模糊检测数据下服役钢筋混凝土桥梁的动态可靠度评估

针对服役桥梁的可靠性评估中无法基于不完备、缺陷的检测数据准确获得变量统计特征的问题,提出了模糊检测数据下既有钢筋混凝土桥梁的动态可靠性分析方法。以氯盐腐蚀下引起钢筋截面积减弱,导致结构承载能力和可靠性能降低为主线,采用隶属函数来描述缺陷信息,通过等概率原理将模糊变量转化成当量随机变量,进而基于任意概率密度函数的Monte Carlo抽样方法,分析了一座实桥的动态可靠指标变化情况。结果表明：模糊检测数据的均值对钢筋截面减弱影响较大,其中保护层厚度均值对其影响最为显著,这同样表现在对可靠指标的影响中;变量模糊幅度的影响较小,在确定隶属函数的过程中,更应注重隶属函数的均值。     

模糊数学方法在地震预测中的应用研究及其实效

20世纪80年代以来,针对地震孕育期间孕震信息的模糊性,国内开展了多项模糊数学方法在地震预测中的应用研究。应用这些方法可以将多项指标所显示的孕震的模糊信息转化为清晰的地震预测结果。现仅介绍模糊聚类分析和模糊综合评判两种常用方法在地震预测中的应用研究概况及其预测实效。     

Suspended load estimation using L1-fuzzy regression,L2-fuzzy regression and MARS-fuzzy regression models

ABSTRACT

The problem of estimation of suspended load carried by a river is an important topic for many water resources projects. Conventional estimation methods are based on the assumption of exact observations. In practice, however, a major source of natural uncertainty is due to imprecise measurements and/or imprecise relationships between variables. In this paper, using the Multivariate Adaptive Regression Splines (MARS) technique, a novel fuzzy regression model for imprecise response and crisp explanatory variables is presented. The investigated fuzzy regression model is applied to forecast suspended load by discharge based on two real-world datasets. The accuracy of the proposed method is compared with two well-known parametric fuzzy regression models, namely, the fuzzy least-absolutes model and the fuzzy least-squares model. The comparison results reveal that the MARS-fuzzy regression model performs better than the other models in suspended load estimation for the particular datasets. This comparison is done based on four goodness-of-fit criteria: the criterion based on similarity measure, the criterion based on absolute errors and the two objective functions of the fuzzy least-absolutes model and the fuzzy least-squares model. The proposed model is general and can be used for modelling natural phenomena whose available observations are reported as imprecise rather than crisp.

Editor D. Koutsoyiannis; Associate editor H. Aksoy     

结构振动的模糊建模与模糊控制规则提取

模糊振动控制中存在的模糊控制规则的建立大都依赖于主观经验的现状。对此本文提出了一种通过对结构振动模糊建模来产生控制规则的方法。首先，通过对系统运动状态变量的模糊化，建立结构振动的模糊关系模型；其次通过对结构振动的模糊关系模型的分析，提取出模糊控制规则；最后，通过一个单自由度体系的数值仿真方法进行了验证。     

A new fuzzy linear regression approach for dissolved oxygen prediction

Abstract

A new method for fuzzy linear regression is proposed to predict dissolved oxygen using abiotic factors in a riverine environment, in Calgary, Canada. The proposed method is designed to accommodate fuzzy regressors, regressand and coefficients, i.e. representing full system uncertainty. The regression equation is built to minimize the distance between fuzzy numbers, and generalizes to crisp regression when crisp parameters are used. The method is compared to two existing fuzzy linear regression techniques: the Tanaka method and the Diamond method. The proposed new method outperforms the existing methods with higher Nash-Sutcliffe efficiency, and lower RMSE, AIC and total fuzzy distance. The new method demonstrates that nonlinear membership functions are more suitable for representing uncertain environmental data than the typical triangular representations. A result of this research is that low DO prediction is improved and consequently the approach can be used for risk analysis by water resource managers.

Editor D. Koutsoyiannis; Associate editor T. Okruszko     

Probabilistic-fuzzy health risk modeling

Health risk analysis of multi-pathway exposure to contaminated water involves the use of mechanistic models that include many uncertain and highly variable parameters. Currently, the uncertainties in these models are treated using statistical approaches. However, not all uncertainties in data or model parameters are due to randomness. Other sources of imprecision that may lead to uncertainty include scarce or incomplete data, measurement error, data obtained from expert judgment, or subjective interpretation of available information. These kinds of uncertainties and also the non-random uncertainty cannot be treated solely by statistical methods. In this paper we propose the use of fuzzy set theory together with probability theory to incorporate uncertainties into the health risk analysis. We identify this approach as probabilistic-fuzzy risk assessment (PFRA). Based on the form of available information, fuzzy set theory, probability theory, or a combination of both can be used to incorporate parameter uncertainty and variability into mechanistic risk assessment models. In this study, tap water concentration is used as the source of contamination in the human exposure model. Ingestion, inhalation and dermal contact are considered as multiple exposure pathways. The tap water concentration of the contaminant and cancer potency factors for ingestion, inhalation and dermal contact are treated as fuzzy variables while the remaining model parameters are treated using probability density functions. Combined utilization of fuzzy and random variables produces membership functions of risk to individuals at different fractiles of risk as well as probability distributions of risk for various alpha-cut levels of the membership function. The proposed method provides a robust approach in evaluating human health risk to exposure when there is both uncertainty and variability in model parameters. PFRA allows utilization of certain types of information which have not been used directly in existing risk assessment methods.     

