Correlation of lineaments and groundwater quality in Dasht-e-Arjan Fars,SW of Iran

The NE-oriented Dasht-e-Arjan graben is located 65 km west of Shiraz and has resulted from the active Kare-e-Bas fault segmentations. This extensional graben bounded by two fault system east-Arjan and west-Arjan to the Shahneshin and Salamati anticline. In these study using Landsat 7 ETM images with resolution 2.5 m and directional filtering in the four azimuths and semi-automatic technique for linear structure in the study area. Using the obtained data from extracted lineaments, the rose diagrams of the main strike lineaments are well confirm with field measurements of faults with N56° ± 4°E direction. The structural lineaments of the study area show that the Dasht-e-Arjan area is underlain by the limestone, sandstone, and marl. LANDSAT imagery of the area has been analyzed and interpreted in order to determine the lineament and groundwater quality across the area. The fracture is structurally controlled and mostly influences both the groundwater and surface water pollution and flow directions in the Dasht-e-Arjan. Using visual interpretation, determining the lineaments on the satellite image is very difficult and subjective, and it requires an experienced interpreter. In this study, the lineament analysis is undertaken to examine the orientation of the lineament, the relationship between lineaments and tectonic features and groundwater quality. Lineament density maps show that the lineament density is high around areas. Areas having high lineament density represent areas with relatively high groundwater pollution. Field observations agreed with the results from the analysis of the imagery.     

A simple two-step method for spatio-temporal design-based balanced sampling

We introduce a two-step method to perform spatio-temporal balanced sampling in a design-based approach. For populations with spatio-temporal trends and with anisotropic effects in the variable of interest, the prediction can be further improved by selecting samples that are well spread over the entire population in space and time. We control the spread of the sample over the population by using the volume of the corresponding three-dimensional Voronoi tessellation. Indeed, spatio-temporal design-based balanced sampling is even more efficient under the presence of a trend and anisotropic effects. We present an intensive simulation study comparing our method to other available methods for spatio-temporal sampling. Finally, we analyze real data by sampling from a population of temperature stations over six European countries.     

Development of a new connection for precast concrete walls subjected to cyclic loading

The Industrialized Building System (IBS) was recently introduced to minimize the time and cost of project construction. Accordingly, ensuring the integration of the connection of precast components in IBS structures is an important factor that ensures stability of buildings subjected to dynamic loads from earthquakes, vehicles, and machineries. However, structural engineers still lack knowledge on the proper connection and detailed joints of IBS structure construction. Therefore, this study proposes a special precast concrete wall-to-wall connection system for dynamic loads that resists multidirectional imposed loads and reduces vibration effects (PI2014701723). This system is designed to connect two adjacent precast wall panels by using two steel U-shaped channels (i.e., male and female joints). During casting, each joint is adapted for incorporation into a respective wall panel after considering the following conditions: one side of the steel channel opens into the thickness face of the panel; a U-shaped rubber is implemented between the two channels to dissipate the vibration effect; and bolts and nuts are used to create an extension between the two U-shaped male and female steel channels. The developed finite element model of the precast wall is subjected to cyclic loads to evaluate the performance of the proposed connection during an imposed dynamic load. Connection performance is then compared with conventional connections based on the energy dissipation, stress, deformation, and concrete damage in the plastic range. The proposed precast connection is capable of exceeding the energy absorption of precast walls subjected to dynamic load, thereby improving its resistance behavior in all principal directions.     

Aquifer vulnerability assessment using GIS and fuzzy system: a case study in Tehran–Karaj aquifer,Iran

Aquifer vulnerability assessment techniques have been developed to predict which areas are more likely than others to become contaminated as a result of activities at or near the land surface. This research focuses on the evaluation of groundwater vulnerability to pollution in an urban area. Among several assessment methods, DRASTIC has been selected for this study. ArcGIS has been used to overlay and calculate different layers and obtain the vulnerability map. In order to show the importance of fuzzy algorithms in classification, both Boolean and fuzzy algorithms were used and compared. The fuzzy algorithm could recognize the areas with low and negligible vulnerability potentials whereas the Boolean model classified them as moderate. Two sensitivity tests, the map removal sensitivity analyses and single-parameter sensitivity analysis, were performed to show the importance of each parameter in the index calculation.     

