Groundwater risk assessment based on optimization framework using DRASTIC method

Due to anthropogenic influences and large amounts of pollutant released into the groundwater, it is vital to investigate groundwater quality and to characterize susceptible areas to contamination. In this paper, a new optimization-based methodology is proposed for determining groundwater risk using DRASTIC model based on genetic algorithm optimization model and Wilcoxon test. The correlation coefficient between DRASTIC/modified DRASTIC indices and nitrate concentrations in monitoring wells is used as a criteria for evaluating the efficiency of the proposed models. In this regard, because of the unsatisfactory original DRASTIC’s result, sensitivity analysis, genetic algorithm (GA), and Wilcoxon test (1945) are carried out to tackle the subjectivity associated with the original DRASTIC model and obtain better and reliable results. The results indicate that application of Wilcoxon test and GA optimization outperforms the others. Consequently, the correlation coefficient increased remarkably as compared to the original DRASTIC model (from 0.57 to 0.82). The proposed optimization process is adaptable to be applied in different case studies; mainly since it has the ability to optimize the weights of the model based on hydrogeological characteristics of the aquifer. Finally, the risk maps of the models are prepared using ArcGIS® to determine the most vulnerable areas.     

Estimating width of the stable channels using multivariable mathematical models

Predicting behavior and the geometry of the channels and alluvial rivers in which the erosion and sediment transport are in equilibrium is one of the most important topics in river engineering. Various researchers have proposed empirical equations to estimate stable river width (W). In this research, empirical equations were examined and tested with a comprehensive available data set consisting of 1644 points collected from 29 stable rivers in various parts of the world. The data set covers a wide range of flow conditions, river geometry, and bed sediments. This data set is classified in two groups (W < 600 m and W ≥ 600 m) for presenting the new models. The new linear and nonlinear multivariable equations were fitted to these two groups, and the best models were selected by preliminary tests and diagnostic determined for each group. The determination coefficient of these models ranged from 0.87 to 0.96. The results show that the models presented in this paper are more accurate with respect to the previously presented models. In the second part, “Artificial neural networks,” perceptron was used and a new methodology for estimating stable channel width was developed. Comparison of the statistical methods presented in this paper and the results of perceptron neural network revealed preferential recent method.     

Influence of size of granulated rubber on bearing capacity of fine-grained sand

Nowadays, in most of the advanced and developing countries, waste tires have caused serious environmental problems such as fire and environmental contamination. For reusing them in an appropriate and beneficial way, waste tires have been utilized as a lightweight fill material in geotechnical engineering applications such as highway embankments. In this study, Babolsar fine-grained sand and granulated rubber with sizes in the ranges of 1 to 4, 1 to 9, and 4 to 9 mm were used. A series of model footing tests on reinforced sand with different sizes of granulated rubber were carried out. According to the results, 4- to 9-mm granulated rubber had the highest effect on enhancement of bearing capacity and reduction of fine-grained sand settlement. The results showed that sand-granulated rubber mixtures with granulated rubber in the range of 4 to 9 mm and content of 10% by weight of mixture can increase the bearing capacity of sand up to 50%. In addition, for this mixture, a series of laboratory tests were conducted to determine the optimum width and depth of the reinforcement layer consisting of sand-granulated rubber mixture. The results indicate that the optimum width and the most effective depth of this mixture are 5B and 1B, respectively (where B is the footing width).     

A correlation between P-wave velocity and Schmidt hardness with mechanical properties of travertine building stones

As known, P-wave velocity and Schmidt hardness are non-destructive tests, which have been used for many years in geological, geotechnical, and civil engineering as an index tests for a quick assessment of rocks mechanical properties due to its rapidity and easiness, and non-destructiveness. The purpose of this study is to investigate the correlation between P-wave velocity and Schmidt hardness with some of mechanical properties of travertine building stones by empirical equations. Moreover, we have compared the accuracy of P-wave velocity and Schmidt hardness to estimate the mechanical properties of rocks. For this purpose, 15 types of travertine have been collected from various quarries of Iran and tested. The tests include the determination of P-wave velocity and Schmidt hardness, and mechanical properties include the unconfined compressive strength, Brazilian tensile strength, and point load strength. Using data analysis, empirical equations have been developed for estimating the mechanical properties from P-wave velocity and Schmidt hardness. To check the validity of the empirical equations, a t test was performed, which confirmed the validity of the proposed empirical equations. Moreover, the results show that P-wave velocity appears to be more reliable than the Schmidt hardness for estimating the mechanical properties. Consequently, we propose empirical equations avoiding from cumbersome and time consuming tests for determining the mechanical properties of rocks.     

Flow units delineation of multiple hydrocarbon reservoirs using hydraulic zonation technique via cluster analysis algorithm,Zeit Bay Field,Gulf of Suez,Egypt

Investigation of hydraulic units in drilled wells through multiple reservoirs in Zeit Bay Field is the main target of this paper. Karim Limestone and Nubian Sandstone are target reservoirs in this study. Hydraulic zones provide a fundamental unit for subsequent reservoir characterization and simulation and also prove to be helpful in defining future well completion and work over practices. Hydraulic zonation by means of cluster analysis has been utilized to achieve this target. Hierarchical cluster analysis, elbow method, and k-means clustering are the main steps of this analysis. Petrophysical properties obtained from cores are utilized to perform this zonation. These properties are permeability and porosity data. Results have been validated by comparing the geological behavior of each zone against the measured permeability curve. Finally through this zonation, flow units which have the potential to extract fluids are determined more accurately. Petroleum geologists and engineers are more interested in these results. The cluster analysis flow suggesting in this paper is cost-effective due to lack of need for any commercial software.