Journal of University of Babylon

The structural behavior of continuous recycled aggregate concrete (RAC) beams under point loading at each span is examined experimentally in this work. Nine continuous beams, each 225 mm deep, 150 mm wide, and 3000 mm long, were tested as part of the program.
As references, there were two beams made of normal concrete, one intended for shear failure and the other for flexural failure. Three beams were cast with different replacement ratios of recycled concrete Aggregate (20%, 40%, and 70%) to assess the effect of RAC content. The impact of RCA placement was also investigated using two flexure-critical beams and two shear-critical beams, each developed with a particular recycled aggregate location. The main experimental parameters included steel fiber, the position of recycled Aggregate, and the ratio of recycled aggregate replacement.
According to the results, recycled Aggregate reduced the ultimate load by up to 5.38%. On the other hand, the ultimate load increased by as much as 10% when steel fiber was added. Additionally, flexural strength was reduced by up to 10% when recycled Aggregate was moved within beams intended for flexural failure. In contrast, shear-critical beams had a shear strength drop of up to 23% when the same modification was made.

Waste PVC parts and waste rubber materials are used to improvise a pressure switch. Also, metallic parts are involved in building that pressure switch. The design of pressure switch considers the using of cheap waste materials and involves so simple design that any primitive technic can build this item. Furthermore, the design of pressure switch puts into considerations the hazard associated with rubber’s diaphragm leakage or failure. The dimensions of switch’s parts are selected based on computation using finite element analysis for the two pieces of rubber which are synchronously working. The first rubber piece works as a diaphragm while the second, which is strip, works as spring as well as adjustable tensioner to regulate the set value of pressure. There are two metallic parts working on transferring the motion from the diaphragm to strip. The first metallic part is spindle and the second is the leverage which is connected to the electrical contact too. The two rubber pieces and the two metallic parts are drawn in Catia R17 software, then they are imported to Ansys Workbench 2022 software where a three dimensional modeling is created. The purpose of this modeling is to find out the relation between the applied pressure on the diaphragm and the resulting deformation on the strip which is previously stretched to corresponding certain value of pressure. Depending on the results obtained from the modeling, a real pressure switch is built with the same dimensions in modeling. Then, that real pressure switch is tested using the same input pressure values in the modeling. The practical deformation values are compared to the corresponding values obtained from the Ansys Workbench 2022 modeling. The comparison shows that the experimental deformations are higher at lower pressure values because the mechanical properties of the old real rubber are degraded with time while at higher pressure vales the real deformations become less than those in the modeling because the friction in real joints increases with high pressure and also, in Ansys Workbench 2022 modeling, the friction in joints is neglected. Anyway, there is little difference in magnitudes of deformation but generally both practical and modeling results have the same behavior and manor besides they follow the same logical path.

Differential protection is a key technique used to safeguard power transformers from internal faults while ensuring stable operation during inrush currents and external faults. The objective of this study is to evaluate the operating characteristic curve of differential protection by the simulation model enabled various fault scenarios, including magnetizing inrush currents, external faults, and internal faults. A differential protection model for a three-phase transformer was simulated using MATLAB/Simulink to simulate various fault conditions and also present a practical test was conducted using an Omicron CMC 356 tester tuned to the proposed curve to validate the relay's performance under controlled experimental conditions, which addresses the lack of experimental validation for differential protection under combined fault scenarios. The same fault types and parameters were used in both Simulink and Omicron CMC 356 tests, producing identical detection results with a 30 ms trip time. The experimental results closely align with those obtained from the simulation, confirming that the proposed differential protection operating characteristic curve is more stable when distinguishing external faults and inrush currents, and performs more efficiently when distinguishing internal faults. Combining real-time MATLAB/Simulink and CMC 356 experiments provides an effective methodology for validating the proposed differential protection technology for power transformers.

In this study the Artificial Intelligent Method (AIM) was used to find safety factors against sliding, overturning and bearing capacity of cantilever type retaining wall. The method was also used to determine the optimum dimensions of retaining wall and consequently minimum retaining wall volume. Three parameters were investigated in this study: friction of the soil beneath and behind the retaining wall ( ), retaining wall height (H) and surcharge load (Q). approximately, zero constrains violation and constant volume of retaining wall were achieved through three iterations of solution. This results gave a reliability for using this method for optimum design of cantilever type retaining wall. The results demonstrated that, the significant increasing in sliding and bearing capacity factors of safety started at ( ), while the safety factor against overturning decreased with increasing soil friction up to ( ) and then increased. On the other hand, there is no change in base width of retaining wall when ( ) excessed ( ), and the optimum volume of retaining wall decreased significantly at this value of friction angle. Finally, the research concluded the possibility of using the results obtained from this study as a design charts to obtain the various retaining wall elements and different safety factors within the limits of studied parameters i.e, soil friction, height of retaining wall and surcharge load.

