Evaluation of soil collapse potential in regional scale

Collapsible soils have considerable strength and stiffness in their dry natural state but settle dramatically when they become wet. This paper documents a low-cost, qualitative evaluation scheme using fuzzy set analysis to determine site collapsibility based on subjective knowledge of the geological, geotechnical, and environmental conditions and their uncertainty. For each category, factors or subcategories were defined in a decision tree based on relevant literature. Each category and subcategory was then weighted or rated using linguistic terms developed from expert assessment. The linguistic data or information obtained from the assessments was represented and processed using fuzzy sets. To calibrate the criteria, 87 collapse potential tests were performed on undisturbed soil samples gathered from 27 different locations throughout Iran, leading to the definition of a standard collapse potential fuzzy set. Finally, on the basis of the established criteria, a collapse potential map was prepared for a suburban area in the western part of the city of Kerman, Iran.     

Assessment of weathering processes effect on engineering properties of Alvand granitic rocks (west of Iran), based on weathering indices

The Alvand batholith is one of the largest plutonic bodies in the west of Iran. In this research, several physico-mechanical tests have been performed on granodiorite and porphiroid monzogranite consisting of five degrees of weathering in Hamedan area, west of Iran. Furthermore, weathering process of Alvand granitoid is studied by chemical analysis and petrographical studies. The results indicated that engineering properties of weathered granodiorite and monzogranite vary over the wide range depending on the degree of weathering. On the other hand, this research is focused on the assessment of relationship between weathering indices and uniaxial compressive strength. For this reason, some of the most important weathering indices are reviewed. It should be noted that, application of these chemical, engineering and petrographical indices are good quantitative indicators for describing the degree of weathering. Using these indices for the assessment of uniaxial compressive strength of granodiorite and monzogranite rocks, yields suitable and meaningful results.     

Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models – A case study

Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash–Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.     

Relationship Between Petrographic Characteristics and the Engineering Properties of Jurassic Sandstones, Hamedan, Iran

To study the relationship between engineering properties and petrographic characteristics, 20 rock samples were collected from Jurassic sandstones in the Hamedan region, western Iran. The specimens were tested to determine uniaxial compressive strength, point load strength index, tangent modulus, porosity, and dry and saturated unit weights. Samples were also subjected to petrographic examination, which included the observation of 11 parameters and modal analysis. Based on the results of a statistical analysis, polynomial prediction equations were developed to estimate physical and mechanical properties from petrographic characteristics. The results show that textural characteristics are more important than mineral compositions for predicting engineering characteristics. The packing density, packing proximity and grain shape are the petrographic properties that significantly affect the engineering properties of samples. Multivariate linear regression analysis was performed, employing four steps comprising various combinations of petrographic characteristics for each engineering parameter. The optimal equation, along with the relevant combination of petrographic characteristics for estimating the engineering properties of the rock samples is proposed.     

Sorkhe‐Dizaj Iron Oxide–Apatite Ore Deposit in the Cenozoic Alborz‐Azarbaijan Magmatic Belt,NW Iran

The Sorkhe‐Dizaj iron oxide–apatite deposit in the Cenozoic Alborz‐Azarbaijan magmatic belt, NW Iran, is hosted mainly by a Late Eocene to Oligocene quartz‐monzonitic body, and subordinately in the Eocene volcanic and volcanoclastic sequences. The Sorkhe‐Dizaj intrusive body is an I‐type granitoid of the calc‐alkaline series. Mineralization is associated with actinolization, K‐feldspar, sericitic, propylitic, and tourmaline alteration types. The orebodies are massive, banded, stockwork, and breccia in shape and occur mainly along the fault zones within the quartz‐monzonitic intrusion, volcanic, and volcanoclastic rocks. Ore minerals dominantly comprise magnetite, apatite, and monazite, as well as minor amounts of chalcopyrite, bornite, and pyrite. Four major paragenetic stages are discriminated in the mineralization including early, oxide, sulfide, and late stage. The Sorkhe‐Dizaj deposit is similar in the aspects of host rock lithology, alteration, and mineralogy to the Kiruna‐type deposits associated with minor Cu sulfide minerals. Spatial and temporal association of the mineralization with the Late Eocene–Early Oligocene quartz‐monzonite intrusive body suggests that the ore fluid was probably related to magmatic activity.     

Automatic Determination of Number of Homogenous Regions in SAR Images Utilizing Splitting and Merging Based on a Reversible Jump MCMC Algorithm

This paper presents an algorithm dealing with initial segmentation of speckled Synthetic Aperture Radar (SAR) intensity images in order to automatically determine the number of homogeneous regions. Taking this problem into account, segmentation procedure utilizing splitting and merging is designed, iteratively. The proposed approach is based upon Bayesian inference, a maximum likelihood gamma distribution parameter estimator, and a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. By using of image splitting operation, SAR image is partitioned into finite regions iteratively, until all individual regions are coherent. Then each region is assigned a unique label to indicate the class to which the homogeneous region belongs. The intensities of pixels in each coherent region are assumed to satisfy identical and independent gamma distribution. Then an RJMCMC scheme is designed to simulate the posterior distribution in order to estimate the number of components and delineate an initial segmentation. Thus, the main purpose of this research is to define the number of homogeneous regions rather than a perfect segmentation, i.e. model outputs can be served for unsupervised segmentation methodologies as prior information. The results obtained from Radarsat-1/2 of SAR intensity images show that the proposed algorithm is both capable and reliable in defining the accurate number of homogeneous regions in a wide variety of SAR intensity images, comprising a high level of speckle noise.