Probabilistic approach for active control of structures: experimental verification

In our previous study (Earthquake Engineering and Structural Dynamics 2003; 32 :2301), we have developed a probabilistic algorithm for active control of structures. In the probabilistic control algorithm, the control force is determined by the probability that the structural energy exceeds a specified target critical energy, and the direction of a control force is determined by the Lyapunov controller design method. In this paper, an experimental verification of the proposed probabilistic control algorithm is presented. A three‐story test structure equipped with an active mass driver (AMD) has been used. The effectiveness of the control algorithm has been examined by exciting the test structure using a sinusoidal signal, a scaled El Centro earthquake and a broadband Gaussian white noise; and, especially, experiments on control have been performed under different conditions to that of system identification in order to prove the stability and robustness of the proposed control algorithm. The experimental results indicate that the probabilistic control algorithm can achieve a significant response reduction under various types of ground excitations even when the modeling error exists. Copyright © 2004 John Wiley & Sons, Ltd.     

基于正交多项式逼近法的岩土参数概率分布推断

针对岩上参数样本容量较大的情况,基于数值分析中的逼近原理,直接根据试验样本值,运用勒让德正交多项式来拟合岩土参数的概率密度函数,并用K-S检验法从理论上证明所求的密度函数的正确性和实用性。     

Assessing the reliability of probabilistic flood inundation model predictions

An ability to quantify the reliability of probabilistic flood inundation predictions is a requirement not only for guiding model development but also for their successful application. Probabilistic flood inundation predictions are usually produced by choosing a method of weighting the model parameter space, but previous study suggests that this choice leads to clear differences in inundation probabilities. This study aims to address the evaluation of the reliability of these probabilistic predictions. However, a lack of an adequate number of observations of flood inundation for a catchment limits the application of conventional methods of evaluating predictive reliability. Consequently, attempts have been made to assess the reliability of probabilistic predictions using multiple observations from a single flood event. Here, a LISFLOOD‐FP hydraulic model of an extreme (>1 in 1000 years) flood event in Cockermouth, UK, is constructed and calibrated using multiple performance measures from both peak flood wrack mark data and aerial photography captured post‐peak. These measures are used in weighting the parameter space to produce multiple probabilistic predictions for the event. Two methods of assessing the reliability of these probabilistic predictions using limited observations are utilized; an existing method assessing the binary pattern of flooding, and a method developed in this paper to assess predictions of water surface elevation. This study finds that the water surface elevation method has both a better diagnostic and discriminatory ability, but this result is likely to be sensitive to the unknown uncertainties in the upstream boundary condition. Copyright © 2015 John Wiley & Sons, Ltd.     

高光谱影像概率分类向量机分类方法研究

从分析基于支持向量机和相关向量机的高光谱影像分类方法的优势和不足出发,将基于概率分类向量机的方法用于高光谱影像分类试验。在贝叶斯理论框架下,概率分类向量机为基函数权值引入截断Gauss先验概率分布,使得不同类别的基函数权值具有不同符号的先验分布,并利用EM算法进行参数推断,得到足够稀疏的概率模型,弥补了相关向量机选取错误类别的样本作为相关向量的不足,从而有效地提高了模型的分类精度和稳定性。OMIS和PHI影像分类试验表明,概率分类向量机能够很好地应用在高光谱影像分类。     

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.     

A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts

Traditional precipitation skill scores are affected by the well-known“double penalty”problem caused by the slight spatial or temporal mismatches between forecasts and observations. The fuzzy (neighborhood) method has been proposed for deterministic simulations and shown some ability to solve this problem. The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts. We developed an ensemble precipitation verification skill score, i.e., the Spatial Continuous Ranked Probability Score (SCRPS), and used it to extend spatial verification from deterministic into ensemble forecasts. The SCRPS is a spatial technique based on the Continuous Ranked Probability Score (CRPS) and the fuzzy method. A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency, which were then used in the reference score to calculate the skill score of the SCRPS. The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained. The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used.     

节理分布空间变异的地下洞室稳定性概率分析

以岩土材料力学参数空间变异性的"点估计-有限元"分析方法为基础,结合节理分析时自身存在几何模型、网格划分等特性,扩展了该方法在节理分布空间变异性分析方面的适用性,明确了具体的研究步骤与方法。以某抽水蓄能水电站为例,通过分析节理空间变异性对围岩变形与塑性区的影响,验证了扩展后该方法的准确性和合理性。对工程案例开挖揭露的1400余条节理进行概率统计,建立了节理空间变异性的有限元分析模型;采用扩展后的概率分析方法,研究了节理分布对地下洞室群围岩开挖稳定性的影响。研究结果表明:(1)对比概率分析得到的围岩变形概率分布与现场监测结果,发现剔除变形异常点后监测变形量值大部分位于得到的位移概率分布范围内,说明节理的空间变异性是导致监测变形波动的主要影响因素;(2)围岩变形概率分布的标准差能有效识别出围岩开挖变形受节理空间变异性的影响程度,对于所给出的案例依次为:机窝>边墙>顶拱;(3)围岩塑性区的概率分区能合理判断地下洞室群开挖时受节理影响较大的区域和范围,为工程施工的支护设计提供依据。     

Cartograms for Use in Forecasting Weather-Driven Natural Hazards

This paper evaluates the potential of using cartograms for visualizing and interpreting forecasts of weather-driven natural hazards in the context of global weather forecasting and early warning systems. The use of cartograms is intended to supplement traditional cartographic representations of the hazards in order to highlight the severity of an upcoming event. Cartogrammetric transformations are applied to forecasts of floods, heatwaves, windstorms and snowstorms taken from the European Centre for Medium-range Weather Forecasts (ECMWF) forecast archive. Key cartogram design principles in standard weather forecast visualization are tested. Optimal cartogram transformation is found to be dependent on geographical features (such as coastlines) and forecast features (such as snowstorm intensity). For highly spatially autocorrelated weather variables used in analysing several upcoming natural hazards such as 2m temperature anomaly, the visualization of the distortion provides a promising addition to standard forecast visualizations for highlighting upcoming weather-driven natural hazards.     

Re-examining urban region and inferring regional function based on spatial–temporal interaction

Urban system is shaped by the interactions between different regions and regions planned by the government, then reshaped by human activities and residents’ needs. Understanding the changes of regional structure and dynamics of city function based on the residents’ movement demand are important to evaluate and adjust the planning and management of urban services and internal structures. This paper constructed a probabilistic factor model on the basis of probabilistic latent semantic analysis and tensor decomposition, for purpose of understanding the higher order interactive population mobility and its impact on urban structure changes. First, a four-dimensional tensor of time (T)?×?week (W)?×?origin (O)?×?destination (D) was constructed to identify the day-to-day activities in three time modes and weekly regularity of weekday/weekend pattern. Then we reclassified the urban regions based on the space clustering formed by the space factor matrix and core tensor. Finally, we further analysed the space–time interaction on different time scales to deduce the actual function and connection strength of each region. Our research shows that the application of individual-based spatial–temporal data in human mobility and space–time interaction study can help to analyse urban spatial structure and understand the actual regional function from a new perspective.