Misan Journal of Engineering Sciences https://www.uomisan.edu.iq/eng/mjes/index.php/eng en-US [email protected] (Editor-in-manger \ Prof. Dr. Ahmed Kadhim Alshara) [email protected] (Technical Editor\Assist. Prof. Dr. Mustafa Al-Bazoon) Sun, 07 Jul 2024 00:00:00 +0000 OJS 3.3.0.17 http://blogs.law.harvard.edu/tech/rss 60 Quick Estimation of Seepage Discharge and Water Level Height Out Of the Central Core in the Zoned Earth-fill Dam https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/69 <p>This paper relates to the investigation of the amount of seepage discharge and the water level height out of the central core in the zoned earth-fill dam using the finite element procedure by Geo Studio-SEEP/W software. The investigation models were carried out in two shapes of the central core, the rectangular and the wedge shapes, by 200 miscellaneous models. For each model, seepage discharge and height of water level out were determined. Investigation results were examined using the artificial neural network technique (ANN) to demonstrate the influence weight of each independent variable (geometry and characteristics) on the output-dependent variables results using the statistical software SPSS. Core material hydraulic conductivity had the highest impact on seepage discharge and water level height while dam upstream slope had the least impact on them. Two statistical empirical equations were developed using multiple nonlinear regression for both the seepage discharge and the height of water level out using the SPSS software. The recommended equations were also verified by comparing their results with the results of 25% of the models analyzed by Geo Studio software. They showed great agreement that the correlation coefficient (r<sup>2</sup>) was 97.3% for the seepage discharge equation and 96.6% for the height of the water level-out equation.</p> Wissam Kidder, Shamil Behaya Copyright (c) 2024 Misan Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0 https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/69 Sun, 07 Jul 2024 00:00:00 +0000 Design of Structures Subjected to Blast Loads: Analysis and Design Review https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/70 <p>When designing structures to withstand explosions, the main goals are to minimize the number and extent of occupant injuries and to reduce the chance of catastrophic damage to structures. Although there is uncertainty in the source, extent, and location of explosions, the assessment of blast loading and structural performance is important when designing blast-resistant structures. This study is a review of the literature on the prediction of blast loads, structural modeling and analysis, and design criteria for structures to resist explosions. The paper provides in one concise document the general guidelines, references, and tools that structural engineers and researchers need to analyze and design structures subjected to blast loading. References on the topics discussed in this work are provided for more detail.</p> Mustafa Al-Bazoon, Jasbir Arora Copyright (c) 2024 Misan Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0 https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/70 Sun, 07 Jul 2024 00:00:00 +0000 Predicting Solar Power Generation Utilized in Iraq Power Grid using Neural Network https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/72 <p>The prediction of a photovoltaic (PV) energy production over time is considered the major challenge to integrate it with the utilize grid. This is because it is affected by many factors, including geographical locations and weather conditions. Hence, accurately forecasting PV generation is a crucial stage for ensuring a grid stability. In this paper, several studies are discussed between 2015 to 2023 based on various term forecasting conditions. Then, a neural network (NN) is employed to forecast a medium-term PV power generation by gathering meteorological data specific to the southern region of Iraq. The proposed NN model based on Python language is trained to find the PV power prediction for various seasons of the year using solar radiation and surrounding temperature of weather condition tests. While, the MATLAB Simulink model is designed to address the PV actual power for the same tests. The results show that the root mean square error of a PV generation test at autumn season provides the lowest percentage of 92.2119 W when compared with the other three seasons</p> Shahad Mohammed Radhi, Sadeq Al-Majidi , Maysam Abbod, Hamed Al-Raweshidy Copyright (c) 2024 Misan Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0 https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/72 Sun, 07 Jul 2024 00:00:00 +0000 Forecasting Robot Movement with Sensor Readings and Multi-Layer Perceptron Models https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/77 <p>&nbsp;Classification of sensor data plays a crucial role in different fields, aiding in important tasks like detecting faults, recognizing events and predicting maintenance needs. This research paper thoroughly explores the use of machine learning methods, in specific Multi-Layer Perceptron (MLP) networks, to classify sensor data. The dataset used in this study includes raw measurements from all 24 ultrasound sensors alongside their corresponding class labels indicating the robots actions (such as moving or turning left). The study tackles challenges like imbalanced classes, noisy signals, and striving for classification through a case study employing MLP models. Through conducting experiments and analysis, we fine-tuned the MLP models setup to achieve a 93.04% accuracy on the test dataset. Additionally evaluation metrics like precision, recall and F1 score highlighted the models effectiveness across different classes. A comparison with Support Vector Machines (SVM) and Logistic Regression models highlighted the performance of the MLP model. These results not only show the effectiveness of MLP networks but also provide valuable insights into best practices, for classifying sensor data. Through examining the intricacies of sensor data analysis and classification this study contributes to enhancing our knowledge of applying machine learning to real world challenges.</p> Sarah Sabeeh Copyright (c) 2024 Misan Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0 https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/77 Sun, 07 Jul 2024 00:00:00 +0000 A Line to Ground Fault Detector in 400 KV Transmission Line by Using Intelligent System https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/79 <p>The ability to locate and identify problems in power transmission lines is the most crucial component of power system engineering since it ensures the stable and efficient operation of electrical grids. to minimize downtime, prevent cascading failures, and maintain the power system's stability. problem localization helps identify the precise location of the problem and speeds up the process of restoring the power supply while minimizing the impact on consumers. This paper presents a measurement-based automatic fault localization and detection (ANFIS) technique for transmission lines. High-speed processing of real-time error localization and detection is made possible by the design and implementation of ANFIS. It is suggested that ANFIS for digital distance protection systems be able to discover issues in addition to detecting them. When a transmission experiences a three-phase malfunction Thus, the suggested method can precisely pinpoint the impacted stages. ANFIS has been trained and tested using many kinds of field data. The field data are collected by simulating faults in the Simulink Matlab model depicting the transmission line at various sites between Misan – Kut station 400 Kv along 200 Km using computer software based on Matlab. Phase current and voltage measurements are utilized as ANFIS inputs and are available at the busses. Regarding the kind and detection of defects, The output will demonstrate fault and location identification with an extremely low error percentage through simulated processes; the results also demonstrate that the approach's selectivity and speed are quite dependable and provide adequate performance for applications involving transmission and distribution monitoring; and security. The study emphasizes the criticality of promptly and precisely locating faults in power transmission lines to preserve the stability and security of the power system.