Use of raster-based data layers to model spatial variation of seismotectonic data in probabilistic seismic hazard assessment

Recent achievements in computer and information technology have provided the necessary tools to extend the application of probabilistic seismic hazard mapping from its traditional engineering use to many other applications. Examples for such applications are risk mitigation, disaster management, post disaster recovery planning and catastrophe loss estimation and risk management. Due to the lack of proper knowledge with regard to factors controlling seismic hazards, there are always uncertainties associated with all steps involved in developing and using seismic hazard models. While some of these uncertainties can be controlled by more accurate and reliable input data, the majority of the data and assumptions used in seismic hazard studies remain with high uncertainties that contribute to the uncertainty of the final results. In this paper a new methodology for the assessment of seismic hazard is described. The proposed approach provides practical facility for better capture of spatial variations of seismological and tectonic characteristics, which allows better treatment of their uncertainties. In the proposed approach, GIS raster-based data models are used in order to model geographical features in a cell-based system. The cell-based source model proposed in this paper provides a framework for implementing many geographically referenced seismotectonic factors into seismic hazard modelling. Examples for such components are seismic source boundaries, rupture geometry, seismic activity rate, focal depth and the choice of attenuation functions. The proposed methodology provides improvements in several aspects of the standard analytical tools currently being used for assessment and mapping of regional seismic hazard. The proposed methodology makes the best use of the recent advancements in computer technology in both software and hardware. The proposed approach is well structured to be implemented using conventional GIS tools.     

Risk-informed decision-making in the presence of epistemic uncertainty

An important issue in risk analysis is the distinction between epistemic and aleatory uncertainties. In this paper, the use of distinct representation formats for aleatory and epistemic uncertainties is advocated, the latter being modelled by sets of possible values. Modern uncertainty theories based on convex sets of probabilities are known to be instrumental for hybrid representations where aleatory and epistemic components of uncertainty remain distinct. Simple uncertainty representation techniques based on fuzzy intervals and p-boxes are used in practice. This paper outlines a risk analysis methodology from elicitation of knowledge about parameters to decision. It proposes an elicitation methodology where the chosen representation format depends on the nature and the amount of available information. Uncertainty propagation methods then blend Monte Carlo simulation and interval analysis techniques. Nevertheless, results provided by these techniques, often in terms of probability intervals, may be too complex to interpret for a decision-maker and we, therefore, propose to compute a unique indicator of the likelihood of risk, called confidence index. It explicitly accounts for the decision-maker's attitude in the face of ambiguity. This step takes place at the end of the risk analysis process, when no further collection of evidence is possible that might reduce the ambiguity due to epistemic uncertainty. This last feature stands in contrast with the Bayesian methodology, where epistemic uncertainties on input parameters are modelled by single subjective probabilities at the beginning of the risk analysis process.     

面向可信软件的风险评估方法

风险管理逐渐成为开发高质量软件过程中的重要的组成部分。风险评估作为风险管理的重要活动之一,是风险控制的前提。贝叶斯网络作为风险管理的有力工具之一,是处理不确定性的有效方法。结合贝叶斯网络与模糊理论,提出一种风险评估方法,首先使用贝叶斯网络对影响可信软件的风险因素进行风险概率评估,然后利用模糊综合评价法进行风险综合影响评估。该方法用于软件项目的风险评估,为开发高质量的可信软件提供新策略。     

Sensitivity analysis of structural response uncertainty propagation using evidence theory

Sensitivity analysis for the quantified uncertainty in evidence theory is developed. In reliability quantification, classical probabilistic analysis has been a popular approach in many engineering disciplines. However, when we cannot obtain sufficient data to construct probability distributions in a large-complex system, the classical probability methodology may not be appropriate to quantify the uncertainty. Evidence theory, also called Dempster–Shafer Theory, has the potential to quantify aleatory (random) and epistemic (subjective) uncertainties because it can directly handle insufficient data and incomplete knowledge situations. In this paper, interval information is assumed for the best representation of imprecise information, and the sensitivity analysis of plausibility in evidence theory is analytically derived with respect to expert opinions and structural parameters. The results from the sensitivity analysis are expected to be very useful in finding the major contributors for quantified uncertainty and also in redesigning the structural system for risk minimization.     

