砌体和钢混结构建筑群的破坏概率模型研究          下载免费PDF全文

王磊  孙海  高惠瑛  姜慧 《世界地震工程》2018,34(3):146-152

通过对已有震害预测的易损性矩阵进行研究,对目前易损性矩阵的概率模型进行了修改。分析了华南地区建筑群的3种主要结构类型:钢混结构,砖混结构和砖木结构。将3种结构在各种烈度下的破坏状态分别进行了拟合和参数计算,针对其差异性提出了偏态分布和正态分布,给出了不同的破坏概率模型。在广东省惠州市数据的验算下,证明了对于砌体和钢混建筑群易损性矩阵的呈现有一定的参考意义。  相似文献   

基于多项式拟合均匀荷载面曲率曲线的结构损伤识别方法          下载免费PDF全文

唐盛华  苏彬建  方志  张学兵 《世界地震工程》2018,34(1):121-130

为仅利用结构损伤状态的柔度矩阵对结构进行损伤程度识别,先对损伤状态的均匀荷载面曲率曲线进行最小二乘法拟合。根据曲率曲线差判断结构的损伤位置,对损伤位置的点进行剔除后,再利用未损伤位置上的点进行局部最小二乘法拟合,代替损伤前的均匀荷载面曲率曲线,用于结构的损伤定位与定量。通过一简支梁数值算例,先以理论的二次多项式进行拟合,考虑单损伤和多损伤的情况,进行损伤识别分析,再分析多项式次数、测点数目以及不同噪声水平对损伤定量精度的影响。结果表明:在一定范围内,次数越高拟合误差越小,但差别不明显,采用理论的二次多项式拟合即可满足结构损伤识别要求,无噪声的情况下,测点数目减半不影响损伤识别的精度,该方法具有一定的抗噪声能力。  相似文献   

Simulating rainfall time-series: how to account for statistical variability at multiple scales?     

Fabio?OrianiEmail authorView author&#;s OrcID profile  Raj?Mehrotra  Grégoire?Mariethoz  Julien?Straubhaar  Ashish?Sharma  Philippe?Renard 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):321-340

Daily rainfall is a complex signal exhibiting alternation of dry and wet states, seasonal fluctuations and an irregular behavior at multiple scales that cannot be preserved by stationary stochastic simulation models. In this paper, we try to investigate some of the strategies devoted to preserve these features by comparing two recent algorithms for stochastic rainfall simulation: the first one is the modified Markov model, belonging to the family of Markov-chain based techniques, which introduces non-stationarity in the chain parameters to preserve the long-term behavior of rainfall. The second technique is direct sampling, based on multiple-point statistics, which aims at simulating a complex statistical structure by reproducing the same data patterns found in a training data set. The two techniques are compared by first simulating a synthetic daily rainfall time-series showing a highly irregular alternation of two regimes and then a real rainfall data set. This comparison allows analyzing the efficiency of different elements characterizing the two techniques, such as the application of a variable time dependence, the adaptive kernel smoothing or the use of low-frequency rainfall covariates. The results suggest, under different data availability scenarios, which of these elements are more appropriate to represent the rainfall amount probability distribution at different scales, the annual seasonality, the dry-wet temporal pattern, and the persistence of the rainfall events.  相似文献   

Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall–runoff modeling     

Vahid?NouraniEmail authorView author&#;s OrcID profile  Afshin?Partoviyan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):545-562

Successful modeling of stochastic hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. On the other hand in training phase of any Artificial Intelligence based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole population. Accordingly in the present article first, wavelet-based denoising method was used in order to smooth hydrological time series and then small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smoothed time series to form different denoised-jittered training data sets, for Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling of daily and multi-step-ahead rainfall–runoff process of the Milledgeville station of the Oconee River and the Pole Saheb station of the Jighatu River watersheds, respectively located in USA and Iran. The proposed hybrid data pre-processing approach in the present study is used for the first time in modeling of time series and especially in modeling of hydrological processes. Furthermore, the impacts of denoising (smoothing) and noise injection (jittering) have been simultaneously investigated neither in hydrology nor in any other engineering fields. To evaluate the modeling performance, the outcomes were compared with the results of multi linear regression and Auto Regressive Integrated Moving Average models. Comparing the achieved results via the trained ANN and ANFIS models using denoised-jittered data showed that the proposed data pre-processing approach which serves both denoising and jittering techniques could improve performance of the ANN and ANFIS based single-step-ahead rainfall–runoff modeling of the Milledgeville station up to 14 and 12% and of the Pole Saheb station up to 22 and 16% in the verification phase. Also the results of multi-step-ahead modeling using the proposed data pre-processing approach showed improvement of modeling for both watersheds.  相似文献   

Estimation of carbon stock for greenhouse gas emissions from hydropower reservoirs     

Amit?KumarEmail authorView author&#;s OrcID profile  M.?P.?Sharma  Tao?Yang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(11):3183-3193

The identification and accurate quantification of sources or sinks of greenhouse gas (GHG) have become a key challenge for scientists and policymakers working on climate change. The creation of a hydropower reservoir, while damming a river for power generation, converts the terrestrial ecosystems into aquatic and subsequently aerobic and anaerobic decomposition of flooded terrestrial soil organic matter resulting in the emission of significant quantity of GHG to the atmosphere. Tropical/subtropical hydropower reservoirs are more significant sources of GHG compared to boreal or temperate one. This paper aims to estimate the emission factor (gCO₂eq./kWh) and net GHG emission from Koteshwar hydropower reservoir in Uttarakhand, India. Further, estimated GHG are compared with those from global reservoirs located in the same eco-region so that its impact could be timely minimized/mitigated. Results have shown that emission factor and net GHG emission of Koteshwar reservoir are, respectively, estimated as 13.87 gCO₂eq./kWh and 167.70 Gg C year^?1 which are less than other global reservoirs located in the same eco-region. This information could be helpful for the hydropower industries to construct reservoirs in tropical eco-regions.  相似文献   

