Exploring Spatial Non‐Stationarity and Varying Relationships between Crash Data and Related Factors Using Geographically Weighted Poisson Regression

The spatial nature of crash data highlights the importance of employing Geographical Information Systems (GIS) in different fields of safety research. Recently, numerous studies have been carried out in safety analysis to investigate the relationships between crashes and related factors. Trip generation as a function of land use, socio‐economic, and demographic characteristics might be appropriate variables along with network characteristics and traffic volume to develop safety models. Generalized Linear Models (GLMs) describe the relationships between crashes and the explanatory variables by estimating the global and fixed coefficients. Since crash occurrences are almost certainly influenced by many spatial factors; the main objective of this study is to employ Geographically Weighted Poisson Regression (GWPR) on 253 traffic analysis zones (TAZs) in Mashhad, Iran, using traffic volume, network characteristics and trip generation variables to investigate the aspects of relationships which do not emerge when using conventional global specifications. GWPR showed an improvement in model performance as indicated by goodness‐of‐fit criteria. The results also indicated the non‐stationary state in the relationships between the number of crashes and all independent variables.     

Determining safe yield and mapping water level zoning in groundwater resources of the Neishabour Plain

Groundwater is a crucial sources of water supply, especially in arid and semi-arid areas around the world. With uncontrolled withdrawals and limited availability of these resources, it is essential to determine the safe yield of these valuable resources. The Hill method approach was used in this study to determine the safe yield the Neishabour aquifer in Khorasan Razvi province in Iran. The results showed that the safe yield in the Neishabour aquifer is 60% lower than the current pumping amounts...     

Optimizing compaction calculation for improved probabilistic 2D mapping

Integration of all available data in reservoir characterization is critically important. 2D mapping is a reliable and robust technique that allows integration of multiple secondary data, including geological and geophysical surfaces and maps, to generate realistic summaries of reservoir quality at each location in an area of interest with an associated measure of uncertainty. This is achieved in 2D mapping with a more straightforward implementation, requiring significantly less time and fewer resources than three-dimensional modelling. In this paper, we propose an approach for the empirical calculation and optimization of differential compaction maps by leveraging existing well control for the use in 2D mapping. Success of the proposal is demonstrated through tests of accuracy, precision and fairness of the local uncertainty distributions for 100 new stratigraphical wells drilled in the Christina Lake and Kirby East area.     

Behavior of moment‐resisting frame structures subjected to near‐fault ground motions

Near‐fault ground motions impose large demands on structures compared to ‘ordinary’ ground motions. Recordings suggest that near‐fault ground motions with ‘forward’ directivity are characterized by a large pulse, which is mostly orientated perpendicular to the fault. This study is intended to provide quantitative knowledge on important response characteristics of elastic and inelastic frame structures subjected to near‐fault ground motions. Generic frame models are used to represent MDOF structures. Near‐fault ground motions are represented by equivalent pulses, which have a comparable effect on structural response, but whose characteristics are defined by a small number of parameters. The results demonstrate that structures with a period longer than the pulse period respond very differently from structures with a shorter period. For the former, early yielding occurs in higher stories but the high ductility demands migrate to the bottom stories as the ground motion becomes more severe. For the latter, the maximum demand always occurs in the bottom stories. Preliminary regression equations are proposed that relate the parameters of the equivalent pulse to magnitude and distance. The equivalent pulse concept is used to estimate the base shear strength required to limit story ductility demands to specific target values. Copyright © 2004 John Wiley & Sons, Ltd.     

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

ABSTRACT

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.     

Effects of periodic fluctuations of micro‐polar boundary conditions on shear localizations in granular soil–structure interaction

In this work, the interface behavior between an infinite extended narrow granular layer and a rough surface of rigid body is investigated numerically, using finite element method in the updated Lagrangian (UL) frame. In this regard, the elasto‐plastic micro‐polar (Cosserat) continuum approach is employed to remove the limitations caused by strain‐softening of materials in the classical continuum. The mechanical properties of cohesionless granular soil are described with Lade's model enhanced by polar terms, including Cosserat rotations, curvatures, and couple stresses. Furthermore, the mean grain diameter as the internal length is incorporated into the constitutive relations accordingly. Here, the evolution and location of shear band, within the granular layer in contact with the rigid body, are mainly focused. In this regard, particular attention is paid to the effects of homogeneous distribution and periodic fluctuation of micro‐polar boundary conditions, prescribed along the interface. Correspondingly, the effects of pressure level, mean grain diameter, and stratified soil are also considered. The finite element results demonstrate that the location and evolution of shear band in the granular soil layer are strongly affected by the non‐uniform micro‐polar boundary conditions, prescribed along the interface. It is found that the shear band is located closer to the boundary with less restriction of grain rotations. Furthermore, the predicted thickness of shear band is larger for higher rotation resistance of soil grains along the interface, larger mean grain diameter, and higher vertical pressure. Regarding the stratified soil, comprising a thin layer with slightly different initial void ratio, the shear band moves towards the layer with initially higher void ratio. Copyright © 2011 John Wiley & Sons, Ltd.     

Accounting for Parameter Uncertainty in Reservoir Uncertainty Assessment: The Conditional Finite-Domain Approach

An important aim of modern geostatistical modeling is to quantify uncertainty in geological systems. Geostatistical modeling requires many input parameters. The input univariate distribution or histogram is perhaps the most important. A new method for assessing uncertainty in the histogram, particularly uncertainty in the mean, is presented. This method, referred to as the conditional finite-domain (CFD) approach, accounts for the size of the domain and the local conditioning data. It is a stochastic approach based on a multivariate Gaussian distribution. The CFD approach is shown to be convergent, design independent, and parameterization invariant. The performance of the CFD approach is illustrated in a case study focusing on the impact of the number of data and the range of correlation on the limiting uncertainty in the parameters. The spatial bootstrap method and CFD approach are compared. As the number of data increases, uncertainty in the sample mean decreases in both the spatial bootstrap and the CFD. Contrary to spatial bootstrap, uncertainty in the sample mean in the CFD approach decreases as the range of correlation increases. This is a direct result of the conditioning data being more correlated to unsampled locations in the finite domain. The sensitivity of the limiting uncertainty relative to the variogram and the variable limits are also discussed.