基于模糊集和DS证据理论的信息安全风险评估方法*

在信息安全风险评估过程中,存在着很多不确定和模糊的因素,针对专家评价意见的不确定性和主观性问题,提出了一种将模糊集理论与DS证据理论进行结合的的风险评估方法。首先,根据信息安全风险评估的流程和要素,建立风险评估指标体系,确定风险影响因素;其次,通过高斯隶属度函数,求出专家对各影响因素的评价意见隶属于各个不同评价等级的程度;再次,将其作为DS理论所需的基本概率分配,引入基于矩阵分析和权值分配的融合算法综合多位专家的评价意见;最后,结合贝叶斯网络模型的推理算法,得出被测信息系统所面临的风险大小,并对其进行分析。结果显示,将模糊集理论和DS证据理论应用到传统贝叶斯网络风险评估的方法,在一定程度上能够提高评估结果的客观性。     

地震震害评估系统的设计与实现

结合地理信息技术与震害评估模型,研究了如何对损失结果进行快速的评估计算。简要介绍了基于GIS的地震震害评估系统的设计思路,给出基于四层结构的系统体系架构以及系统功能设计等内容。以人员伤亡评估模型为例,讲解如何综合运用经济损失、人员伤亡、强震等震线等专家模型,对损失结果进行快速评估和动态修正,为抗震减灾决策人员提供支持。     

Construction of families of probability boxes and corresponding membership functions at different fractiles

Uncertainty comes in many forms in the real world and is an unavoidable component of human life. Generally, two types of uncertainties arise, namely, aleatory and epistemic uncertainty. Probability is a well established mathematical tool to handle aleatory uncertainty and fuzzy set theory is a tool to handle epistemic uncertainty. However, in certain situations, parameters of probability distributions may be tainted with epistemic uncertainty; and so, representation of parameters of probability distributions may be treated as fuzzy numbers (may be of different shapes). A probability box (P‐box) can be constructed when parameters are not precisely known. In this paper, an attempt has been made to construct families of P‐boxes when parameters of probability distributions are bell shaped or normal fuzzy numbers; and from these families of P‐boxes, membership functions are generated at different fractiles for different alpha levels.     

基于网格环境下的安全隐患评估模型研究

企业的安全生产是十分重要的,而安全隐患几乎存在于任何职业和企业里,为了减少人员伤亡、伤害以及财产的损失,这就需要加大安全隐患排除力度,切实有效地为事故发生做好预防。根据以上问题,本文提出了一种安全隐患评估模型,该方法是将网格环境中的相关内容引入安全隐患评估模型中,通过初期的人工自学习阶段后,使其能智能选取评估方法对生产数据进行安全隐患的评估,进而提高安全隐患评估的工作效率,所以建立一个能有效控制危险有害因素的安全隐患评估模型是十分必要的。     

Study of Assessment Method Based on Coupling Factor of Casualty in Earthquake Disasters in Guangdong Area

In this paper, the rapid assessment method of casualties in earthquake disasters is studied and a casualty assessment model based on coupling factors is established from the perspective of factors affecting casualties in earthquake disasters. Taking Guangdong area as an example, the spatialization and quantification of casualty coupling factors in earthquake disasters are analyzed in detail. A software system is developed based on ARC Engine 10.1 platform. For the same earthquake case, different algorithms (algorithm model based on coupling factor and algorithm model based on building seismic vulnerability) are used to calculate the number of casualties in order to compare research results.     

Stochastic dynamics simulation with generalized interval probability

Two types of uncertainties are generally recognized in modelling and simulation, including variability caused by inherent randomness and incertitude due to the lack of perfect knowledge. In this paper, a generalized interval-probability theory is used to model both uncertainty components simultaneously, where epistemic uncertainty is quantified by generalized interval in addition to probability measure. Conditioning, independence, and Markovian probabilities are uniquely defined in generalized interval probability such that its probabilistic calculus resembles that in the classical probability theory. A path-integral approach can be taken to solve the interval Fokker–Planck equation for diffusion processes. A Krylov subspace projection method is proposed to solve the interval master equation for jump processes. Thus, the time evolution of both uncertainty components can be simulated simultaneously, which provides the lower and upper bound information of evolving probability distributions as an alternative to the traditional sensitivity analysis.     

Uncertain probabilities I: the discrete case

We consider discrete (finite) probability distributions where some of the probability values are uncertain. We model these uncertainties using fuzzy numbers. Then, employing restricted fuzzy arithmetic, we derive the basic laws of fuzzy (uncertain) probability theory. Applications are to the binomial probability distribution and queuing theory.     