</p> Batool Alhashemi, Ahmed Raisan Hussein Copyright (c) 2024 Misan Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0 https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/79 Sun, 07 Jul 2024 00:00:00 +0000 A Comprehensive Review of Radio Signal Propagation Prediction for Terrestrial Wireless Communication Systems https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/80 <p>The subject of study known as radio propagation prediction refers to predicting the behaviour and characteristics of radio waves as they propagate through the atmosphere. It is a basic element of all wireless communication systems, including satellite communications, broadcasting and cellular networks. While there is no study covers all the techniques used to predict the radio signal prediction, this study presents a review of the propagation prediction models between 2018-2023 used for terrestrial wireless communication systems; the classic empirical models were briefly explained, followed by the deterministic propagation models that have been developed using ray-tracing with deep and machine learning techniques. Recent studies on an improvement of the computational efficiency and accuracy of propagation prediction models were also reviewed, in addition to an overview of some of the traditional statistical models. Furthermore, some of the new techniques in propagation prediction were described. The results of these studies explain that techniques using ray tracing produce better results than other techniques.</p> Nabaa Ali, Hasanain A. H. Al-Behadili Copyright (c) 2024 Misan Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0 https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/80 Sun, 07 Jul 2024 00:00:00 +0000 Effect pile dimensions on behavior of container berth under ship impact https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/81 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Container berths, which are an essential component of the marine wharf, are exposed to loads from various sources, such as self-weight, operation loads and the environment loads, but the ship load is considered to have the greatest impact on the structure. The container berth in Um Qaser Port was chosen as a model for the study. This paper was based on numerical simulation which was performed by using ABAQUS program to investigate the response of container berth structure under the impact dynamic load.. Studies have dealt with choice eight different diameters of the piles, starting from 800mm to 1500mm. The study included three different cases of pile diameter. The first case is the effect of changing the spacing between the piles. The second case is the effect of the pile depth embedded in the soil. The third case is the change in thickness of pile diameter. The aim of this study is to know the behavior and performance of the pavement structure by changing some parameters, including the diameter of the pile, which is considered the basic and important part in the formation of the structure.</p> Ibtisam Jarih, Alaa Galeb, Abdulamir Atalla Copyright (c) 2024 Misan Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0 https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/81 Sun, 07 Jul 2024 00:00:00 +0000 Enhancing Robotic Grasping Performance through Data-Driven Analysis https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/78 <p>In automation, reliability in robotic grasping in dynamic environment is still a problem encountered. Further, there is the need to consider deep learning methods, as traditional approaches are not easily flexible in dealing with different objects and situations. In this work, we aim to analyze how well deep neural network models perform in predicting grasp strength based on data collected from the Smart Grasping Sandbox simulation trials. Hence, the proposed approach for analyzing the joint positions, velocities and efforts led to the design of a deep neural network for improving robot grasp performance. The question here could is if deep learning could help better predict grasp stability. To implement the method, we underwent rigorous data pre-processing such as outlier detection, and feature normalization and employed a structured neural network model for training. Our model got a training accuracy of nearly 99% and the test accuracy of nearly 96% demonstrating significant promise. These results were also better than CNN and LSTM where their accuracy rate was 94.12% and 91.81%, respectively. High deep learning performance was proven in robotic grasping, which can contribute to the creation of more flexible and sophisticated robotic platforms. This work also provides the foundation for subsequent research in robotics and automation, with emphasis on the role of data-driven methods in robotic grasping tasks.</p> Sarah Sabeeh Copyright (c) 2024 Misan Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0 https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/78 Sun, 07 Jul 2024 00:00:00 +0000 Comprehensive Review on the Flexure Behaviour of Corroded Reinforcement Concrete Beams Under Sustained Loads https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/75 <p>This study presents a comprehensive review of the flexural behaviour of corroded Reinforced Concrete (RC) beams subjected to sustained loads. The investigation synthesizes extant literature to elucidate the complex interaction between concurrent stress and steel corrosion in RC members. Emphasis is placed on the critical necessity of conducting corrosion tests under continuous stress conditions to accurately simulate in-situ structural behaviour. The review encompasses previous numerical studies on deteriorated RC beam performance and provides a critical analysis of historical loading regimes designed to mitigate corrosion in loaded RC beams. Notably, the literature reveals conflicting findings regarding the influence of loading on corrosion rates and crack propagation, highlighting areas necessitating further research. The review also considers the effects of creep, shrinkage, and stress, with particular emphasis on long-term deflection characteristics. This comprehensive analysis aims to consolidate current knowledge and identify critical research gaps in understanding the flexural behaviour of corroded RC beams under sustained loads, thereby providing a foundation for future investigations in this domain.</p> Al-Mortadha Omar, Sultan Daud Copyright (c) 2024 Misan Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0 https://www.uomisan.edu.iq/eng/mjes/index.php/eng/article/view/75 Sun, 07 Jul 2024 00:00:00 +0000