Latitudinal and Longitudinal Variations of Earth’s Magnetic Force on Electrified Hydrometeors

Geomagnetism and Aeronomy - In this study, a hypothesis is proposed about the possible effect of Geomagnetic field (GMF) on the charge structure of a thundercloud based on Lorentz force equation...     

Experimental vulnerability curves for the residential buildings of Iran

Iran is one of the most seismically active countries of the world located on the Alpine-Himalayan earthquake belt. More than 180,000 people were killed due to earthquakes in Iran during the last five decades. Considering the fact that most Iranians live in masonry and non-engineered houses, having a comprehensive program for decreasing the vulnerability of society holds considerable importance. For this reason, loss estimation should be done before an earthquake strikes to prepare proper information for designing and selection of emergency plans and the retrofitting strategies prior to occurrence of earthquake. The loss estimation process consists of two principal steps of hazard analysis and vulnerability assessment. After identifying the earthquake hazard, the first step is to evaluate the vulnerability of residential buildings and lifelines and also the social and economic impacts of the earthquake scenarios. Among these, residential buildings have specific importance, because their destruction will disturb the daily life and result in casualties. Consequently, the vulnerability assessment of the buildings in Iran is important to identify the weak points in the built environment structure. The aim of this research is to prepare vulnerability curves for the residential buildings of Iran to provide a proper base for estimating probable damage features by future earthquakes. The estimation may contribute fundamentally for better seismic performance of Iranian societies. After a brief review of the vulnerability assessment methods in Iran and other countries, through the use of the European Macroseismic method, a model for evaluating the vulnerability of the Iranian buildings is proposed. This method allows the vulnerability assessment for numerous sets of buildings by defining the vulnerability curves for each building type based on the damage observations of previous earthquakes. For defining the vulnerability curves, a building typology classification is presented in this article, which is representative of Iranian building characteristics. The hazard is described in terms of the macroseismic intensity and the EMS-98 damage grades have been considered for classifying the physical damage to the buildings. The calculated vulnerability indexes and vulnerability curves show that for engineered houses there is not any notable difference between the vulnerability of Iranian and Risk-UE building types. For the non-engineered houses, the vulnerability index of brick and steel structures is less than the corresponding values of the other unreinforced masonry buildings of Iran. The vulnerability index of unreinforced and masonry buildings of Iran are larger than the values of the similar types in Risk-UE and so the Iranian buildings are more vulnerable in this regard.     

Comparison of univariate and multivariate geographically weighted regression for estimating air temperature over Iran

Station recording air temperature (T_a) has limited spatial coverage, especially in unpopulated areas. Since temperature can change greatly both spatially and temporally, stations data are often inadequate for meteorology and subsequently climatology studies. Time series of moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (T_s) and normalized difference vegetation index (NDVI) products, combined with digital elevation model (DEM), albedo from Era-Interim and meteorological data from 2006 to 2015, were used to estimate daily mean air temperature over Iran. Geographically weighted regression was applied to compare univariate and multivariate model accuracy. In the first model, which only interfered with land surface temperature (LST), the results indicate a weak performance with coefficient of determination up to 91% and RMSE of 1.08 to 2.9 °C. The mean accuracy of a four-variable model (which used LST, elevation, slope, NDVI) slightly increased (6.6% of the univariate model accuracy) when compared to univariate model. RMSE dropped by 19% of the first model. By addition albedo in the third model, the coefficient of determination increased significantly. This increase was 32% of the univariate model and 23.75% of the 4-variable model accuracy. The statistical comparison between the three models revealed that there is significant improvement in air estimation by applying the geographically weighted regression (GWR) method with interfering LST, NDVI, elevation, slope, and albedo with mean absolute RMSE of 0.62 °C and mean absolute R² of 0.99. In order to better illustrate the third model, t values were spatially mapped at 0.05 level.     

Structural characteristics of annual precipitation in Lake Urmia basin

Theoretical and Applied Climatology - Precipitation as the main process that brings evaporated water from the oceans to the land’s surface is a critical role player in Lake Urmia basin...     