Lymphoma diagnosis remains a clinical challenge due to its biological heterogeneity, concurrent symptoms, and often uncertain initial manifestations. This study proposes an integrated data-driven system combining supervised machine learning and Multi-Criteria Decision Analysis (MCDA) to enhance clinical interpretability and diagnostic accuracy. The method ranks patient cases based on the relative severity of diagnostic factors such as CRP, LDH, hemoglobin, WBC, platelet count, tumor size, and age using an entropy-weighted TOPSIS model. Two thousand simulated patient profiles were created in a clinically informative dataset for validating and testing the system. The same set of features was used for training a Random Forest classifier to gauge the decision model's robustness. The classifier obtained an AUC of 0.53 and a prediction accuracy of 54.75%. Performance metrics were highly aligned with MCDA-generated rankings , and the three most important diagnostic markers – LDH, CRP, and hemoglobin – were always among both the machine learning models and the MCDA rankings. Complementary approaches like Principal Component Analysis (PCA), correlation heatmaps, ROC plots, and decision tree visualizations supported the models' structure and interpretability. The results show that the combination of MCDA and data-driven classification has the promise to aid the creation of transparent, flexible, and clinically meaningful diagnostic systems. This hybrid system supports future precision medicine uses, such as integration with imaging modalities, longitudinal monitoring of patients, and molecular data.

Machine learning (ML) algorithms for detecting defects and predictive maintenance of industrial equipment have emerged as a set of critical methods for improving operational efficiency, reducing unexpected downtime, and extending machinery life. This study presents a comprehensive examination of various machine learning models, signal processing approaches, and reduced dimensionality methods for monitoring system health and detecting possible flaws based on research published in the last five years. Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Genetic Algorithms (GA), Multi-Layer Perceptron (MLP), and Fully Connected Neural Networks (FCNN) are utilized for classifying fault patterns coming from sensor data. In contrast, signal processing techniques such as Mel-Frequency Cepstral Coefficients (MFCC) and Short-Time Fourier Transform (STFT) are used to collect significant features from vibration and acoustic signals. Dimensionality reduction approaches such as Principal Component Analysis (PCA), t-distributed Stochastic Neighbour Embedding (t-SNE), ISOMAP, Independent Component Analysis (ICA), and Autoencoders (AE) are used to simplify complex data structures and show crucial defect signals. Random Forest, K-Nearest Neighbours (KNN), and CatBoost are some of the algorithmic ensembles learning methods studied for prediction accuracy and robustness. Furthermore, advanced deep learning models, such as 1D Deep Convolutional Neural Networks (1D-DCNN) and ResNet-3N, are utilized to capture temporal and spatial patterns in time-series data, leading to a more complete comprehension of fault dynamics. The research shows the effectiveness of these various approaches in boosting fault detection systems and improving maintenance techniques, paving the way for intelligent technologies in modern manufacturing.

This study presents a comprehensive analysis of hazardous solid waste generation and management in residential areas of Hilla City, Iraq, and suggests an innovative pre-treatment method for basis separation. The study found alarming levels of hazardous materials—such as batteries, electronics, medications, paints, and household chemicals—such as around 8.3% of the total waste by carefully compiling and evaluating solid waste from 20 households spread over various neighborhoods in the heart of Hilla city. Every person generated around 1.42 kg of waste daily, with roughly 0.118 kg of possibly dangerous chemicals. This emphasizes how daily behavior adds to environmental dangers. In response, a new three-step separation method was introduced, combining color-coded bins, magnetic sorting for metal hazards and density-based techniques to isolate chemical waste— offering a more practical and effective. During pilot testing, the proposed management system successfully separated hazardous waste with 87% efficiency. If widely adopted, this approach could significantly reduce environmental pollution—by as much as 65%—and greatly enhance the way waste is managed in Hilla City, creating a cleaner and healthier environment for its residents. The study recommends establishment of neighborhood-level hazardous waste collection points and community education programs to ensure sustainable implementation of the proposed system.