Seismic liquefaction potential assessed by fuzzy comprehensive evaluation method

Liquefaction of loose, saturated granular soils during earthquakes poses a major hazard in many regions of the world. The determination of liquefaction potential of soils induced by earthquake is a major concern and an essential criterion in the design process of the civil engineering structures. A large number of factors that affect the occurrence of liquefaction during earthquake exist a form of uncertainty of non-statistical nature. Fuzzy systems are used to handle uncertainty from the data that cannot be handled by classical methods. It uses the fuzzy set to represent a suitable mathematical tool for modeling of imprecision and vagueness. The pattern classification of fuzzy classifiers provides a means to extract fuzzy rules for information mining that leads to comprehensible method for knowledge extraction from various information sources. Therefore, it is necessary to handle the soil liquefaction problem in a rational framework of fuzzy set theory. This study investigates the feasibility of using fuzzy comprehensive evaluation model for predicting soil liquefaction during earthquake. In the fuzzy comprehensive evaluation model of soil liquefaction, the following factors, such as earthquake intensity, standard penetration number, mean diameter and groundwater table, are selected as the evaluating indices. The results show that the method is a useful tool to assess the potential of soil liquefaction.     

模糊神经网络在矿震预测中的应用

矿震同天然地震一样会给矿山生产及人身安全等带来重大灾难。也是目前尚不能准确预测的矿山灾害现象之一。根据现有的研究成果可知，矿震是一个多输入、多干扰、单输出的复杂系统。由于干扰项的存在，使利用建模、神经网络等手段对系统进行预测时会导致很大误差。模糊神经网络系统在建立对象输入、输出关系时与传统数学方法不同。即可以建立在无模型基础上，并利用其较强的学习训练特性，自动获取对象的输入、输出关系表达；可以将专家的评价语言作为系统的干扰项引入。这在一定程度上缓解了人为因素对预测结果的影响，且平滑了观测数据的随机性。文章利用改进的模糊神经网络及抚顺老虎台矿的矿震资料，对矿震最大震级的预测方法进行了探索。‘初步探讨了改进的模糊神经网络在矿震预测中的应用。得出在运用模糊神经网络进行预测时，为减小预测误差，应综合多种因素并提高专家评判语言的精确度的结论。指出在建立矿震系统预测模型时，利用干扰项将人为因素引入系统是必需的。通过实际应用证明其可行性。     

贝叶斯网络在水资源管理中的应用

为了解决水资源管理中具有不确定性的多目标决策问题,将贝叶斯网络方法引入水资源管理中。通过对实例系统中变量间相互关系的分析,构建描述变量间不确定性关系的贝叶斯网络模型,其中包括表示其依赖关系的有向无环图和表示其具体概率依赖程度的条件概率表,并在6个目标变量均达到预期目标的前提下进行概率推理。实例结果表明：当补偿款数额增加到500元/亩时,所有的目标变量均可达到最优,因此确定出政府应给农民补偿款的数额为500元/亩的合理水资源决策方案。贝叶斯网络以图模型的方式直观地表达了实例系统中变量之间的不确定性关系,概率推理的结果兼顾了环境效益以及农民的利益,使多个预期目标均达到了最优,有效地解决了水资源管理中具有不确定性的多目标决策问题。     

Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives

Multi-criteria decision-making methods support decision makers in all stages of the decision-making process by providing useful data. However, criteria are not always certain as uncertainty is a feature of the real world. MCDM methods under uncertainty and fuzzy systems are accepted as suitable techniques in conflicting problems that cannot be represented by numerical values, in particular in energy analysis and planning. In this paper, a modified TOPSIS method for multi-criteria group decision-making with qualitative linguistic labels is proposed. This method addresses uncertainty considering different levels of precision. Each decision maker’s judgment on the performance of alternatives with respect to each criterion is expressed by qualitative linguistic labels. The new method takes into account linguistic data provided by the decision makers without any previous aggregation. Decision maker judgments are incorporated into the proposed method to generate a complete ranking of alternatives. An application in energy planning is presented as an illustrative case example in which energy policy alternatives are ranked. Seven energy alternatives under nine criteria were evaluated according to the opinion of three environmental and energy experts. The weights of the criteria are determined by fuzzy AHP, and the alternatives are ranked using qualitative TOPSIS. The proposed approach is compared with a modified fuzzy TOPSIS method, showing the advantages of the proposed approach when dealing with linguistic assessments to model uncertainty and imprecision. Although the new approach requires less cognitive effort to decision makers, it yields similar results.     