Capability of vis-NIR spectroscopy and Landsat 8 spectral data to predict soil heavy metals in polluted agricultural land (Iran)

Heavy metals are toxic elements that have hazardous effect on the environment. They cause soil pollution as a result of their toxicity, potential reactivity, and mobility in soils. There are so many methods for the measurement of heavy metal concentrations in soils and aquatic systems. The traditional methods used for detecting heavy metal distribution in soil involve laboratory analysis and raster sampling. Both of them are expensive and time-consuming for large areas. Remote sensing techniques are used for obtaining the earth’s surface information, and these techniques have been used in the investigations of heavy metal distributions in preliminary analysis of soils as a rapid method. Today, near-infrared reflectance spectroscopy (NIRS) of soil characteristics has been of interest as a significant object. The present investigation is focused on the detection of heavy metals in contaminated soils by the application of reflectance spectroscopy in the spectral range of 350 to 2500 nm. This study also discusses the circumstances of the applied current methods for the detection and estimation of arsenic (As), cadmium (Cd), nickel (Ni), and lead (Pb) in contaminated agricultural soils. In the first part of laboratory spectroscopy, estimations were done using heavy metal reflectance spectroscopy and partial least square regression (PLSR) approaches, while in the second part, the heavy metal estimations were done using soil organic carbon reflectance spectroscopy through the PLSR approaches. Similar to the tasks above, estimations of As, Cd, Ni, and Pb by using Landsat 8 images were done in the forms of direct and indirect methods and the distribution of heavy metals in the study area was determined. Finally, the results obtained using direct and indirect methods were compared with the wet chemical measurements of heavy metals and organic carbon. It was found that although the direct detection of heavy metals using the images of Landsat 8 produced more accurate results than the indirect detections, the results obtained from laboratory spectroscopy corresponded more with the results from atomic adsorption spectroscopy. On the other hand, based on the fact that the soil has a complex content, the use of nonlinear methods, such as artificial neural networks in predicting soil heavy metal contents, could be regarded as a trusted method.     

Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation

An accurate estimation of flow using different models is an issue for water resource researchers. In this study, support vector regression (SVR) and gene expression programming (GEP) models in daily and monthly scale were used in order to simulate Gamasiyab River flow in Nahavand, Iran. The results showed that although the performance of models in daily scale was acceptable and the result of SVR model was a little better, their performance in the daily scale was really better than the monthly scale. Therefore, wavelet transform was used and the main signal of every input was decomposed. Then, by using principal component analysis method, important sub-signals were recognized and used as inputs for the SVR and GEP models to produce wavelet-support vector regression (WSVR) and wavelet-gene expression programming. The results showed that the performance of WSVR was better than the SVR in such a way that the combination of SVR with wavelet could improve the determination coefficient of the model up to 3% and 18% for daily and monthly scales, respectively. Totally, it can be said that the combination of wavelet with SVR is a suitable tool for the prediction of Gamasiyab River flow in both daily and monthly scales.     

Treatment of Synthetic Olefin Plant Wastewater at Various Salt Concentrations in a Membrane Bioreactor

The objective of this study was to investigate the effect of salt concentration on performance of a membrane bioreactor (MBR) for treating an olefin plant wastewater. For this purpose, a lab‐scale submerged MBR with a flat‐sheet ultrafiltration membrane was used for treatment of synthetic wastewater according to oxidation and neutralization unit of olefin plant. The synthetic wastewater was adjusted to have 500 mg/L chemical oxygen demand (COD). Trials on different concentrations of sodium sulfate (Na₂SO₄) (0–20 000 ppm) in the feed were conducted under aerobic conditions in the MBR. The results showed that increasing the salt concentrations causes an increase in the effluent COD, phenol, and oil concentrations. These results are due to reduction of the membrane filtration efficiency and also decline in the microbial activity that it is indicated by decreasing the sOUR in MBR. But in all the trials, the effluent COD and oil concentration was well within the local discharge limit of 100 and 10 mg/L, respectively. These results indicate that the MBR system is highly efficient for treating the olefin plant wastewater, and although high salt concentrations decreased organic contaminant removal rates in the MBR, the effluent still met the discharge limits for treating the olefin plant wastewater.     

Development of a New Empirical Relation to Assess P-wave Velocity Anisotropy of Rocks

Geotechnical and Geological Engineering - Several physical parameters and anisotropy related to rock textural arrangements, schistosity and weakness planes such as cracks and joints affect the...