Screw piles are widely used in supporting structures subjected to pullout forces, such as power towers and offshore structures, and this research investigates their performance in gypseous soil of medium relative density. The bearing capacity and displacement of a single screw pile model inserted in gypseous soil with various diameters (D = 20, 30, and 40) mm are examined in this study. The soil used in the testing had a gypsum content of 40% and the bedding soil had a relative density of 40%. To simulate the pullout testing in the lab, a physical model was manufactured with specific dimensions. Three steel screw piles with helix diameters of 20, 30, and 40 mm are used, with a total length of 500 mm. The helix is continuous over the

Gypseous soils are distributed in many regions in the world including Iraq, which cover more than (31%) of the surface area of the country. Existence of these soils, always with high gypsum content, caused difficult problems to the buildings and strategic projects due to dissolution and leaching of the gypsum caused by the action of water flow through soil mass. For the study, the gypseous soil was brought from Bahr Al-Najaf, Al-Najaf Governorate which is located in the middle of Iraq. The model pile was embedded in gypseous soil with 42% gypsum content. Compression axial model pile load tests have been carried out for model pile embedded in gypseous soil at initial degree of saturation of (7%) before and after soil satu

The construction of embankment for roadway interchange system at urban area is restricted due to the large geometry requirements, since the value of land required for such construction is high, and the area available is limited as compared to rural area. One of the optimum solutions to such problem is the earth reinforcement technique which requires a limited area for embankment construction. Gypseous soil from Al-Anbar governorate area was obtained and subjected to various physical and chemical analysis to determine it is properties. A laboratory model box of 50x50x25 cm was used as a representative embankment; soil has been compacted in five layers at maximum dry density (modified compaction) and an aluminum reinforcement strips we

The construction of embankment for roadway interchange system at urban area is restricted due to the large geometry requirements, since the value of land required for such construction is high, and the area available is limited as compared to rural area. One of the optimum solutions to such problem is the earth reinforcement technique which requires a limited area for embankment construction. Gypseous soil from Al-Anbar governorate area was obtained and subjected to various physical and chemical analysis to determine it is properties. A laboratory model box of 50x50x25 cm was used as a representative embankment; soil has been compacted in five layers at maximum dry density (modified compaction) and an aluminum reinforcement strips were i

In many oil fields only the BHC logs (borehole compensated sonic tool) are available to provide interval transit time (Δtp), the reciprocal of compressional wave velocity VP.

   To calculate the rock elastic or inelastic properties, to detect gas-bearing formations, the shear wave velocity VS is needed. Also VS is useful in fluid identification and matrix mineral identification.

   Because of the lack of wells with shear wave velocity data, so many empirical models have been developed to predict the shear wave velocity from compressional wave velocity. Some are mathematical models others used the multiple regression method and neural network technique.

   In this study a number of em

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

The aim of this paper is to design fast neural networks to approximate periodic functions, that is, design a fully connected networks contains links between all nodes in adjacent layers which can speed up the approximation times, reduce approximation failures, and increase possibility of obtaining the globally optimal approximation. We training suggested network by Levenberg-Marquardt training algorithm then speeding suggested networks by choosing most activation function (transfer function) which having a very fast convergence rate for reasonable size networks.             In all algorithms, the gradient of the performance function (energy function) is used to determine how to

Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability

This study aims to investigate the behavior and strength of self-compacted ferrocement slabs under punching shear load. Experimental results of thirteen square ferrocement slabs of 500×500 mm simply supported on all edges are presented.  The main parameters investigated include the volume fraction of reinforcement, slab thickness and size of load-bearing plate. The load deflection and cracking characteristics of the tested slabs are studied and compared.  The test results showed that the volume fraction of wire mesh has significant effect on both ultimate load and displacement. The increase of slab thickness leads to decrease in deflection values and increase in stiffness of slabs. Both ductility and stiffness increase as the

This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett