The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial voids ratio. Multi-layer perceptron training by the backpropagation algorithm was used in creating the network. It was found that both models can predict shear strength parameters for gypseous soils with good reliability. Sensitivity analysis of the first model indicated that dry unit weight and plasticity index have the most significant effect on the predicted cohesion. While in the second model, the results indicated that the gypsum content and plasticity index have the most significant effect on the predicted angle of internal friction.
Modern emerged technologies impose development and fabrication of miniatur-ized parts and devices in the micro- and nano-scale. Producing micro- and nano-featured structures requires nonconventional machining processes where con-ventional machining processes such as grinding, milling and eroding have failed. New emerging processes, such laser machining processes, are still fraught with almost invincible processes. Micro-/nano-machining are the pro-cesses of producing parts, microsystems or features at a scale of a few microm-eters and less than one hundred nanometers, respectively. Precise cutting and clean material removal accompanied with a negligible heat affected zone (HAZ), which are usually the characteristics of laser ablation, have
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... Show MoreIn this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and
... Show MoreIn the present research, the chemical washing method has been selected using three chelating agents: citric acid, acetic acid and Ethylene Diamine Tetraacetic Acid (EDTA) to remove 137Cs from two different contaminated soil samples were classified as fine and coarse grained. The factors that affecting removal efficiency such as type of soil, mixing ratio and molarity have been investigated. The results revealed that no correlation relation was found between removal efficiency and the studied factors. The results also showed that conventional chemical washing method was not effective in removing 137Cs and that there are further studies still need to achieve this objective.
Measurements of radon gas concentrations were carried out for 12 soil samples at 3 sampling depths (surface, 5 cm and 10 cm) collected from (4) locations in south Baghdad suburbs (Bu'aitha) using solid state nuclear track detector CR-39 and sealed can technique. Radon concentrations for surface samples were ranged from 402.2 to 1538.4 Bq.m-3 with an average 994.4 Bq.m-3. Whereas, radon concentration was ranged from 813.1to 2050.4 Bq.m-3 and from 1309.8 to 4626. 1Bq.m-3 with an average values of 1359.8 Bq.m-3 and 2338.3 Bq.m-3 for 5 cm and 10 cm depths respectively. Maximum radon level was found at the location near to the river (site S4) while the minimum radon level was f
... Show MoreSoil stabilization with liquid asphalt is considered as a sustainable step towards roadway construction on problematic subgrade soil, there are no requirements to import good quality materials or to implement energy consumption, but to mix the readily available soil with liquid asphalt through the cold mix technique. In this work, collapsible soil obtained from Nasiriya was mixed with asphalt emulsion, lime, and combinations of lime and asphalt emulsion (combined stabilization) and tested in the laboratory for California bearing ratio in dry and soaked conditions. Field trial sections have been prepared with the same combinations and subjected to plate bearing test. The influence of combined stabilization on the structural properties in ter
... Show MoreIn this experimental study, the use of stone powder as a stabilizer to the clayey soil studied. Tests of Atterberg limits, compaction, fall cone (FCT), Laboratory vane shear (LVT), and expansion index (EI) were carried out on soil-stone powder mixtures with fixed ratios of stone powder (0%, 5%, 10%, 15%, and 20%) by the dry weight. Results indicated that the undrained shear strength obtained from FCT and LVT increased at all the admixture ratios, and the expansion index reduced with the increase of the stone powder.
Positive and negative parity states for 114Te have been studied applying the vibration al limit U(5) of Interacting boson model (IBM- 1 ) . The present results have shown their good agreement with experimental data in addition to the determination of the spin/parity of new energy levels are not assigned experimentally as the levels 0+2 and 5+1 and the levels 3"1 and 5-1 . Then back propagation multiLayer neural network used for positive and negative parity states for 114Te and shown their membership to the Vibration limit U(5) the network implemented by MATLAB system.