基于高斯曲率模态相关系数的弯梁桥支座损伤研究          下载免费PDF全文

梁栋  陈磊  杜延昭  张硕 《世界地震工程》2018,34(3):069-77

对于弯梁桥支座病害中普遍存在的刚度不足,甚至脱空现象,提出利用平面多阶模态及高斯曲率模态相关系数来识别弯梁桥支座的损伤情况。在理论推导的基础上,以某小半径弯梁桥为研究对象,建立了相关分析模型。分析结果表明:无论单支座损伤,还是多支座损伤,相应支座处的高斯曲率模态相关系数与其它支座位置处的差异明显,可实现支座损伤的准确定位;且支座损伤越严重,相关系数下降越多;支点预偏心和抗扭支承等因素对损伤定位没有影响,相邻控制点处相关系数有所降低,但不会影响支座损伤识别的有效性。  相似文献   

钢筋混凝土框架柱震损程度量化鉴定方法          下载免费PDF全文

曲博闻  张昊宇  毛晨曦  张亮泉 《世界地震工程》2018,34(3):085-93

钢筋混凝土结构震后损伤鉴定中,最常见的方式是鉴定者观察房屋破坏现象,根据经验给出震损等级。该方法直观高效,但对鉴定者的专业经验要求较高,且鉴定结果的主观差异较大。对此以RC框架柱为对象,开展了基于震损现象的震损量化鉴定方法研究:在RC框架柱震损现象量化试验基础上给出基于构件骨架曲线特征阶段的震损分级方法;对7个RC框架柱试件进行了改进Park-Ang损伤指数分析,建立了RC框架柱损伤指数-震损分级-震损现象的对应关系;基于RC框架柱的试验结果及典型震害编制了RC框架柱震损图集,并给出了使用图集进行框架柱震损鉴定的流程及方法。使用该方法对2个实际震害中的RC框架柱进行了震损鉴定,可为更加客观以及准确地开展钢筋混凝土结构的震损鉴定提供参考。  相似文献   

Rayleigh波入射下相邻山体地震响应边界元法模拟          下载免费PDF全文

张雪  刘中宪    何颖 《世界地震工程》2018,34(4):008-15

采用间接边界元法（IBEM）,对Rayleigh波入射下两邻近山体的地震响应进行了定量分析。结果表明:与单个山体在Rayleigh波入射下的地震响应相比,两山地形的地震反应规律更为复杂,反应特征受控于入射波频率和山体间距等因素。总体上看:入射Rayleigh波频率较低时,两山间距对双山地形地震反应影响较大。低频波入射,受邻近山体影响,迎波面山体水平位移峰值约为入射波水平位移的6.3倍,放大效应可达单山作用的1.5倍。且山体山脚处竖向位移的频谱振荡更为剧烈,特定频率下山脚的竖向位移反应可达单山放大效应的1.6倍。较高频波入射时,迎波面一侧山体受邻近山体影响较小,且对波表现出明显的屏障效应,背波面山体地震反应强度被削弱。  相似文献   

某1.5MW风电塔振动台缩尺模型设计          下载免费PDF全文

孟家瑶  戴靠山    毛振西  王英  赵志 《世界地震工程》2018,34(4):191-198

风电塔是一种顶部有较大偏心质量的高耸薄壁悬臂结构,以某1.5MW水平轴三叶片风电塔为研究对象,重点关注风电塔振动台试验缩尺模型设计。根据量纲分析理论和相似条件,基于模型质量分布和刚度分布等效原则,设计模型塔筒截面及附加质量,保证模型与原型结构自振频率和振型相似。通过对比分析模型动力特性测试结果与原型实测结果,验证了该模型设计方法的合理性,可为同类型风电塔振动台试验设计提供参考与依据。针对该柔性对称高塔模型在动力特性测试中出现的正交耦合振动及拍振现象也进行了详细阐述。  相似文献   

Simulation of intrinsic random fields of order <Emphasis Type="Italic">k</Emphasis> with a continuous spectral algorithm     

Daisy?Arroyo  Xavier?EmeryEmail authorView author&#;s OrcID profile 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(11):3245-3255

Intrinsic random fields of order k, defined as random fields whose high-order increments (generalized increments of order k) are second-order stationary, are used in spatial statistics to model regionalized variables exhibiting spatial trends, a feature that is common in earth and environmental sciences applications. A continuous spectral algorithm is proposed to simulate such random fields in a d-dimensional Euclidean space, with given generalized covariance structure and with Gaussian generalized increments of order k. The only condition needed to run the algorithm is to know the spectral measure associated with the generalized covariance function (case of a scalar random field) or with the matrix of generalized direct and cross-covariances (case of a vector random field). The algorithm is applied to synthetic examples to simulate intrinsic random fields with power generalized direct and cross-covariances, as well as an intrinsic random field with power and spline generalized direct covariances and Matérn generalized cross-covariance.  相似文献   

