Finite element modeling assumptions impact on seismic response demands of MRF-buildings

Recent seismic events have raised concerns over the safety and vulnerability of reinforced concrete moment resisting frame “RC-MRF” buildings. The seismic response of such buildings is greatly dependent on the computational tools used and the inherent assumptions in the modelling process. Thus, it is essential to investigate the sensitivity of the response demands to the corresponding modelling assumption. Many parameters and assumptions are justified to generate effective structural finite element (FE) models of buildings to simulate lateral behaviour and evaluate seismic design demands. As such, the present study focuses on the development of reliable FE models with various levels of refinement. The effects of the FE modelling assumptions on the seismic response demands on the design of buildings are investigated. the predictive ability of a FE model is tied to the accuracy of numerical analysis; a numerical analysis is performed for a series of symmetric buildings in active seismic zones. The results of the seismic response demands are presented in a comparative format to confirm drift and strength limits requirements. A proposed model is formulated based on a simplified modeling approach, where the most refined model is used to calibrate the simplified model.     

Total least square method applied to rating curves

The ordinary least square method (OLS) has been the most frequently used least square method in hydrological data analysis. Its computational algorithm is simple, and the error analysis is also simple and clear. However, the primary assumption of the OLS method, which states that the dependent variable is the only error‐contaminated variable and all other variables are error free, is often violated in hydrological data analyses. Recently, a matrix algorithm using the singular value decomposition for the total least square (TLS) method has been developed and used in data analyses as errors‐in‐variables model where several variables could be contaminated with observational errors. In our study, the algorithm of the TLS is introduced in the evaluation of rating curves between the flow discharge and the water level. Then, the TLS algorithm is applied to real data set for rating curves. The evaluated TLS rating curves are compared with the OLS rating curves, and the result indicates that the TLS rating curve and the OLS rating curve are in good agreement. The TLS and OLS rating curves are discussed about their algorithms and error terms in the study. Copyright © 2013 John Wiley & Sons, Ltd.     

Climate changes associated with high-amplitude <Emphasis Type="Italic">Sq</Emphasis> geomagnetic variations

When the solar irradiance propagates between the outer magnetospheric regions and the ionosphere, dynamic processes of the magnetosphere-ionosphere-thermosphere system are affected at the lower end of their paths by the interaction of radiation with the neutral troposphere. The main target of this work is to investigate the relationship between the diurnal magnetic field variations resulting from solar activities and the variation in the troposphere temperature. Meteorological and geomagnetic data acquired from different observatories located in Egypt, Portugal and Slovakia in a long-term and daily-term scales were analyzed.     

Numerical investigation on the performance of Wells turbine with non-uniform tip clearance for wave energy conversion

The performance of a Wells turbine with various non-uniform tip clearances was investigated using computational fluid dynamics (CFD). The investigation was performed on numerical models of a NACA0020 blade profile under steady flow conditions. The performance of turbines with uniform and non-uniform tip clearances was compared. The results were also compared with experimental results in literature. It was shown that the performance of turbine with non-uniform tip clearance is similar with that of turbine with uniform one in terms of torque coefficient, input power coefficient, and efficiency. However, the turbine with non-uniform tip clearance seems to have a preferable overall performance. An investigation on the flow-field around the turbine blade was performed in order to explain the phenomena.     

A regional Bayesian POT model for flood frequency analysis

Flood frequency analysis is usually based on the fitting of an extreme value distribution to the local streamflow series. However, when the local data series is short, frequency analysis results become unreliable. Regional frequency analysis is a convenient way to reduce the estimation uncertainty. In this work, we propose a regional Bayesian model for short record length sites. This model is less restrictive than the index flood model while preserving the formalism of “homogeneous regions”. The performance of the proposed model is assessed on a set of gauging stations in France. The accuracy of quantile estimates as a function of the degree of homogeneity of the pooling group is also analysed. The results indicate that the regional Bayesian model outperforms the index flood model and local estimators. Furthermore, it seems that working with relatively large and homogeneous regions may lead to more accurate results than working with smaller and highly homogeneous regions.     

Arbitrary amplitude dust-acoustic double-layers in a warm dusty plasma with suprathermal electrons, two-temperature thermal ions, and drifting dust grains

Arbitrary amplitude dust-acoustic double-layers (DA-DLs) in a plasma with suprathermal electrons, two-temperature thermal ions, and warm drifting dust grains are investigated. Our results reveal that the spatial patterns of the DA-DLs are affected by the degree of the electron suprathermality. The electron thermalization involves a decrease of the cold ion component density, for the existence of localized DA-DLs. An increase of the dust drift velocity requires a decrease of κ (the electron spectral index), for the onset of dust-acoustic double-layers. An increase of the Mach number M leads to an increase of the DL amplitude as well as the corresponding electron spectral index for which the DL occurs.     