Time independent seismic hazard analysis in Alborz and surrounding area

The Bayesian probability estimation seems to have efficiencies that make it suitable for calculating different parameters of seismicity. Generally this method is able to combine prior information on seismicity while at the same time including statistical uncertainty associated with the estimation of the parameters used to quantify seismicity, in addition to the probabilistic uncertainties associated with the inherent randomness of earthquake occurrence. In this article a time-independent Bayesian approach, which yields the probability that a certain cut-off magnitude will be exceeded at certain time intervals is examined for the region of Alborz, Iran, in order to consider the following consequences for the city of Tehran. This area is located within the Alpine-Himalayan active mountain belt. Many active faults affect the Alborz, most of which are parallel to the range and accommodate the present day oblique convergence across it. Tehran, the capital of Iran, with millions of inhabitants is located near the foothills of the southern Central Alborz. This region has been affected several times by historical and recent earthquakes that confirm the importance of seismic hazard assessment through it. As the first step in this study an updated earthquake catalog is compiled for the Alborz. Then, by assuming a Poisson distribution for the number of earthquakes which occur at a certain time interval, the probabilistic earthquake occurrence is computed by the Bayesian approach. The highest probabilities are found for zone AA and the lowest probabilities for zones KD and CA, meanwhile the overall probability is high.     

Bayesian techniques for seismic hazard assessment using imprecise data

Some Bayesian methods of dealing with inaccurate or vague data are introduced in the framework of seismic hazard assessment. Inaccurate data affected by heterogeneous errors are modeled by a probability distribution instead of the usual value plus a random error representation; these data are generically called imprecise. The earthquake size and the number of events in a certain time are modeled as imprecise data. Imprecise data allow us to introduce into the estimation procedures the uncertainty inherent in the inaccuracy and heterogeneity of the measuring systems from which the data were obtained. The problem of estimating the parameter of a Poisson process is shown to be feasible by the use of Bayesian techniques and imprecise data. This background technique can be applied to a general problem of seismic hazard estimation. Initially, data in a regional earthquake catalog are assumed imprecise both in size and location (i.e errors in the epicenter or spreading over a given source). By means of scattered attenuation laws, the regional catalog can be translated into a so-called site catalog of imprecise events. The site catalog is then used to estimate return periods or occurrence probabilities, taking into account all sources of uncertainty. Special attention is paid to priors in the Bayesian estimation. They can be used to introduce additional information as well as scattered frequency-size laws for local events. A simple example is presented to illustrate the capabilities of this methodology.     

Using Bayesian networks in analyzing powerful earthquake disaster chains

Substantial economic losses, building damage, and loss of life have been caused by secondary disasters that result from strong earthquakes. Earthquake disaster chains occur when secondary disasters take place in sequence. In this paper, we summarize 23 common earthquake disaster chains, whose structures include the serial, parallel, and parallel–serial (dendroid disaster chain) types. Evaluating the probability of powerful earthquake disaster chains is urgently needed for effective disaster prediction and emergency management. To this end, we introduce Bayesian networks (BNs) to assess powerful earthquake disaster chains. The structural graph of a powerful earthquake disaster chain is presented, and the proposed BN modeling method is provided and discussed. BN model of the earthquake–landslides–barrier lakes–floods disaster chain is established. The use of BN shows that such a model enables the effective analysis of earthquake disaster chains. Probability inference reveals that population density, loose debris volume, flooded areas, and landslide dam stability are the most critical links that lead to loss of life in earthquake disaster chains.     

Fuzzy probabilistic seismic hazard analysis with applications to Kunming city,China

China is prone to highly frequent earthquakes due to specific geographical location, which could cause significant losses to society and economy. The task of seismic hazard analysis is to estimate the potential level of ground motion parameters that would be produced by future earthquakes. In this paper, a novel method based on fuzzy logic techniques and probabilistic approach is proposed for seismic hazard analysis (FPSHA). In FPSHA, we employ fuzzy sets for quantification of earthquake magnitude and source-to-site distance, and fuzzy inference rules for ground motion attenuation relationships. The membership functions for earthquake magnitude and source-to-site distance are provided based on expert judgments, and the construction of fuzzy rules for peak ground acceleration relationships is also based on expert judgment. This methodology enables to include aleatory and epistemic uncertainty in the process of seismic hazard analysis. The advantage of the proposed method is in its efficiency, reliability, practicability, and precision. A case study is investigated for seismic hazard analysis of Kunming city in Yunnan Province, People’s Republic of China. The results of the proposed fuzzy logic-based model are compared to other models, which confirms the accuracy in predicting the probability of exceeding a certain level of the peak ground acceleration. Further, the results can provide a sound basis for decision making of disaster reduction and prevention in Yunnan province.     