A Warning System for Stromboli Volcano Based on Statistical Analysis

In this paper we describe a warning system based on statistical analysis for the purpose of monitoring ground deformation at the Sciara del Fuoco (Stromboli Volcano, Sicily). After a statistical analysis of ground deformation time-series measured at Stromboli by the monitoring system known as THEODOROS (THEOdolite and Distancemeter Robot Observatory of Stromboli), the paper describes the solution adopted for implementing the warning system. A robust statistical index has been defined in order to evaluate the movements of the area. A fuzzy approach has been proposed to evaluate an AI (Alarm Intensity) index which indicates the level of hazard of the Sciara del Fuoco sliding.     

Fuzzy computing based rainfall–runoff model for real time flood forecasting

This paper analyses the skills of fuzzy computing based rainfall–runoff model in real time flood forecasting. The potential of fuzzy computing has been demonstrated by developing a model for forecasting the river flow of Narmada basin in India. This work has demonstrated that fuzzy models can take advantage of their capability to simulate the unknown relationships between a set of relevant hydrological data such as rainfall and river flow. Many combinations of input variables were presented to the model with varying structures as a sensitivity study to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff. The most appropriate set of input variables was determined, and the study suggests that the river flow of Narmada behaves more like an autoregressive process. As the precipitation is weighted only a little by the model, the last time‐steps of measured runoff are dominating the forecast. Thus a forecast based on expected rainfall becomes very inaccurate. Although good results for one‐step‐ahead forecasts are received, the accuracy deteriorates as the lead time increases. Using the one‐step‐ahead forecast model recursively to predict flows at higher lead time, however, produces better results as opposed to different independent fuzzy models to forecast flows at various lead times. Copyright © 2004 John Wiley & Sons, Ltd.     

模糊数学方法在分析日本东海地区地震前兆中的应用

本文把模糊模式识别的直接方法用于检测日本东海地区地震活动性、体积应变、地下水位、地下水氡含量、地倾斜和潮位中的前兆变化。此方法可消除诸如降雨和气温变化等干扰,使我们能更清晰地识别出地震前兆。此外,还把模糊相似方法和模糊聚类方法应用于识别地震空区及检验地震活动随时间变化的相似性。上述方法在日本其他地区同样有效,即将编入日本气象研究所板内地震预报计划中的计算机系统内。     

模糊数学方法在地震综合预报中的应用

本文的目的,是把模糊数学中发展起来的模糊识别的直接方法与间接方法,试用于地震综合预报.模糊识别的直接方法,就是直接由前兆指标的从属函数来估计地震危险性并进行预报,其效果依赖于建立前兆指标的从属函数的技巧.文中以地下水氡含量、视电阻率、波速等前兆资料为基础,提出了一种主要依据前兆变化速率及相关系数,并使用其它途径来建立从属函数的具体方法与公式.使用这样的从属函数之后,可以更好地识别出前兆异常,并且更容易区分出异常的起始、终结或发生明显转折的时期.模糊识别的间接方法,本文中采用的是模糊聚类分析方法,它与选取表示型类区别的相似系数或距离有关.我们这里采用的是基于模糊等价关系的聚类分析方法.此方法包括以下步骤:将一系列样本按彼此间的相似程度建立一个模糊相容关系;通过合成运算把这个模糊相容关系改造为一个模糊等价关系;选择一个适当的参数的数值,并对原始样本进行分类.选取某一给定地区的地震活动性的一些统计指标,或者选取由多手段单台或单手段多台得出的一些前兆数据(地形变、地下水氡含量、视电阻率等等),就可以使用上述模糊聚类分析方法来进行地震综合预报.作为说明此方法的例子,文中给出了对我国西部强震及中强震得出的一些初步结果.由所得结果可以看出,利用模      

A field demonstration of the entropy-weighted fuzzy DRASTIC method for groundwater vulnerability assessment

Abstract

Groundwater vulnerability assessment based on the DRASTIC index has been widely used since the 1980s to map potential risks of groundwater contamination. However, its applicability and usefulness are affected by two uncertain and subjective factors. One is the discretization of continuous input variables and the other is the assignment of different weights to the index variables. In this study, an entropy-weighted fuzzy-optimization approach was developed to augment and improve the classic DRASTIC method by reducing the uncertainties associated with variable discretization and weight assignment. The modified DRASTIC method was applied to a study site in Shandong, north China. The entropy-weighted fuzzy-optimization approach is shown to provide a more rigorous delineation of the relative vulnerability distribution. Meanwhile, the new approach does not require the use of more parameters. The results suggest that this approach significantly improves and enhances the ability of the classic DRASTIC method in a more systematic and rigorous way.