基于模糊理论的软件项目风险评估模型研究

在软件项目的开发和管理过程中存在很多的不确定性,针对软件项目的复杂性和不确定性,该文在模糊理论的基础上,提出了一种基于模糊理论的软件项目风险评估模型。该模型可以评估常用于衡量风险程度的风险当量以外,还可以评估多种风险对某种风险后果的组合影响,以及单个风险对整体后果的综合影响。通过实践证明该模型可以减少专家评估的不确定性,有效地预测潜在的风险,为降低风险发生的概率和提高软件项目的成功提供了行之有效的方法。     

An intelligent simulation system for earthquake disaster assessment

This paper presents an intelligent simulation system for an earthquake disaster assessment system based on a development platform of a Geographic Information System (GIS) and Artificial Intelligence (AI). This system is designed to identify the weakness of the structure and infrastructure system in pre-earthquake conditions, quickly assess earthquake damage and make an intelligent emergency response for the public and government during the earthquake and post-earthquake. The system includes the following functions: intelligent seismic hazard assessment, earthquake damage and loss evaluation, optimizing emergency response and post-earthquake recovering plan. The principle, design criteria, structure, functions and test results of this system are described in this paper. Based on its functional characteristics, this system is composed of four parts: an information database, analytical modules, an intelligent decision-making sub-system and a friendly user interface. There are 132 coverages and 78 analytical modules included in the information database and analytical modules. With this system, seismic disaster mitigation strategies can be verified during a pre-earthquake, and be executed at the time of an earthquake and post-earthquake; the earthquake resisting capacities for an entire city and all of its communities can be greatly enhanced. To check its reliability and its efficiency, this system has been tested based on a scenario earthquake event in one city, and the related results have also been given in this paper. At the present, this system has been installed and used in Daqing City, China. After running for almost 10 years, this system has successfully been used in rehearsing of seismic disaster mitigation and post-earthquake emergency response. Simultaneously, an optimizing aseismic retrofitting plan in Daqing City has been executed based on results from this system.     

Numerical accuracy and efficiency in the propagation of epistemic and aleatory uncertainties

The need to differentiate between epistemic and aleatory uncertainties is now well admitted by the risk analysis community. One way to do so is to model aleatory uncertainty by classical probability distributions and epistemic uncertainty by means of possibility distributions, and then propagate them by their respective calculus. The result of this propagation is a random fuzzy variable. When dealing with complex models, the computational cost of such a propagation quickly becomes too high. In this paper, we propose a numerical approach, the Random/Fuzzy (RaFu) method, whose aim is to determine an optimal numerical strategy so that computational costs are reduced to their minimum, using the theoretical frameworks mentioned above. We also give some means to take account of the resulting numerical error. The benefits of the RaFu method are shown by comparing it to previously proposed methods.     

地震灾害潜在损失评价模型分析与研究——以唐山地震为例

地震灾害潜在损失评价模型的研究是综合应用遥感和地理信息系统技术,估算潜在地震灾区可能的最大损失量,以及潜在震区直接和间接的最大经济损失,为震区灾后的重建提供辅助决策支持信息。     

On the Stability of Takagi–Sugeno Fuzzy Systems With Time-Varying Uncertainties

This paper studies the problems of stability analysis of Takagi-Sugeno free fuzzy systems with time-varying uncertainties. In our prior study, we represented the time-varying uncertainty incurred in characteristic interval matrices in terms of the stability of Takagi-Sugeno free fuzzy systems with consequent parameter uncertainties. Based on Mayer's convergent theorem for powers of single interval matrix and its generalization, we further proposed some sufficient conditions for the Takagi-Sugeno free fuzzy system with time-varying uncertainties to be globally asymptotically stable. In this paper, we propose the notion of simultaneously nilpotent interval matrices to investigate the Takagi-Sugeno free fuzzy system with time-varying uncertainties to be strongly stable within steps, where relates to the dimension of interval matrices. Moreover, a unique situation for the deterministic Takagi-Sugeno free fuzzy system to be strongly stable within steps is derived as well, where relates to the dimension of characteristic matrices for the deterministic Takagi-Sugeno free fuzzy system.     

A novel evidence-based fuzzy reliability analysis method for structures

Epistemic uncertainties always exist in engineering structures due to the lack of knowledge or information, which can be mathematically described by either fuzzy-set theory or evidence theory (ET) In this work, the authors present a novel uncertainty model, namely evidence-based fuzzy model, in which the fuzzy sets and ET are combined to represent the epistemic uncertainty. A novel method for combining multiple membership functions and a corresponding reliability analysis method is also developed. In the combination method, the combined fuzzy-set representations are approximated by the enveloping lines of the multiple membership functions (smoothed by neglecting the valleys in the membership functions curves) and the Murphy’s average combination rule is applied to compute the basic probability assignment for focal elements. Then, the combined membership function is transformed to the equivalent probability density function by means of a normalizing factor. Finally, the Markov Chain Monte Carlo (MCMC) subset simulation method is used to solve reliability by introducing intermediate failure events. A numerical example and two engineering examples are provided to demonstrate the effectiveness of the proposed method.