基于熵权的模糊综合评价法在公路洪灾风险评价中的应用

公路洪灾是一个多因素耦合的复杂系统,本文针对公路洪灾风险的复杂性、模糊性及不确定性,构造了基于熵权的模糊综合评价模型,通过熵权使权重由主观判断转为由各指标数据之间的关系来确定,从危险性和易损性两方面选取24个评价因子,定量地对重庆境内渝黔高速公路进行洪灾风险评价。评价结果表明,熵权模糊综合评价法是科学可行的,为公路洪灾风险趋势预测以及风险等级划分提供了良好的分析方法,在实践中具有一定的应用价值。     

Probabilistic parameter estimation and predictive uncertainty based on field measurements for unsaturated soil slope

A key issue in assessment of rainfall-induced slope failure is a reliable evaluation of pore water pressure distribution and its variations during rainstorm, which in turn requires accurate estimation of soil hydraulic parameters. In this study, the uncertainties of soil hydraulic parameters and their effects on slope stability prediction are evaluated, within the Bayesian framework, using the field measured temporal pore-water pressure data. The probabilistic back analysis and parameter uncertainty estimation is conducted using the Markov Chain Monte Carlo simulation. A case study of a natural terrain site is presented to illustrate the proposed method. The 95% total uncertainty bounds for the calibration period are relatively narrow, indicating an overall good performance of the infiltration model for the calibration period. The posterior uncertainty bounds of slope safety factors are much narrower than the prior ones, implying that the reduction of uncertainty in soil hydraulic parameters significantly reduces the uncertainty of slope stability.     

The use of the possibility theory to investigate the epistemic uncertainties within scenario-based earthquake risk assessments

This paper presents a methodology to represent and propagate epistemic uncertainties within a scenario-based earthquake risk model. Unlike randomness, epistemic uncertainty stems from incomplete, vague or imprecise information. This source of uncertainties still requires the development of adequate tools in seismic risk analysis. We propose to use the possibility theory to represent three types of epistemic uncertainties, namely imprecision, model uncertainty and vagueness due to qualitative information. For illustration, an earthquake risk assessment for the city of Lourdes (Southern France) using this approach is presented. Once adequately represented, uncertainties are propagated and they result in a family of probabilistic damage curves. The latter is synthesized, using the concept of fuzzy random variables, by means of indicators bounding the true probability to exceed a given damage grade. The gap between the pair of probabilistic indicators reflects the imprecise character of uncertainty related to the model, thus picturing the extent of what is ignored and can be used in risk management.     

Bayesian analysis of data and model uncertainty in 3D seismic travel-time tomography

We present a probabilistic analysis of seismic travel-time equations using the Bayesian Method. The assessment of models and data is crucial in 3D seismic travel-time tomography, and a method quantitatively assess the quality of both the data and the model is necessary in order to attain the most realistic results. The Bayesian method that we propose here is more effective than the frequentist approach, both in analysis time and uncertainty minimization, when processing large sets of tomographic data.     

水文水资源系统贝叶斯分析现状与前景

简介了贝叶斯分析的基本原理,综述了它在水文水资源系统中考虑不确定性和风险的特点及其在径流预报、洪水分析与地区综合、水资源规划与管理等问题中的应用,并分析了其发展前景,着重指出将灰色先验分布、模糊似然函数在贝叶斯定理框架中耦合的综合途径.     

A Hazard Assessment Method for Potential Earthquake-Induced Landslides – A Case Study in Huaxian County, Shaanxi Province

The hazard assessment of potential earthquake-induced landslides is an important aspect of the study of earthquake-induced landslides. In this study, we assessed the hazard of potential earthquake-induced landslides in Huaxian County with a new hazard assessment method. This method is based on probabilistic seismic hazard analysis and the Newmark cumulative displacement assessment model. The model considers a comprehensive suite of information, including the seismic activities and engineering geological conditions in the study area, and simulates the uncertainty of the intensity parameters of the engineering geological rock groups using the Monte Carlo method. Unlike previous assessment studies on ground motions with a given exceedance probability level, the hazard of earthquake-induced landslides obtained by the method presented in this study allows for the possibility of earthquake-induced landslides in different parts of the study area in the future. The assessment of the hazard of earthquake-induced landslides in this study showed good agreement with the historical distribution of earthquake-induced landslides. This indicates that the assessment properly reflects the macroscopic rules for the development of earthquake-induced landslides in the study area, and can provide a reference framework for the management of the risk of earthquake-induced landslides and land planning.     