Editor D. Koutsoyiannis

Citation Yu, C., Zhang, B.X., Yao, Y.Y., Meng, F.H., and Zheng, C.M., 2012. A field demonstration of the entropy-weighted fuzzy DRASTIC method for groundwater vulnerability assessment. Hydrological Sciences Journal, 57 (7), 1420–1432.     

1996年11月9日长江口以东6.1级地震的模糊预测和模糊跟踪

把模糊数学方法引入地震预测已有十余年的历史,在众多的结果中选择模糊编网、灰色模糊模型、日本海沟和本地区的模糊相关及显著地震发生后的模糊跟踪,作为长、中、短（临）和震后趋势判断的各个环节,构成了地震的模糊预测方法。1996年11月9日（简称“11.9”飞下同）长江口以东海中6.1级地震前后,根据模糊方法作了各阶段预测,分别列入近几年的上海市地 震局地震会商报告中,震前预测和震后跟踪结果与实际基本一致。可以说这次地震是模糊方法取得较为成功的一个震例。     

Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey / Estimation de l’évaporation journalière du bac dans l’Ouest de la Turquie par des modèles à base de logique floue

Abstract

Abstract Evaporation is one of the fundamental elements in the hydrological cycle, which affects the yield of river basins, the capacity of reservoirs, the consumptive use of water by crops and the yield of underground supplies. In general, there are two approaches in the evaporation estimation, namely, direct and indirect. The indirect methods such as the Penman and Priestley-Taylor methods are based on meteorological variables, whereas the direct methods include the class A pan evaporation measurement as well as others such as class GGI-3000 pan and class U pan. The major difficulty in using a class A pan for the direct measurements arises because of the subsequent application of coefficients based on the measurements from a small tank to large bodies of open water. Such difficulties can be accommodated by fuzzy logic reasoning and models as alternative approaches to classical evaporation estimation formulations were applied to Lake Egirdir in the western part of Turkey. This study has three objectives: to develop fuzzy models for daily pan evaporation estimation from measured meteorological data, to compare the fuzzy models with the widely-used Penman method, and finally to evaluate the potential of fuzzy models in such applications. Among the measured meteorological variables used to implement the models of daily pan evaporation prediction are the daily observations of air and water temperatures, sunshine hours, solar radiation, air pressure, relative humidity and wind speed. Comparison of the classical and fuzzy logic models shows a better agreement between the fuzzy model estimations and measurements of daily pan evaporation than the Penman method.     

Development of a fuzzy logic-based rainfall-runoff model

Abstract

Rainfall-runoff models are used to describe the hydrological behaviour of a river catchment. Many different models exist to simulate the physical processes of the relationship between precipitation and runoff. Some of them are based on simple and easy-to-handle concepts, others on highly sophisticated physical and mathematical approaches that require extreme effort in data input and handling. Recently, mathematical methods using linguistic variables, rather than conventional numerical variables applied extensively in other disciplines, are encroaching in hydrological studies. Among these is the application of a fuzzy rule-based modelling. In this paper an attempt was made to develop fuzzy rule-based routines to simulate the different processes involved in the generation of runoff from precipitation. These routines were implemented within a conceptual, modular, and semi-distributed model-the HBV model. The investigation involved determining which modules of this model could be replaced by the new approach and the necessary input data were identified. A fuzzy rule-based routine was then developed for each of the modules selected, and application and validation of the model was done on a rainfall-runoff analysis of the Neckar River catchment, in southwest Germany.     

Conceptual fuzzy neural network model for water quality simulation

Artificial neural networks (ANNs) have been applied successfully in various fields. However, ANN models depend on large sets of historical data, and are of limited use when only vague and uncertain information is available, which leads to difficulties in defining the model architecture and a low reliability of results. A conceptual fuzzy neural network (CFNN) is proposed and applied in a water quality model to simulate the Barra Bonita reservoir system, located in the southeast region of Brazil. The CFNN model consists of a rationally‐defined architecture based on accumulated expert knowledge about variables and processes included in the model. A genetic algorithm is used as the training method for finding the parameters of fuzzy inference and the connection weights. The proposed model may handle the uncertainties related to the system itself, model parameterization, complexity of concepts involved and scarcity and inaccuracy of data. The CFNN showed greater robustness and reliability when dealing with systems for which data are considered to be vague, uncertain or incomplete. The CFNN model structure is easier to understand and to define than other ANN‐based models. Moreover, it can help to understand the basic behaviour of the system as a whole, being a successful example of cooperation between human and machine. Copyright © 2006 John Wiley & Sons, Ltd.     