Declustering experimental variograms by global estimation with fourth order moments     

Mehdi?RezvandehyEmail authorView author&#;s OrcID profile  Clayton?V.?Deutsch 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(1):261-277

The variogram is a key parameter for geostatistical estimation and simulation. Preferential sampling may bias the spatial structure and often leads to noisy and unreliable variograms. A novel technique is proposed to weight variogram pairs in order to compensate for preferential or clustered sampling . Weighting the variogram pairs by global kriging of the quadratic differences between the tail and head values gives each pair the appropriate weight, removes noise and minimizes artifacts in the experimental variogram. Moreover, variogram uncertainty could be computed by this technique. The required covariance between the pairs going into variogram calculation, is a fourth order covariance that must be calculated by second order moments. This introduces some circularity in the calculation whereby an initial variogram must be assumed before calculating how the pairs should be weighted for the experimental variogram. The methodology is assessed by synthetic and realistic examples. For synthetic example, a comparison between the traditional and declustered variograms shows that the declustered variograms are better estimates of the true underlying variograms. The realistic example also shows that the declustered sample variogram is closer to the true variogram.  相似文献   

Distributions-oriented wind forecast verification by a hidden Markov model for multivariate circular–linear data     

Gianluca?MastrantonioEmail authorView author&#;s OrcID profile  Alessio?Pollice  Francesca?Fedele 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(1):169-181

Winds from the North–West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations over a residential neighborhood in the city of Taranto (Italy). In 2012 the local government prescribed a reduction of industrial emissions by 10% every time such meteorological conditions are forecasted 72 h in advance. Wind forecasting is addressed using the Weather Research and Forecasting (WRF) atmospheric simulation system by the Regional Environmental Protection Agency. In the context of distributions-oriented forecast verification, we propose a comprehensive model-based inferential approach to investigate the ability of the WRF system to forecast the local wind speed and direction allowing different performances for unknown weather regimes. Ground-observed and WRF-forecasted wind speed and direction at a relevant location are jointly modeled as a 4-dimensional time series with an unknown finite number of states characterized by homogeneous distributional behavior. The proposed model relies on a mixture of joint projected and skew normal distributions with time-dependent states, where the temporal evolution of the state membership follows a first order Markov process. Parameter estimates, including the number of states, are obtained by a Bayesian MCMC-based method. Results provide useful insights on the performance of WRF forecasts in relation to different combinations of wind speed and direction.  相似文献   

Estimating hydrologic model uncertainty in the presence of complex residual error structures   总被引：1，自引：1，他引：0  

S.?SamadiEmail authorView author&#;s OrcID profile  D.?L.?Tufford  G.?J.?Carbone 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(5):1259-1281

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On approximation for fractional stochastic partial differential equations on the sphere     

Vo?V.?Anh  Philip?Broadbridge  Andriy?Olenko  Yu?Guang?WangEmail authorView author&#;s OrcID profile 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(9):2585-2603

This paper gives the exact solution in terms of the Karhunen–Loève expansion to a fractional stochastic partial differential equation on the unit sphere \({\mathbb {S}}^{2} \subset {\mathbb {R}}^{3}\) with fractional Brownian motion as driving noise and with random initial condition given by a fractional stochastic Cauchy problem. A numerical approximation to the solution is given by truncating the Karhunen–Loève expansion. We show the convergence rates of the truncation errors in degree and the mean square approximation errors in time. Numerical examples using an isotropic Gaussian random field as initial condition and simulations of evolution of cosmic microwave background are given to illustrate the theoretical results.  相似文献   

Reducing uncertainty in stochastic streamflow generation and reservoir sizing by combining observed,reconstructed and projected streamflow     

Jason?PatskoskiEmail authorView author&#;s OrcID profile  A.?Sankarasubramanian 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(4):1065-1083

Reservoir sizing is one of the most important aspects of water resources engineering as the storage in a reservoir must be sufficient to supply water during extended droughts. Typically, observed streamflow is used to stochastically generate multiple realizations of streamflow to estimate the required storage based on the Sequent Peak Algorithm (SQP). The main limitation in this approach is that the parameters of the stochastic model are purely derived from the observed record (limited to less than 80 years of data) which does not have information related to prehistoric droughts. Further, reservoir sizing is typically estimated to meet future increase in water demand, and there is no guarantee that future streamflow over the planning period will be representative of past streamflow records. In this context, reconstructed streamflow records, usually estimated based on tree ring chronologies, provide better estimates of prehistoric droughts, and future streamflow records over the planning period could be obtained from general circulation models (GCMs) which provide 30 year near-term climate change projections. In this study, we developed paleo streamflow records and future streamflow records for 30 years are obtained by forcing the projected precipitation and temperature from the GCMs over a lumped watershed model. We propose combining observed, reconstructed and projected streamflows to generate synthetic streamflow records using a Bayesian framework that provides the posterior distribution of reservoir storage estimates. The performance of the Bayesian framework is compared to a traditional stochastic streamflow generation approach. Findings based on the split-sample validation show that the Bayesian approach yielded generated streamflow traces more representative of future streamflow conditions than the traditional stochastic approach thereby, reducing uncertainty on storage estimates corresponding to higher reliabilities. Potential strategies for improving future streamflow projections and its utility in reservoir sizing and capacity expansion projects are also discussed.  相似文献