Shear Strength Parameters of Improved Peat by Chemical Stabilizer

The present research aimed to discuss the applicability of cationic grouts in geotechnical engineering. The effects of several cationic stabilizers such as monovalent (sodium silicate), divalent (calcium oxide and calcium chloride), and trivalent (aluminum hydroxide) were investigated on shear strength improvement of tropical peat samples. The unconfined compressive strength (UCS) tests were performed after the time frame of 7, 21, and 30 days as curing time, respectively. Apart from the physicochemical characteristics of the stabilized peat, scanning electron microscopy and energy-dispersive X-ray spectroscopy tests were also carried out to study the ongoing microstructural changes. It is to be noted that the shear strength values for peat samples rose to 8, 6, 6, and 4 % of sodium silicate, calcium oxide, calcium chloride, and aluminum hydroxide, respectively. The highest observed UCS outcome is the one taken from the calcium oxide where the UCS of treated peat after 30-day curing time increased to 76 kPa. The strength changes resulted from the various cationic stabilizers can best be explained via the consideration within the mineralogical composition as well as those physicochemical changes happening in the peat.     

Prediction of drought in dry lands through feedforward artificial neural network abilities

Drought is one of the most important natural hazards in Iran. It is especially more prevalent in arid and hyper arid regions where there are serious limitations in regard to providing sufficient water resources. On the other hand, drought modeling and particularly its prediction can play important role in water resources management under conditions of lack of sufficient water resources. Therefore, in this study, drought prediction in a hyper arid location of Iran (Ardakan region) has been surveyed based on the abilities of artificial neural. Standardized Precipitation Index (SPI) in different time scales (3, 6, 9, 12, and 24 monthly time series) computed based on the data gathered from four rain gauge stations. After evaluation and testing of different artificial neural networks (ANN) structures, gradient descent back propagation (traingd) network showed higher abilities than others. Then, the predictions of SPI time series with different monthly lag times (1:12 months) were tested. Generally, drought prediction by ANNs in the Ardakan region has shown considerable results with the correlation coefficient (R) more than 0.79 and in the most cases and it rises more than 0.90, which indicates the ANN’s ability of drought prediction.     

Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions

Arabian Journal of Geosciences - Prediction of monthly discharge volume is important for reservoir management and evaluation of drinking-water supplies. Also, it is very essential in arid and...     

http://www.sciencedirect.com/science/article/pii/S167498711100137X

This study presents promising variants of genetic programming (GP),namely linear genetic programming (LGP) and multi expression programming (MEP) to evaluate the liquefaction resistance of sandy soils....     

Iranian earthquakes, a uniform catalog with moment magnitudes

A uniform earthquake catalog is an essential tool in any seismic hazard analysis. In this study, an earthquake catalog of Iran and adjacent areas was compiled, using international and national databanks. The following priorities were applied in selecting magnitude and earthquake location: (a) local catalogs were given higher priority for establishing the location of an earthquake and (b) global catalogs were preferred for determining earthquake magnitudes. Earthquakes that have occurred within the bounds between 23–42° N and 42–65° E, with a magnitude range of M _W 3.5–7.9, from the third millennium BC until April 2010 were included. In an effort to avoid the “boundary effect,” since the newly compiled catalog will be mainly used for seismic hazard assessment, the study area includes the areas adjacent to Iran. The standardization of the catalog in terms of magnitude was achieved by the conversion of all types of magnitude into moment magnitude, M _W, by using the orthogonal regression technique. In the newly compiled catalog, all aftershocks were detected, based on the procedure described by Gardner and Knopoff (Bull Seismol Soc Am 64:1363–1367, 1974). The seismicity parameters were calculated for the six main tectonic seismic zones of Iran, i.e., the Zagros Mountain Range, the Alborz Mountain Range, Central Iran, Kope Dagh, Azerbaijan, and Makran.     

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study

The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling.     

