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1.
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. 相似文献
2.
在有限数据条件下,可靠度敏感性分析是研究各种不确定性因素对边坡失稳概率影响规律的重要途径。基于直接蒙特卡洛模拟和概率密度加权分析方法提出了一种高效边坡稳定可靠度敏感性分析方法。所提出的方法通过随机场表征岩土体参数的空间变异性,并采用局部平均理论建立岩土体参数的缩维概率密度函数,用于概率密度加权分析中高效、准确地计算不同敏感性分析方案对应的边坡失稳概率。最后,通过一个工程案例--詹姆斯湾堤坝说明了所提出方法的有效性和准确性。结果表明：在敏感性分析过程中,所提出的方法只需要执行一次直接蒙特卡洛模拟,避免了针对不同敏感性分析方案重新产生随机样本和执行边坡稳定分析,节约了大量的计算时间和计算资源,显著提高了基于蒙特卡洛模拟的敏感性分析计算效率;在概率密度加权分析中采用岩土体参数的缩维概率密度函数能够准确地计算边坡失稳概率,避免了有偏估计,使概率密度加权分析方法适用于考虑空间变异性条件下的边坡稳定可靠度敏感性分析问题。 相似文献
3.
Monte Carlo Simulation (MCS) method has been widely used in probabilistic analysis of slope stability, and it provides a robust and simple way to assess failure probability. However, MCS method does not offer insight into the relative contributions of various uncertainties (e.g., inherent spatial variability of soil properties and subsurface stratigraphy) to the failure probability and suffers from a lack of resolution and efficiency at small probability levels. This paper develop a probabilistic failure analysis approach that makes use of the failure samples generated in the MCS and analyzes these failure samples to assess the effects of various uncertainties on slope failure probability. The approach contains two major components: hypothesis tests for prioritizing effects of various uncertainties and Bayesian analysis for further quantifying their effects. Equations are derived for the hypothesis tests and Bayesian analysis. The probabilistic failure analysis requires a large number of failure samples in MCS, and an advanced Monte Carlo Simulation called Subset Simulation is employed to improve efficiency of generating failure samples in MCS. As an illustration, the proposed probabilistic failure analysis approach is applied to study a design scenario of James Bay Dyke. The hypothesis tests show that the uncertainty of undrained shear strength of lacustrine clay has the most significant effect on the slope failure probability, while the uncertainty of the clay crust thickness contributes the least. The effect of the former is then further quantified by a Bayesian analysis. Both hypothesis test results and Bayesian analysis results are validated against independent sensitivity studies. It is shown that probabilistic failure analysis provides results that are equivalent to those from additional sensitivity studies, but it has the advantage of avoiding additional computational times and efforts for repeated runs of MCS in sensitivity studies. 相似文献
4.
边坡稳定性预测的模糊神经网络模型 总被引：9，自引：0，他引：9
根据边坡稳定问题具有的模糊性,提出了一种判定边坡稳定性的模糊神经网络模型。该系统仅从期望输入输出数据集即可达到获取知识、确定模糊初始规则基的目的。再利用神经网络学习能力便不难修改规则库中的模糊规则以及隶属函数和网络权值等参数,这样大大减少了规则匹配过程,加快了推理速度,从而极大程度地提高了系统的自适应能力。最后用收集到的边坡数据样本训练和测试模糊神经网络模型,结果表明该模糊神经网络预测边坡稳定性是可行的、有效的。 相似文献
5.
《地学前缘(英文版)》2021,12(5)
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. 相似文献
6.
边坡稳定性类比评价的定量实现 总被引：9，自引：1，他引：8
岩土边坡是一个受多因素影响、随时空变异的动态系统 ,它具有数据有限、不确定和没有原型等特点 ,因此 ,已有成功经验知识的运用在边坡稳定性评价中具有重要的意义。本文基于类比推理和灰色系统理论 ,提出了一种定量开发利用经验知识的新方法 ,并将其成功地应用于边坡稳定性分析和评价。结果表明 ,在运用经验知识方面 ,该法与目前广泛使用的神经网络方法有异曲同工之妙。此外 ,本文所提出的方法还具有简单、方便和实用等许多优点。 相似文献
7.