CGCM3 predictors used for daily temperature and precipitation downscaling in Southern Québec, Canada

This study provides some guidance on the choice of predictor variables from both reanalysis products and the third version of the Canadian Coupled Global Climate Model (CGCM3) outputs for regression-based statistical downscaling models (SDMs) for climate change application in southern Québec (Canada). Twenty CGCM3 grid points and four surface observation sites in the study area were employed. Twenty-five deseasonalized predictors and four deseasonalized predictands (daily maximum and minimum temperatures, precipitation occurrence and wet day precipitation amount) were used to investigate correlation coefficients among predictors and to evaluate their predictive ability when used in a multiple linear regression (MLR) downscaling model. The basic statistical characteristics of vorticity at 1,000-, 850- and 500-hPa levels, U-component of velocity at 1,000-hPa level, temperature at 2?m (T ₂) and wind direction at 1,000- and 500-hPa level of CGCM3 showed a larger difference with those of the NCEP reanalysis data. Therefore, those seven variables require high caution to be included as predictors in statistical downscaling models. Specific humidity at 1,000-, 850- and 500-hPa levels, geopotential height at 850- and 500-hPa levels and T ₂ were the most sensitive predictors for future climate conditions (i.e. A1B and A2 emission scenarios). Specific humidity and geopotential height at different levels and T ₂ were important explainable predictors for the daily temperatures. Mean sea level pressure, specific humidity, U and V components and divergence showed potential as predictors for daily precipitation. Spatial explained variance of MLRs between predictors of every different CGCM3 grid points and the four predictands showed large values at the CGCM3 grid points located near the observation sites, whereas relatively small values were shown at the CGCM3 grid points located more than 400?km from the sites. The explained variance of the downscaled predictands by predictors of three or four CGCM3 grid points located near the observation site produced 2–5% larger R-squares than those by predictors of the nearest grid point. The results illustrated that the use of predictors from more than one AOGCM grid points located near the observation site can increase the skill of the MLR downscaling models.     

Tectonic model of Egypt based on magnetic analysis

The main target of this work was to study the dynamics of the Earth’s crust for Egypt based on the magnetic survey. High-resolution land magnetic data were analyzed, combined with the results of GPS and seismic stress analyses. The constructed tectonic map shows that the N35°-N45°W trend of the structure (related to the Red Sea and Gulf of Suez tectonics) predominates along the Gulf of Suez, Red Sea, covering wide parts of the study area. The N45°-N65°E tectonic trend (related to the Syrian Arc tectonics), prevailing in the northern part of Egypt, is of the second rank. The Aqaba (N15°-25°E) and E-W trends prevail in the northern part and along the transition zone of stable/unstable shelves. The depth to the basement rocks ranges from the surface along the Red Sea and southern parts of Egypt to more than 4 km below sea level at the northern part of the study area.     

Shear behavior of sensitive marine clay–steel interfaces

Many soil–structure interaction problems require the knowledge of the shear resistance and behavior between the soil and construction materials. Although sensitive marine clay deposits are widely found in Canada (Leda clay) and many regions in the world (e.g., Scandinavia), and steel is a common construction material for many civil engineering structures, our understanding of the interface shear behavior between sensitive marine clay and steel is still limited. This paper presents the results of an experimental study on the interface shear behavior between Leda clay and steel. In this research, direct shear tests (DSTs) are conducted to investigate the interface shear strength parameters and behavior between Leda clay and steel, and the effect of several factors (e.g., steel surface roughness, properties of the Leda clay) on the interface shear behavior and parameters. All tests have been carried out with a standard DST apparatus at normal loads which range from 250 to 450 kPa. The results show that the Leda clay interface shear behavior can be significantly affected by the steel surface roughness, the Leda clay’s OCR, dry density, and salt content. The results presented in this paper will contribute to a more cost-effective design of geotechnical structures in Leda clay.     

Tenfold cross validation artificial neural network modeling of the settlement behavior of a stone column under a highway embankment

Construction of embankments in engineering structures on soft clay soils normally encounters problems related to excessive settlement issues. The conventional methods are inadequate to analyze and predict the surface settlement when the necessary parameters are difficult to determine in the field and in the laboratory. In this study, artificial neural network systems (ANNs) were used to predict settlement under embankment load using soft soil properties together with various geometric parameters as input for each stone column (SC) arrangement and embankment condition. Data from a highway project called Lebuhraya Pantai Timur2 in Terengganu, Malaysia, were investigated. The FEM package of Plaxis v8 program analysis was utilized. The actual angle of internal friction, spacing between SC, diameter of SC, length of SC, and height of embankment were used as the input parameters, and the settlement was used as the main output. Non cross validation (NCV) and tenfold cross validation (TFCV) were used to build the ANN model. The results of the TFCV model were more accurate than those of the NCV model. Comparisons made with the predictions of the Priebe model showed that the proposed TFCV model could provide better predictions than conventional methods.