A micromechanical study of rolling and sliding contacts in assemblies of oval granules

The evolution of the microstructure of an assembly of cohesionless granular materials with associated pores, which carry the overall applied stresses through frictional contacts is a complex phenomenon. The macroscopic flow of such materials take place by the virtue of the relative rolling and sliding of the grains on the micro‐scale. A new discrete element method for biaxial compression simulations of random assemblies of oval particles with mixed sizes is introduced. During the course of deformation, the new positions of the grains are determined by employing the static equilibrium equations. A key aspect of the method is that, it is formulated for ellipse cross‐sectional particles, hence desirable inherent anisotropies are possible. A robust algorithm for the determination of the contact points between neighbouring grains is given. Employing the present methodology, many aspects of the behaviour of two‐dimensional assemblies of oval cross‐sectional rods have been successfully addressed. The effects of initial void ratio, interparticle friction angle, aspect ratio, and bedding angle on the rolling and sliding contacts are examined. The distribution of normals to the rolling and sliding contacts have different patterns and are concentrated along directions, which are approximately perpendicular to one another. On the other hand, the distribution of all contact normals (combined rolling and sliding) are close to that of rolling contacts, which confirm that rolling is the dominant mechanism. This phenomenon becomes more pronounced for higher intergranular friction angle. Characteristics of the rolling and sliding contacts are also discussed in the context of the force angle, which is the inclination of contact force with respect to the contact normal. Copyright © 2003 John Wiley & Sons, Ltd.     

A late Pleistocene long pollen record from Lake Urmia, NW Iran

A palynological study based on two 100-m long cores from Lake Urmia in northwestern Iran provides a vegetation record spanning 200 ka, the longest pollen record for the continental interior of the Near East. During both penultimate and last glaciations, a steppe of Artemisia and Poaceae dominated the upland vegetation with a high proportion of Chenopodiaceae in both upland and lowland saline ecosystems. While Juniperus and deciduous Quercus trees were extremely rare and restricted to some refugia, Hippophaë rhamnoides constituted an important phanerophyte, particularly during the late last glacial period. A pronounced expansion in Ephedra shrub-steppe occurred at the end of the penultimate late-glacial period but was followed by extreme aridity that favoured an Artemisia steppe. Very high lake levels, registered by both pollen and sedimentary markers, occurred during the middle of the last glaciation and late part of the penultimate glaciation. The late-glacial to early Holocene transition is represented by a succession of Hippophaë, Ephedra, Betula, Pistacia and finally Juniperus and Quercus. The last interglacial period (Eemian), slightly warmer and moister than the Holocene, was followed by two interstadial phases similar in pattern to those recorded in the marine isotope record and southern European pollen sequences.     

Vector ionosphere modeling by vector spherical Slepian base functions

GPS Solutions - Ionosphere and its spatial and temporal gradients play an important role in positioning and navigation using global navigation satellite systems (GNSS). This study intends to...     

Sensitivity analysis and accuracy assessment of the land transformation model using cellular automata

This study evaluates the effects of cellular automata (CA) with different neighborhood sizes on the predictive performance of the Land Transformation Model (LTM). Landsat images were used to extract urban footprints and the driving forces behind urban growth seen for the metropolitan areas of Tehran and Isfahan in Iran. LTM, which uses a back-propagation neural network, was applied to investigate the relationships between urban growth and the associated drivers, and to create the transition probability map. To simulate urban growth, the following two approaches were implemented: (a) the LTM using a top-down approach for cell allocation grounding on the highest values in the transition probability map and (b) a CA with varying spatial neighborhood sizes. The results show that using the LTM-CA approach increases the accuracy of the simulated land use maps when compared with the use of the LTM top-down approach. In particular, the LTM-CA with a 7 × 7 neighborhood size performed well and improved the accuracy. The level of agreement between simulated and actual urban growth increased from 58% to 61% for Tehran and from 39% to 43% for Isfahan. In conclusion, even though the LTM-CA outperforms the LTM with a top-down approach, more studies have to be carried out within other geographical settings to better evaluate the effect of CA on the allocation phase of the urban growth simulation.