There is growing interest in the use of back‐propagation neural networks to model non‐linear multivariate problems in geotehnical engineering. To overcome the shortcomings of the conventional back‐propagation neural network, such as overfitting, where the neural network learns the spurious details and noise in the training examples, a hybrid back‐propagation algorithm has been developed. The method utilizes the genetic algorithms search technique and the Bayesian neural network methodology. The genetic algorithms enhance the stochastic search to locate the global minima for the neural network model. The Bayesian inference procedures essentially provide better generalization and a statistical approach to deal with data uncertainty in comparison with the conventional back‐propagation. The uncertainty of data can be indicated using error bars. Two examples are presented to demonstrate the convergence and generalization capabilities of this hybrid algorithm. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
8.
Probabilistic analysis has been used as an effective tool to evaluate uncertainty so prevalent in variables governing rock slope stability. In this study a probabilistic analysis procedure and related algorithms were developed by extending the Monte Carlo simulation. The approach was used to analyze rock slope stability for Interstate Highway 40 (I-40), North Carolina, USA. This probabilistic approach consists of two parts: analysis of available geotechnical data to obtain random properties of discontinuity parameters; and probabilistic analysis of slope stability based on parameters with random properties. Random geometric and strength parameters for discontinuities were derived from field measurements and analysis using the statistical inference method or obtained from experience and engineering judgment of parameters. Specifically, this study shows that a certain amount of experience and engineering judgment can be utilized to determine random properties of discontinuity parameters. Probabilistic stability analysis is accomplished using statistical parameters and probability density functions for each discontinuity parameter. Then, the two requisite conditions, kinematic and kinetic instability for evaluating rock slope stability, are determined and evaluated separately, and subsequently the two probabilities are combined to provide an overall stability measure. Following the probabilistic analysis to account for variation in parameters, results of the probabilistic analyses were compared to those of a deterministic analysis, illustrating deficiencies in the latter procedure. Two geometries for the cut slopes on I-40 were evaluated, the original 75° slope and the 50° slope which has developed over the past 40 years of weathering. 相似文献
9.
极限分析上限方法在边坡稳定性评价中受到了广泛关注,但当前所取得的解析成果尚不能直接应用于解决任意多土层分布、多台阶的广义复杂层状边坡。基于组合对数螺线的旋转破坏机制,推导了具有任意坡面几何特征、任意多土层（含非水平土/岩层）边坡的外功率统一积分表达式及相应的虚功率方程,提出了多阶多层复杂边坡稳定性的通用极限分析上限方法;为克服积分式的复杂解析计算,引入了数值积分技术。在此基础上,结合最优化方法和强度折减技术,优化求解了复杂边坡的全局稳定性安全系数及相应的临界滑动面。通过多个典型算例的验证与对比分析,表明该方法具有较高的精度和广泛适用性。最后,针对典型多阶多层边坡实例,开展了上限法的深度拓展与应用研究,其结果为广义复杂层状边坡的稳定性评价提供了新思路。 相似文献
10.
This paper examines the potential of relevance vector machine (RVM) in slope stability analysis. The nonlinear relationship between slope stability and its influence factors is presented by the relevance vector learning mechanism based on a kernel‐based Bayesian framework. The six input variables used for the RVM for the prediction of stability slope are density (γ), friction angle (C), friction coefficient (?), slope angle (?r), slope height (H), and pore water pressure (ru). Comparison of RVM with some other methods is also presented. RVM has been used to compute the error bar. The results presented in this paper clearly highlight that the RVM is a robust tool for the prediction of slope stability. The experimental results show the effectiveness of the proposed approach. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
11.
基于粗糙集理论的边坡稳定性评价中因素权重确定方法的研究 总被引：6，自引：1，他引：5
为了准确计算影响边坡稳定性的各个因素的权重,应用粗糙集理论对原始数据进行挖掘,在实测数据离散化的基础上,将权重确定问题转化为粗糙集中属性重要性评价问题,建立了关于边坡稳定性评价的关系数据模型,经过属性值特征化建立了知识系统,在数据分析下通过分析评判方法对评价对象的支持度和重要性,计算出边坡稳定性评价模型的权重。该方法克服了传统权重确定方法的主观性,使得边坡稳定性评价方法更具客观性,从而提高了边坡稳定性评价的精度,通过实例说明该方法更加合理有效。 相似文献
12.
13.