Bayesian estimation of reservoir properties—effects of uncertainty quantification of 4D seismic data

This paper shows a history matching workflow with both production and 4D seismic data where the uncertainty of seismic data for history matching comes from Bayesian seismic waveform inversion. We use a synthetic model and perform two seismic surveys, one before start of production and the second after 1 year of production. From the first seismic survey, we estimate the contrast in slowness squared (with uncertainty) and use this estimate to generate an initial estimate of porosity and permeability fields. This ensemble is then updated using the second seismic survey (after inversion to contrasts) and production data with an iterative ensemble smoother. The impact on history matching results from using different uncertainty estimates for the seismic data is investigated. From the Bayesian seismic inversion, we get a covariance matrix for the uncertainty and we compare using the full covariance matrix with using only the diagonal. We also compare with using a simplified uncertainty estimate that does not come from the seismic inversion. The results indicate that it is important not to underestimate the noise in seismic data and that having information about the correlation in the error in seismic data can in some cases improve the results.     

基于不确定性的地下水污染风险评价研究进展

地下水污染风险研究在工程决策中具有重要意义。但地下水系统本身具有各种不确定性,基于这些不确定性的地下水污染风险评价也因此具有不确定性,而且贯穿整个地下水污染风险评价过程。所以,进行不确定性分析是对地下水污染风险进行评价时的必要步骤。一般来讲,基于不确定性分析的地下水污染风险评价结果更可靠,因此也更具有实用价值。本文在对不确定性进行分类（随机不确定性和模糊不确定性）的基础上,通过大量文献调研和分析,对目前国内外用于地下水污染风险评价分析的不确定性分析方法（包括随机理论方法、模糊理论方法、随机-模糊耦合方法等）进行了归纳总结,并基于各种方法目前的研究现状,分析了不确定性理论在地下水污染风险评价研究中的发展前景。     

顶点法在边坡稳定性评价中的应用

考虑到边坡稳定性分析中存在的测量数据的不确定性和确定计算参数的复杂性以及描述边坡稳定状态的模糊性, 本文介绍了-种新的适用于边坡稳定性分析的模糊数学方法顶点法, 该法由不确定的土力学参数求得边坡稳定性系数的模糊集合, 然后运用对比法进行分类来确定边坡所处的稳定状态, 文章最后通过-个工程实例阐述了应用过程。     

Effect of loading duration on uncertainty in creep analysis of clay

Some studies suggest that creep parameters should be determined using a greater quantity of creep test data to provide more reliable prediction regarding the deformation of soft soils. This study aims to investigate the effect of loading duration on model updating. One‐dimensional consolidation data of intact Vanttila clay under different loading durations collected from the literature is used for demonstration. The Bayesian probabilistic method is used to identify all unknown parameters based on the consolidation data during the entire consolidation process, and their uncertainty can be quantified through the obtained posterior probability density functions. Additionally, the optimal models are also determined from among 9 model candidates. The analyses indicate that the optimal models can describe the creep behavior of intact soft soils under different loading durations, and the adopted method can evaluate the effect of loading duration on uncertainty in the creep analysis. The uncertainty of a specific model and its model parameters decreases as more creep data are involved in the updating process, and the updated models that use more creep data can better capture the deformation behavior of an intact sample. The proposed method can provide quantified uncertainty in the process of model updating and assist engineers to decide whether the creep test data are sufficient for the creep analysis.     

Updating the models and uncertainty of mechanical parameters for rock tunnels using Bayesian inference

Rock mechanical parameters and their uncertainties are critical to rock stability analysis, engineering design, and safe construction in rock mechanics and engineering. The back analysis is widely adopted in rock engineering to determine the mechanical parameters of the surrounding rock mass, but this does not consider the uncertainty. This problem is addressed here by the proposed approach by developing a system of Bayesian inferences for updating mechanical parameters and their statistical properties using monitored field data, then integrating the monitored data, prior knowledge of geotechnical parameters,and a mechanical model of a rock tunnel using Markov chain Monte Carlo(MCMC) simulation. The proposed approach is illustrated by a circular tunnel with an analytical solution, which was then applied to an experimental tunnel in Goupitan Hydropower Station, China. The mechanical properties and strength parameters of the surrounding rock mass were modeled as random variables. The displacement was predicted with the aid of the parameters updated by Bayesian inferences and agreed closely with monitored displacements. It indicates that Bayesian inferences combined the monitored data into the tunnel model to update its parameters dynamically. Further study indicated that the performance of Bayesian inferences is improved greatly by regularly supplementing field monitoring data. Bayesian inference is a significant and new approach for determining the mechanical parameters of the surrounding rock mass in a tunnel model and contributes to safe construction in rock engineering.     

双目标耦合不确定性模糊模式识别模型

本文在陈守煜建立的模糊模式识别理论的构架基础上,将加权广义距离与模糊熵最小作为模式识别的复合目标函数,建立了考虑随机和模糊不确定性,使目标函数最小的新型模糊模式识别耦合模型。应用此模型对我国12个湖库的富营养化程度进行综合评价,并与原模型的双目标结果进行比较,说明本文建立的模型考虑了不确定性因素的优越性。