Stochastic perturbation analysis of groundwater flow. Spatially variable soils,semi-infinite domains and large fluctuations

As is well known, a complete stochastic solution of the stochastic differential equation governing saturated groundwater flow leads to an infinite hierarchy of equations in terms of higher-order moments. Perturbation techniques are commonly used to close this hierarchy, using power-series expansions. These methods are applied by truncating the series after a finite number of terms, and products of random gradients of conductivity and head potential are neglected. Uncertainty regarding the number or terms required to yield a sufficiently accurate result is a significant drawback with the application of power series-based perturbation methods for such problems. Low-order series truncation may be incapable of representing fundamental characteristics of flow and can lead to physically unreasonable and inaccurate solutions of the stochastic flow equation. To support this argument, one-dimensional, steady-state, saturated groundwater flow is examined, for the case of a spatially distributed hydraulic conductivity field. An ordinary power-series perturbation method is used to approximate the mean head, using second-order statistics to characterize the conductivity field. Then an interactive perturbation approach is introduced, which yields improved results compared to low-order, power-series perturbation methods for situations where strong interactions exist between terms in such approximations. The interactive perturbation concept is further developed using Feynman-type diagrams and graph theory, which reduce the original stochastic flow problem to a closed set of equations for the mean and the covariance functions. Both theoretical and practical advantages of diagrammatic solutions are discussed; these include the study of bounded domains and large fluctuations.     

Correlation failure analysis of an uncertain hysteretic vibration system

In this paper, a numerical method for correlation sensitivity analysis of a nonlinear random vibration system is presented. Based on the first passage failure model, the probability perturbation method is employed to determine the statistical characteristics of failure modes and the correlation between them. The sensitivity of correlation between failure modes with respect to random parameters characterizing the uncertainty of the hysteretic loop is discussed. In a numerical example, a two-DOF shear structure with uncertain hysteretic restoring force is considered. The statistical characteristics of response, failure modes and the sensitivity of random hysteretic loop parameters are provided, and also compared with a Monte Carlo simulation.     

Seismic interaction in linearly connected electrical substation equipment

An electrical substation consists of a complex set of equipment items that are interconnected through conductor buses or cables. If the connections are not sufficiently flexible, significant dynamic interaction may occur between the connected equipment items during a seismic excitation. This interaction is believed to be responsible for some of the observed substation equipment damage in recent earthquakes. This paper investigates the interaction between two equipment items connected by a linear spring‐dashpot or spring‐dashpot‐mass element representing a conductor bus. It is found that the interaction between the two equipment items may significantly amplify the response of the higher‐frequency equipment item. The influences of various key parameters on the interaction effect are quantified. Means for reducing the adverse interaction effect are described. Copyright © 2001 John Wiley & Sons, Ltd.     

Simulation of artificial random field considering the temporal-spatial variation

Introduction For the seismic design of special structures such as nuclear power station, marine platform, long-span bridge and dam, generally the time-history response analysis of the structure under seismic excitation is imperative, which was coded in most seismic design codes. The earthquake records suitable for the seismic situation and site condition are necessary to be used as the seismic input in the dynamic analysis of structures. As a result of the limited observational condition of st…     

Feedback–feedforward control of offshore platforms under random waves

This study investigates the control of jacket‐type offshore platforms. The deck displacement of jacket‐type offshore platforms can be controlled using both passive and active control mechanisms. Among the passive control mechanisms, a tuned mass damper concept is studied in this paper. Active control mechanisms considered here include the active mass damper, the active tendon mechanism and the propeller thruster. An optimal frequency domain approach to active control of wave‐excited platforms is used in which the H₂ norm of the transfer function from the external disturbance to the regulated output is minimized. In this study, the hydrodynamic drag force is evaluated using the JONSWAP wave spectrum. Unlike conventional linearization approaches, the influence of non‐linearity in the drag force is retained in this scheme by expressing the non‐linear force components in terms of higher‐order convolutions of the water‐particle velocities. To demonstrate the effectiveness of this scheme, the platform performance with and without control devices under different sea states is evaluated. It is demonstrated that the control devices are useful in reducing the displacement response of jacket‐type offshore platforms, especially when the wave forces are concentrated at frequencies close to the natural frequencies of the platform. This becomes especially significant in deep waters because the natural frequencies of jacket‐type platforms fall closer to the dominant wave frequencies in deep waters. Adding control devices to deep water platforms will ensure a reduction both in the global response of the platform and in localized effects, such as the fatigue of welded joints. Copyright © 2001 John Wiley & Sons, Ltd.     

Classifying natural-language spatial relation terms with random forest algorithm

The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.     

基于随机森林的暴雨灾害人口损失预估模型及应用

基于2009—2017年的广西县级暴雨灾情记录,综合考虑致灾因子、孕灾环境和承灾体因素选取7个解释变量,运用随机森林算法,构建暴雨灾害人口损失预估模型;并以精细化网格降水实况分析和预报产品驱动模型,预估是否发生人口损失。研究结果表明:模型训练样本及测试样本的分类准确率均在90%以上,致灾因子(降水情况)是最主要的解释变量,重要性从大到小依次是前10 d降水距平百分率、过程最大日雨量、最大小时雨量和短时强降水频次。应用智能网格降水产品对广西地区近两年的暴雨灾害过程进行回报试验,准确率超过70%。     

基于FY-4A卫星和随机森林算法的西藏山南市雷电短临预报方法

针对青藏高原地区雷电短临预报缺乏雷达资料的问题,采用FY-4A卫星多通道数据、欧洲中心第5代再分析资料(ERA5)中的对流指数、闪电定位仪资料等多源监测数据,根据雷电的发生、发展机理,提出了18个关键预报因子,利用随机森林算法建立了适用于西藏山南地区的雷电短临预报模型。统计分析各预报因子在有无雷电天气样本中的概率密度分布与随机森林模型得到的特征重要度指标,结果表明提出的预报因子物理意义明确,建立的模型可信度较高。利用随机森林算法分别对未来10 min、20 min、30 min建立雷电预报模型,并与光流外推预报方法进行对比检验,结果表明：随机森林模型预报效果命中率(POD)、临界成功指数(CSI)均高于光流法,空报率(FAR)也相对较低;未来20 min的随机森林预报模型CSI评分最高,整体预报效果最佳。