Comparison of grey and phase-space rainfall forecasting models using a fuzzy decision method / Comparaison grâce à une méthode de décision floue des modèles gris et d’espace des phases pour la prévision de pluie

Abstract

Abstract This work applies a fuzzy decision method to compare the performance of the grey model with that of the phase-space model, in forecasting rainfall one to three hours ahead. Four indices and two statistical tests are used to evaluate objectively the performance of the forecasting models. However, a trade-off must be made in choosing a suitable model because various indices may lead to different judgements. Therefore, a fuzzy decision model was applied to solve this problem and to make the optimum decision. The results of fuzzy decision making demonstrate that the grey model outperforms the phase-space model for forecasting one hour ahead, but the phase-space model performs better for forecasting two or three hours ahead.     

高架桥梁地震响应模糊半主动控制

提出了使用MR阻尼器（Magnetorheological Damper）作为控制设备，以模糊集为基础的半主动控制算法，研究了8种模糊控制规则在高架桥梁地震响应中的控制效果。本文提出的模糊方法的优势在于算法自身的鲁棒性、处理非线性问题的能力和不需要结构的精确数学模型，算法需要的输入变量少，模糊算法的输出直接控制MR阻尼器的输入电压，与LQR-clipped算法不同，MR阻尼器的输入电压可以是零与最大值之间的任意值。根据高架桥梁的结构特点，将典型的墩-支座-桥面结构简化为一个两自由度的线性系统，计算了El Centro地震激励下，MR模糊半主动控制的地震响应，并分别与没有控制及其他控制时的地震响应进行了对比，分析了各种控制算法的控制效果。研究结果表明，MR模糊半主动控制算法可以达到LQR-clipped半主动的控制效果，且模糊控制所需要的控制力较小，为有效地发挥MR阻尼器的功能提供了一种简单的半主动算法。     

基于组合权重的模糊物元模型在边坡稳定性评价中的应用

鉴于边坡稳定性评价的复杂性及其影响因素的不确定性和模糊性,运用模糊物元分析理论,分别使用层次分析法和熵权法计算各影响因子的主、客观权重值,建立兼顾主、客观因素的组合权重模糊物元评价模型。该模型有效避免了权重分配困难的问题,既能得到综合评价信息,也能反映评价对象的稳定状态。主、客观权重法既利用了样本资料的统计信息,又反映了专家的理论知识和经验,可得到较为客观、合理的指标权重。工程实例分析表明,该方法能准确反映边坡稳定性状况,为边坡稳定性的综合评价提供一条新的思路。     

一种基于紫外极光图像的亚暴膨胀期起始时刻的自动检测方法

准确界定亚暴起始时刻是理解亚暴相关问题的关键.已有研究主要集中在两方面:一是从极光图像中人工挑选亚暴事件进行案例分析或统计分析来研究亚暴发生机制及亚暴期间的地磁环境;二是基于一些空间物理参数,如AE指数、SME(SuperMAG electrojet)指数、Pi2、正弯扰等,采用人眼判断或是模式识别的方法从中找出亚暴起始时刻.本文尝试采用模式识别的方法从紫外极光图像中自动地检测出亚暴膨胀期起始时刻.首先,将紫外极光图像通过网格化处理转换到磁地方时-地磁纬度(MLT-MLAT)直角坐标下,然后通过模糊c均值聚类方法提取亮斑,再考察亮斑强度是否增强、面积是否极向膨胀来判断是不是亚暴事件.本文方法在1996年12月-1997年2月这三个月的Polar卫星紫外极光图像上进行了实验验证.我们将检测到的亚暴起始时刻与Liou(J. Geophys. Res., 2010, 115: A12219)的人工标记进行了对比,并详细分析了与标记不一致的多检和漏检事件.本文提出的自动检测方法可以快速地从海量紫外极光图像中完成亚暴事件的初步筛选,方便研究人员进一步深入研究极光亚暴.     

地震预测的灰色模糊模型

在灰色系统理论GM(1,1)模型的基础上,把模糊数学的表达方式用于灰色灾变预测中,建立了地震预测的灰色模糊模型。用此模型对我国川滇、甘宁青、新疆及华北等地区大地震时间序列进行预测检验,结果表明:灰色模糊预测模型比纯灰色系统理论模型要好,因而可在实际地震预测中试用。最后对所得结果及实际应用中需要注意的问题也进行了讨论。