Détermination de la saisonnalité des crues à partir de séries hydrologiques

Abstract

Abstract The identification of flood seasonality is a procedure with many practical applications in hydrology and water resources management. Several statistical methods for capturing flood seasonality have emerged during the last decade. So far, however, little attention has been paid to the uncertainty involved in the use of these methods, as well as to the reliability of their estimates. This paper compares the performance of annual maximum (AM) and peaks-over-threshold (POT) sampling models in flood seasonality estimation. Flood seasonality is determined by two most frequently used methods, one based on directional statistics (DS) and the other on the distribution of monthly relative frequencies of flood occurrence (RF). The performance is evaluated for the AM and three common POT sampling models depending on the estimation method, flood seasonality type and sample record length. The results demonstrate that the POT models outperform the AM model in most analysed scenarios. The POT sampling provides significantly more information on flood seasonality than the AM sampling. For certain flood seasonality types, POT samples can lead to estimation uncertainty that is found in up to ten-times longer AM samples. The performance of the RF method does not depend on the flood seasonality type as much as that of the DS method, which performs poorly on samples generated from complex seasonality distributions.     

青藏高原东北缘地震活动性广义帕累托模型的全域敏感性分析

基于广义帕累托分布构建地震活动性模型,因其输入参数取值难以避免不确定性,导致依据该模型所得的地震危险性估计结果具有不确定性。鉴于此,本文选取青藏高原东北缘为研究区,提出了基于全域敏感性分析的地震危险性估计的不确定性分析流程和方法。首先,利用地震活动性广义帕累托模型,进行研究区地震危险性估计;然后,选取地震记录的起始时间和震级阈值作为地震活动性模型的输入参数,采用具有全域敏感性分析功能的E-FAST方法,对上述两个参数的不确定性以及两参数之间的相互作用对地震危险性估计不确定性的影响进行定量分析。结果表明:地震危险性估计结果（不同重现期的震级重现水平、震级上限及相应的置信区间）对两个输入参数中的震级阈值更为敏感;不同重现期的地震危险性估计结果对震级阈值的敏感程度不同;对不同的重现期而言,在影响地震危险性估计结果的不确定性上,两个输入参数之间存在非线性效应,且非线性效应程度不同。本文提出的不确定性分析流程和方法,可以推广应用于基于其它类型地震活动性模型的地震危险性估计不确定性分析。      

矿产资源潜力预测不确定性评价

矿产潜力预测不确定性评价是矿产定量预测的重要环节,其主要研究内容包括未发现矿床数的不确定性、未发现矿床品位、吨位及资源量的不确定性等.本文简要介绍了矿产预测不确定性的主要来源与评价不确定性的途径与方法,并利用模糊集评价了未发现矿床数、品位、吨位及资源量的不确定性.     

Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation

Model parameters are a source of uncertainty that can easily cause systematic deviation and significantly affect the accuracy of soil moisture generation in assimilation systems. This study addresses the issue of retrieving model parameters related to soil moisture via the simultaneous estimation of states and parameters based on the Common Land Model (CoLM). The state-parameter estimation algorithms AEnKF (Augmented Ensemble Kalman Filter), DEnKF (Dual Ensemble Kalman Filter) and SODA (Simultaneous optimization and data assimilation) are entirely implemented within an EnKF framework to investigate how the three algorithms can correct model parameters and improve the accuracy of soil moisture estimation. The analysis is illustrated by assimilating the surface soil moisture levels from varying observation intervals using data from Mongolian plateau sites. Furthermore, a radiation transfer model is introduced as an observation operator to analyze the influence of brightness temperature assimilation on states and parameters that are estimated at different microwave signal frequencies. Three cases were analyzed for both soil moisture and brightness temperature assimilation, focusing on the progressive incorporation of parameter uncertainty, forcing data uncertainty and model uncertainty. It has been demonstrated that EnKF is outperformed by all other methods, as it consistently maintains a bias. State-parameter estimation algorithms can provide a more accurate estimation of soil moisture than EnKF. AEnKF is the most robust method, with the lowest RMSE values for retrieving states and parameters dealing only with parameter uncertainty, but it possesses disadvantages related to increasing sources of uncertainty and decreasing numbers of observations. SODA performs well under the complex situations in which DEnKF shows slight disadvantages in terms of statistical indicators; however, the former consumes far more memory and time than the latter.     