受工程勘察成本及试验场地限制，可获得的试验数据通常有限，基于有限的试验数据难以准确估计岩土参数统计特征和边坡可靠度。贝叶斯方法可以融合有限的场地信息降低对岩土参数不确定性的估计进而提高边坡可靠度水平。但是，目前的贝叶斯更新研究大多假定参数先验概率分布为正态、对数正态和均匀分布，似然函数为多维正态分布，这种做法的合理性有待进一步验证。总结了岩土工程贝叶斯分析常用的参数先验概率分布及似然函数模型，以一个不排水黏土边坡为例，采用自适应贝叶斯更新方法系统探讨了参数先验概率分布和似然函数对空间变异边坡参数后验概率分布推断及可靠度更新的影响。计算结果表明：参数先验概率分布对空间变异边坡参数后验概率分布推断及可靠度更新均有一定的影响，选用对数正态和极值I型分布作为先验概率分布推断的参数后验概率分布离散性较小。选用Beta分布和极值I型分布获得的边坡可靠度计算结果分别偏于保守和危险，选用对数正态分布获得的边坡可靠度计算结果居中。相比之下，似然函数的影响更加显著。与其他类型似然函数相比，由多维联合正态分布构建的似然函数可在降低对岩土参数不确定性估计的同时，获得与场地信息更为吻合的计算结果。另外，构建似然函数时不同位置处测量误差之间的自相关性对边坡后验失效概率也具有一定的影响。 相似文献
14.
《地学前缘(英文版)》2018,9(6):1639-1648
Cohesion(c) and friction angle(φ) of rock are important parameters required for reliability analysis of rock slope stability. There is correlation between c and φ which affects results of reliability analysis of rock slope stability. However, the characterization of joint probability distribution of c and φ through which their correlation can be estimated requires a large amount of rock property data, which are often not available for most rock engineering projects. As a result, the correlation between c and φ is often ignored or simply assumed during reliability studies, which may lead to bias estimation of failure probability. In probabilistic rock slope stability analysis, the influence of ignoring or simply assuming the correlation of the rock strength parameters(i.e., c and φ) on the reliability of rock slopes has not been fully investigated. In this study, a Bayesian approach is developed to characterize the correlation between c and φ, and an expanded reliability-based design(RBD) approach is developed to assess the influence of correlation between c and φ on reliability of a rock slope. The Bayesian approach characterizes the sitespecific joint probability distribution of c and φ, and quantifies the correlation between c and φ using available limited data pairs of c and φ from a rock project. The expanded RBD approach uses the joint probability distribution of c and φ obtained through the Bayesian approach as inputs, to determine the reliability of a rock slope. The approach gives insight into the propagation of the correlation between c and φ through their joint probability into the reliability analysis, and their influence on the calculated reliability of the rock slope. The approaches may be applied in practice with little additional effort from a conventional analysis. The proposed approaches are illustrated using real c and φ data pairs obtained from laboratory tests of fractured rock at Forsmark, Sweden. 相似文献
15.
沉积岩粒度分析专家系统 总被引：4，自引：0，他引：4
本文介绍了沉积岩粒度分析专家系统的构成和其知识库、信息库（包括数据库）、扩展表格条件、文本文件等，推理机及知识获取部分的特性。该系统建立在ＩＢＭ及其兼容微机上，模仿专家解答问题的方式，采用良好的人机交互界面，对沉积岩粒度分析结果进行综合分析。经该专家系统咨询后，用户能得出沉积岩的名称、沉积环境和沉积相。 相似文献
16.
This paper develops a risk de-aggregation and system reliability approach to evaluate the slope failure probability, pf, using representative slip surfaces together with MCS. An efficient procedure is developed to strategically select the candidate representative slip surfaces, and a risk de-aggregation approach is proposed to quantify contribution of each candidate representative slip surface to the pf, identify the representative slip surfaces, and determine how many representative slip surfaces are needed for estimating the pf with reasonable accuracy. Risk de-aggregation is performed by collecting the failure samples generated in MCS and analyzing them statistically. The proposed methodology is illustrated through a cohesive soil slope example and validated against results from previous studies. When compared with the previous studies, the proposed approach substantially improves the computational efficiency in probabilistic slope stability analysis. The proposed approach is used to explore the effect of spatial variability on the pf. It is found that, when spatial variability is ignored or perfect correlation assumed, the pf of the whole slope system can be solely attributed to a single representative slip surface. In this case, it is theoretically appropriate to use only one slip surface in the reliability analysis. As the spatial variability becomes growingly significant, the number of representative slip surfaces increases, and all representative slip surfaces (i.e., failure modes) contribute more equally to the overall system risk. The variation of failure modes has substantial effect on the pf, and all representative surfaces have to be incorporated properly in the reliability analysis. The risk de-aggregation and system reliability approach developed in this paper provides a practical and efficient means to incorporate such a variation of failure modes in probabilistic slope stability analysis. 相似文献
17.