Admissible nested covariance models over spheres cross time     

Ana?Peron  Emilio?Porcu  Xavier?EmeryEmail authorView author&#;s OrcID profile 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(11):3053-3066

Nested covariance models, defined as linear combinations of basic covariance functions, are very popular in many branches of applied statistics, and in particular in geostatistics. A notorious limit of nested models is that the constants in the linear combination are bound to be nonnegative in order to preserve positive definiteness (admissibility). This paper studies nested models on d-dimensional spheres and spheres cross time. We show the exact interval of admissibility for the constants involved in the linear combinations. In particular, we show that at least one constant can be negative. One of the implications is that one can obtain a nested model attaining negative correlations. We provide characterization theorems for arbitrary linear combinations as well as for nonconvex combinations involving two covariance functions. We illustrate our findings through several examples involving nonconvex combinations of well-known parametric families of covariance functions.  相似文献   

Assessing titanium dioxide nanoparticles transport models by Bayesian uncertainty analysis     

Jin?Liu  Xiankui?ZengEmail authorView author&#;s OrcID profile  Jichun?Wu  Xiuyu?Liang  Yuanyuan?Sun  Hongbin?Zhan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(12):3365-3379

With the rapid growth of nanotechnology industry, nanomaterials as an emerging pollutant are gradually released into subsurface environments and become great concerns. Simulating the transport of nanomaterials in groundwater is an important approach to investigate and predict the impact of nanomaterials on subsurface environments. Currently, a number of transport models are used to simulate this process, and the outputs of these models could be inconsistent with each other due to conceptual model uncertainty. However, the performances of different models on simulating nanoparticles transport in groundwater are rarely assessed in Bayesian framework in previous researches, and these will be the primary objective of this study. A porous media column experiment is conducted to observe the transport of Titanium Dioxide Nanoparticles (nano-TiO₂). Ten typical transport models which consider different chemical reaction processes are used to simulate the transport of nano-TiO₂, and the observed nano-TiO₂ breakthrough curves data are used to calibrate these models. For each transport model, the parameter uncertainty is evaluated using Markov Chain Monte Carlo, and the DREAM_(ZS) algorithm is used to sample parameter probability space. Moreover, the Bayesian model averaging (BMA) method is used to incorporate the conceptual model uncertainty arising from different chemical reaction based transport models. The results indicate that both two-sites and nonequilibrium sorption models can well reproduce the retention of nano-TiO₂ transport in porous media. The linear equilibrium sorption isotherm, first-order degradation, and mobile-immobile models fail to describe the nano-TiO₂ retention and transport. The BMA method could instead provide more reliable estimations of the predictive uncertainty compared to that using a single model.  相似文献   

Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processes     

Marianna?Siino  Giada?AdelfioEmail authorView author&#;s OrcID profile  Jorge?Mateu 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(12):3525-3539

We propose a new fitting method to estimate the set of second-order parameters for the class of homogeneous spatio-temporal log-Gaussian Cox point processes. With simulations, we show that the proposed minimum contrast procedure, based on the spatio-temporal pair correlation function, provides reliable estimates and we compare the results with the current available methods. Moreover, the proposed method can be used in the case of both separable and non-separable parametric specifications of the correlation function of the underlying Gaussian Random Field. We describe earthquake sequences comparing several Cox model specifications.  相似文献   

A perfect prognosis approach for daily precipitation series in consideration of space–time correlation structure     

Malika?KhaliliEmail authorView author&#;s OrcID profile  Van-Thanh-Van?Nguyen 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(12):3333-3364

Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7–8):2261–2278.  https://doi.org/10.1007/s00382-016-3443-6, 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15–32.  https://doi.org/10.1002/joc.3402, 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability.  相似文献   

Quantifying the sources of simulation uncertainty in natural catastrophe models     

Jo?KaczmarskaEmail authorView author&#;s OrcID profile  Stephen?Jewson  Enrica?Bellone 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(3):591-605

The risk from natural catastrophes is typically estimated using complex simulation models involving multiple stochastic components in a nested structure. This risk is principally assessed via the mean annual loss, and selected quantiles of the annual loss. Determining an appropriate simulation strategy is important in order to achieve satisfactory convergence of these statistics, without excessive computation time and data storage requirements. This necessitates an understanding of the relative contribution of each of the stochastic components to the total variance of the statistics. A simple framework using random effects models and analysis of variance is used to partition the variance of the annual loss, which permits calculation of the variance of the mean annual loss with varying numbers of samples of each of the components. An extension to quantiles is developed using the empirical distribution function in combination with bootstrapping. The methods are applied to a European flood model, where the primary stochastic component relates to the frequency and severity of flood events, and three secondary components relate to defence levels, exposure locations and building vulnerability. As expected, it is found that the uncertainty due to the secondary components increases as the size of the portfolio of exposures decreases, and is higher for industrial and commercial business, compared with residential for all statistics of interest. In addition, interesting insights are gained as to the impact of flood defences on convergence.  相似文献