Ground motion spatial correlation fitting methods and estimation uncertainty

Ground shaking intensity varies spatially in earthquakes, and many studies have estimated correlations of intensity from past earthquake data. This paper presents a framework for quantifying uncertainty in the estimation of correlations and true variability in correlations from earthquake to earthquake. A procedure for evaluating estimation uncertainty is proposed and used to evaluate several methods that have been used in past studies to estimate correlations. The results indicate that a weighted least squares algorithm is most effective in estimating spatial correlation models and that earthquakes with at least 100 recordings are needed to produce informative earthquake-specific estimates of spatial correlations. The proposed procedure is also used to distinguish between estimation uncertainty and the true variability in model parameters that exist in a given data set. The estimation uncertainty is seen to vary between well-recorded and poorly recorded earthquakes, whereas the true variability is more stable.     

Effects of uncertainty in rock-physics models on reservoir parameter estimation using seismic amplitude variation with angle and controlled-source electromagnetics data

This paper investigates the effects of uncertainty in rock-physics models on reservoir parameter estimation using seismic amplitude variation with angle and controlled-source electromagnetics data. The reservoir parameters are related to electrical resistivity by the Poupon model and to elastic moduli and density by the Xu-White model. To handle uncertainty in the rock-physics models, we consider their outputs to be random functions with modes or means given by the predictions of those rock-physics models and we consider the parameters of the rock-physics models to be random variables defined by specified probability distributions. Using a Bayesian framework and Markov Chain Monte Carlo sampling methods, we are able to obtain estimates of reservoir parameters and information on the uncertainty in the estimation. The developed method is applied to a synthetic case study based on a layered reservoir model and the results show that uncertainty in both rock-physics models and in their parameters may have significant effects on reservoir parameter estimation. When the biases in rock-physics models and in their associated parameters are unknown, conventional joint inversion approaches, which consider rock-physics models as deterministic functions and the model parameters as fixed values, may produce misleading results. The developed stochastic method in this study provides an integrated approach for quantifying how uncertainty and biases in rock-physics models and in their associated parameters affect the estimates of reservoir parameters and therefore is a more robust method for reservoir parameter estimation.     

Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network

Due to the complicated nature of environmental processes, consideration of uncertainty is an important part of environmental modelling. In this paper, a new variant of the machine learning-based method for residual estimation and parametric model uncertainty is presented. This method is based on the UNEEC-P (UNcertainty Estimation based on local Errors and Clustering – Parameter) method, but instead of multilayer perceptron uses a “fuzzified” version of the general regression neural network (GRNN). Two hydrological models are chosen and the proposed method is used to evaluate their parametric uncertainty. The approach can be classified as a hybrid uncertainty estimation method, and is compared to the group method of data handling (GMDH) and ordinary kriging with linear external drift (OKLED) methods. It is shown that, in terms of inherent complexity, measured by Akaike information criterion (AIC), the proposed fuzzy GRNN method has advantages over other techniques, while its accuracy is comparable. Statistical metrics on verification datasets demonstrate the capability and appropriate efficiency of the proposed method to estimate the uncertainty of environmental models.     

强震复发概率模型中的参数不确定性研究

在强震发生概率计算过程中, 往往只考虑参数的随机不确定性, 却很少考虑参数的认知不确定性． 本文以布朗过程时间(BPT)模型为例, 利用贝叶斯估计法定量分析了强震平均复发间隔的认知不确定性; 研究了在强震发生概率计算过程中如何考虑这种认知不确定性． 结果表明: 采用不同的强震复发间隔参数估计方法, 所得的参数认知不确定性存在明显差异; 在计算强震发生概率时, 是否考虑参数认知不确定性所得的结果存在较大差异．