The study is devoted to both static and earthquake response analysis of retaining structures acted upon by lateral earth pressure. Two main approaches were implemented in the analysis, namely, the Mononobe-Okabe analytical method and the numerical Finite element procedure as provided in the ready software ABAQUS with explicit dynamic method. A basic case study considered in the present work is the bridge approach retaining walls as a part of AL-Jadiriya bridge intersection to obtain the effects of the backfill and the ground water on the retaining wall response including displacement of the retaining structure in addition to the behavior of the fill material. Parametric studies were carried out to evaluate the effects of several factors

This paper presents a novel idea as it investigates the rescue effect of the prey with fluctuation effect for the first time to propose a modified predator-prey model that forms a non-autonomous model. However, the approximation method is utilized to convert the non-autonomous model to an autonomous one by simplifying the mathematical analysis and following the dynamical behaviors. Some theoretical properties of the proposed autonomous model like the boundedness, stability, and Kolmogorov conditions are studied. This paper's analytical results demonstrate that the dynamic behaviors are globally stable and that the rescue effect improves the likelihood of coexistence compared to when there is no rescue impact. Furthermore, numerical simul

Due to wind wave actions, ships impacts, high-speed vehicles and others resources of loading, structures such as high buildings rise bridge and electric transmission towers undergo significant coupled moment loads. In this study, the effect of increasing the value of coupled moment and increasing the rigidity of raft footing on the horizontal deflection by using 3-D finite element using ABAQUS program. The results showed that the increasing the coupled moment value leads to an increase in lateral deflection and increase in the rotational angle (α◦). The rotational angle increases from (0.014, 0.15 to 0.19) at coupled moment (120 kN.m), (0.29, 0.31 and 0.49) at coupled moment (240 kN.m) and (0.57, 0.63 and 1.03) at cou

In this paper, a shallow foundation (strip footing), 1 m in width is assumed to be constructed on fully saturated and partially saturated Iraqi soils, and analyzed by finite element method. A procedure is proposed to define the H – modulus function from the soil water characteristic curve which is measured by the filter paper method. Fitting methods are applied through the program (SoilVision). Then, the soil water characteristic curve is converted to relation correlating the void ratio and matric suction. The slope of the latter relation can be used to define the H – modulus function. The finite element programs SIGMA/W and SEEP/W are then used in the analysis. Eight nodded isoparametric quadrilateral elements are used for modeling

In structural construction fields, reducing the overall self-weight of the structure is considered a primary objective and substantial challenge in the civil engineering field, particularly in earthquake-affected buildings and tall buildings. Different techniques were implemented to attain this goal; one of them is setting voids in a specific position through the structure, just like a voided slab or BubbleDeck slab. The main objective of this research is to study the structural behavior of BubbleDeck reinforced concrete slabs under the effect of static uniformly distributed load. The experimental program involved testing five fixed-end supported two-way solid and BubbleDeck slabs of dimensions 2500×2500×200 mm. The considered par

Background: Mitral regurgitation (MR) is the most commonly encountered valve lesion in modern clinical practice. Severe mitral regurgitation may cause systolic dysfunction. Left ventricular ejection fraction may not be an accurate measurement of LV function in patients with mitral insufficiency. Myocardial performance index (MPI) is a simple non invasive measure of myocardial function. Methods: The study involved 50 patients with valvular mitral regurgitation and 50 healthy subjects as a control group. Transthoracic echocardiography was carried out for all patients and control group. The echocardiographic measurements included left ventricular end diastolic and end systolic dimensions, left atrial diameter, ejection fraction (EF), and myoca

Optimum perforation location selection is an important study to improve well production and hence in the reservoir development process, especially for unconventional high-pressure formations such as the formations under study. Reservoir geomechanics is one of the key factors to find optimal perforation location. This study aims to detect optimum perforation location by investigating the changes in geomechanical properties and wellbore stress for high-pressure formations and studying the difference in different stress type behaviors between normal and abnormal formations. The calculations are achieved by building one-dimensional mechanical earth model using the data of four deep abnormal wells located in Southern Iraqi oil fields. The magni

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i