计算边坡安全系数的坡向离心法 总被引：1，自引：0，他引：1
边坡稳定分析方法发展最引人注目,它是经典土力学最早试图解决而至今仍未圆满解决的课题。在常用边坡分析方法的基础上,从边坡失稳机制出发,提出一种更方便的安全系数计算方法--坡向离心法。该法通过不断增大水平加速度,直至边坡失稳为止,依据水平加速度与重力加速度失稳影响机制求得安全系数。通过算例与传统极限平衡法和有限元强度折减法相比较,并对各物理参数进行敏感性分析。结果表明,坡向离心法在边坡工程稳定分析中的应用是切实可行的,其弹性模量和泊松比对该法所求安全系数影响不大。 相似文献
18.
《Computers and Geotechnics》2006,33(4-5):260-274
Three-dimensional (3D) evaluation of slope stability is a widely addressed problem in the domain of geotechnical engineering. The growing popularity of the geographical information system (GIS) approach, with capacities ranging from conventional data storage to complex spatial analysis and graphical presentation, means that it is also becoming a powerful tool for geotechnical engineers. In this study, in which we combine GIS grid-based data with four proposed column-based models of 3D slope stability analysis, we have devised new correspondent GIS grid-based 3D deterministic models to calculate the safety factor of the slope. Based on the four GIS-based 3D slope stability analysis models, a GIS-based program, 3DSlopeGIS, has been developed to implement the algorithm where all the input data are in the same format as the GIS dataset. The 3DSlopeGIS system, which is an extension of the widely used GIS software package, represents the combined development of 3D slope stability analysis and GIS-based component object model (COM) skills. Since all related data are supplied in the GIS format, this new database approach will be convenient for the repeated renewal and consulting of data. Certain widely addressed examples are evaluated in this paper and the results show the correction and potential of this GIS-based tool as a means of assessing the 3D stability of a slope. Two practical slope problems have been evaluated using the 3DSlopeGIS system. The results illustrate the convenience of data management as well as the effective range selection of Monte-Carlo random variables and the critical slip surface location in some parts of a lava dome. 相似文献
19.
Maria Navarro Olivier P. Le Maître Ibrahim Hoteit David L. George Kyle T. Mandli Omar M. Knio 《Computational Geosciences》2018,22(6):1447-1463
This work tackles the problem of calibrating the unknown parameters of a debris flow model with the drawback that the information regarding the experimental data treatment and processing is not available. In particular, we focus on the evolution over time of the flow thickness of the debris with dam-break initial conditions. The proposed methodology consists of establishing an approximation of the numerical model using a polynomial chaos expansion that is used in place of the original model, saving computational burden. The values of the parameters are then inferred through a Bayesian approach with a particular focus on inference discrepancies that some of the important features predicted by the model exhibit. We build the model approximation using a preconditioned non-intrusive method and show that a suitable prior parameter distribution is critical to the construction of an accurate surrogate model. The results of the Bayesian inference suggest that utilizing directly the available experimental data could lead to incorrect conclusions, including the over-determination of parameters. To avoid such drawbacks, we propose to base the inference on few significant features extracted from the original data. Our experiments confirm the validity of this approach, and show that it does not lead to significant loss of information. It is further computationally more efficient than the direct approach, and can avoid the construction of an elaborate error model. 相似文献
20.
岩土工程现场勘察试验通常只能获得有限的试验数据,据此难以真实地量化土体参数的空间变异性。提出了考虑土体参数空间变异性的概率反演和边坡可靠度更新方法,基于室内和现场两种不同来源的试验数据概率反演空间变异参数统计特征和更新边坡可靠度水平,并给出了计算流程。此外为合理地描述土体参数先验信息,发展了不排水抗剪强度非平稳随机场模型。最后通过不排水饱和黏土边坡算例验证了提出方法的有效性,并探讨了试验数据和钻孔位置对边坡后验失效概率的影响。结果表明：提出方法实现了空间变异土体参数概率反演与边坡可靠度更新的一体化,基于有限的多源试验数据概率反演得到的土体参数均值与试验数据非常吻合,明显降低了对参数不确定性的估计,更新的边坡可靠度水平显著增加。受土体参数空间自相关性的影响,试验数据对钻孔取样点附近区域土体参数统计特征更新的影响明显大于距离取样点较远区域。 相似文献