Fully coupled elastoplastic hydro-mechanical analysis of unsaturated porous media using a meshfree method

This paper presents the first application of an advanced meshfree method, ie, the edge-based smoothed point interpolation method (ESPIM), in simulation of the coupled hydro-mechanical behaviour of unsaturated porous media. In the proposed technique, the problem domain is spatially discretised using a triangular background mesh, and the polynomial point interpolation method combined with a simple node selection scheme is adopted for creating nodal shape functions. Smoothing domains are formed on top of the background mesh, and a constant smoothed strain, created by applying the smoothing operation over the smoothing domains, is assigned to each smoothing domain. The deformation and flow models are developed based on the equilibrium equation of the mixture, and linear momentum and mass balance equations of the fluid phases, respectively. The effective stress approach is followed to account for the coupling between the flow and deformation models. Further coupling among the phases is captured through a hysteretic soil water retention model that evolves with changes in void ratio. An advanced elastoplastic constitutive model within the context of the bounding surface plasticity theory is employed for predicting the nonlinear behaviour of soil skeleton. Time discretisation is performed by adopting a three-point discretisation method with growing time steps to avoid temporal instabilities. A modified Newton-Raphson framework is designed for dealing with nonlinearities of the discretised system of equations. The performance of the numerical model is examined through a number of numerical examples. The state-of-the-art computational scheme developed is useful for simulation of geotechnical engineering problems involving unsaturated soils.     

Substructure and the cusp and fold relations

Gravitational lensing of a background source by a foreground galaxy lens occasionally produces four images of the source. The cusp and the fold relations impose conditions on the ratios of magnifications of these four-image lenses. In this theoretical investigation, we explore the sensitivity of these relations to the presence of substructure in the lens. Starting with a smooth lens potential, we add varying amounts of substructure, while keeping the source position fixed, and find that the fold relation is a more robust indicator of substructure than the cusp relation for the images. This robustness is independent of the detailed spatial distribution of the substructure, as well as of the ellipticity of the lensing potential and the presence of external shear.     

Nonlinear dynamic tests of a reinforced concrete frame building at different damage levels

This paper discusses the dynamic tests of a two-story infilled reinforced concrete (RC) frame building using an eccentric-mass shaker. The building, located in El Centro, CA, was substantially damaged prior to the tests due to the seismic activity in the area. During the testing sequence, five infill walls were removed to introduce additional damage states and to investigate the changes in the dynamic properties and the nonlinear response of the building to the induced excitations. The accelerations and displacements of the structure under the forced and ambient vibrations were recorded through an array of sensors, while lidar scans were obtained to document the damage. The test data provide insight into the nonlinear response of an actual building and the change of its resonant frequencies and operational shapes due to varying damage levels and changes of the excitation amplitude, frequency, and orientation.     

Time-frequency decomposition of seismic signals via quantum swarm evolutionary matching pursuit

Matching pursuit belongs to the category of spectral decomposition approaches that use a pre-defined discrete wavelet dictionary in order to decompose a signal adaptively. Although disengaged from windowing issues, matching point demands high computational costs as extraction of all local structure of signal requires a large size dictionary. Thus in order to find the best match wavelet, it is required to search the whole space. To reduce the computational cost of greedy matching pursuit, two artificial intelligence methods, (1) quantum inspired evolutionary algorithm and (2) particle swarm optimization, are introduced for two successive steps: (a) initial estimation and (b) optimization of wavelet parameters. We call this algorithm quantum swarm evolutionary matching pursuit. Quantum swarm evolutionary matching pursuit starts with a small colony of population at which each individual, is potentially a transformed form of a time-frequency atom. To attain maximum pursuit of the potential candidate wavelets with the residual, the colony members are adjusted in an evolutionary way. In addition, the quantum computing concepts such as quantum bit, quantum gate, and superposition of states are introduced into the method. The algorithm parameters such as social and cognitive learning factors, population size and global migration period are optimized using seismic signals. In applying matching pursuit to geophysical data, typically complex trace attributes are used for initial estimation of wavelet parameters, however, in this study it was shown that using complex trace attributes are sensitive to noisy data and would have lower rate of convergence. The algorithm performance over noisy signals, using non-orthogonal dictionaries are investigated and compared with other methods such as orthogonal matching pursuit. The results illustrate that quantum swarm evolutionary matching pursuit has the least sensitivity to noise and higher rate of convergence. Finally, the algorithm is applied to both modelled seismograms and real data for detection of low frequency anomalies to validate the findings.     

Earthquake-induced landslide prediction using back-propagation type artificial neural network: case study in northern Iran

Natural Hazards - Landslides can cause extensive damage, particularly those triggered by earthquakes. The current study used back propagation of an artificial neural network (ANN) to conduct risk...