Journal of Engineering

Traffic congestion has become a serious problem that traffic engineers still face. This research explains the sustainable indicators and congestion index for urban streets and their implementation to evaluate the performance measures proceeding toward sustainable roads. Congestion measures in terms of speed reduction and sustainable indicators; mobility (congestion index, travel time, and delay), costs (vehicle operating cost), socio-economic effect (in terms of an estimated factor called User Satisfaction Index (USI), and air pollution (Fuel emissions) are estimated. Link 3 has the highest delay value of approximately (2 minutes) for the evening peak period in the north-south direction due to a large number of vehicles dense traffic and mixed land use of the study area that produce many attraction trips daily. Congestion is distributed more spatially during the morning peak periods, while in evening periods is relatively concentrated on a specific link. The reduction in travel speed due to the congestion effect induced higher vehicle operating costs of an average unit of 2.9 per Km for links 1, 2.6, and 2.4 for links 2 and 3, respectively, at peak time from (8 a.m. to 12 a.m.). Generally, traffic congestion is mainly concentrated on Links 1 and 3 of Palestine’s urban street segments. The overall user satisfaction index (USI) is 2.209 and about 44.18%, meaning user satisfaction is less than 50%. This illustrates that the selected segment of the study area is unsustainable regarding the social and commuter opinions aspect.

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system train and test part was applied to dust phenomena historical data. Its data has been collected through the Iraqi Meteorological Organization and Seismology (IMOS) raw dataset with 170237 of 17023 rows and 10 columns. The LSTM model achieved small time, computationally complexity of, and layers number while being effective and accurate for dust prediction. The simulation results reveal that the model's mean square error test reaches 0.12877 and Mean Absolute Error (MAE) test is 0.07411 at the same rates of learning and exact features values of vector in the dense layer, representing a neural network layer deeply is connected to the LSTM training proposed model. Finally, the model suggested enhances monitoring performance.

Reactive Powder Concrete (RPC) is a type of high-performance concrete known for its exceptional strength and durability. This study aims to determine some characteristics of RPC, such as compressive strength and fresh density, after exposure to different curing temperatures (60, 120, and 200 °C) for 4 hours over two days. The study involves variables such as micro steel fibers content with 1% by volume of RPC samples and elevated temperature. The optimum temperature discovered was 60 °C, which gave the best results for the mechanical properties of RPC. The fresh density increased by about 1.95%, and compressive strength increased by about 33.3% at 60 °C for 28 days compared to the reference mixture.

This paper investigates the influence of date-palm fibers on the compaction and strength properties of fine soil. The laboratory tests, including specific gravity, compaction tests, and unconfined compression strength tests, were conducted, integrating different proportions (ranging from 0 to 2%) of date-palm fibers into the soil mixture. The palm fibers were divided into two distinct lengths (30 mm and 60 mm) to be mixed with the soil. Nine samples were prepared with varying proportions of date-palm fibers for the experimental investigation, aiming to specifically examine the influences exerted by both palm-fiber length and palm-fiber content on the soil’s compaction and strength characteristics when mixed with date-palm fibers. The compaction test results demonstrate a decrease in the dry unit weight and an increase in the optimum moisture content by approximately 10%. Additionally, the length of the date-palm fibers impacts the optimum moisture content and the maximum dry unit weight of the soil mixture. In contrast, the unconfined compressive stress increased by about 30% with higher date-palm fiber contents. This increase in unconfined compressive stress due to increased date-palm fiber content is a significant finding, indicating improved soil strength. This finding holds the enhancing construction performance, sustainability, and cost-efficiency. In conclusion, this soil-fiber mixture shows suitable hydraulic applications. The utilization of natural materials in civil engineering demands the exploration of available natural fibers.

Reservoir pressure plays a significant role in all reservoir and production engineering studies. It is crucial to characterize petroleum reservoirs: by detecting fluid movement, computing oil in place, and calculating the recovery factor. Knowledge of reservoir pressure is essential for predicting future production rates, optimizing well performance, or planning enhanced oil recovery strategies. However, applying the methods to investigate reservoir pressure performance is challenging because reservoirs are large, complex systems with irregular geometries in subsurface formations with numerous uncertainties and limited information about the reservoir's structure and behavior. Furthermore, many computational techniques, both numerical and analytical, have been utilized to examine reservoir pressure performance. This paper summarizes the concepts and applications of traditional and novel ways to investigate reservoir pressure changes over time. It provides a comprehensive review that assists the reader in recognizing and distinguishing between various techniques for obtaining an accurate description of reservoir pressure behavior during production, such as the reservoir simulation method, material balance equation approach, time-lapse seismic data, and modern artificial intelligence methods.

Due to their great structural efficiency and efficient utilization of materials, steel-reinforced hollow-core concrete columns are often employed in utility poles, ground piles, and piers for bridges. Based on research, these columns' performance is impacted by many design parameters. However, corrosion can be a problem in steel-reinforced concrete structures. This paper examines the differences between using steel and GFRP longitudinal bars in hollow-section square concrete columns and explores the potential benefits of using GFRP bars as an option that is economically viable and non-corrosive. According to the study results, the computational results clearly show how an increased longitudinal GFRP reinforcement ratio improves the columns' bearing capability, but when compared to steel reinforcement, it provides less bearing capability. For the same reinforcement ratio (1.46 %, 3.29 %, and 4.9 %), the findings demonstrated that GFRP columns had a decrease in the axial bearing load by 13.1%, 9.2 %, and 9.4%, respectively.

This study aims to evaluate experimentally the mechanical characteristics of Self-Compacting Concrete (SCC) comprising expanded polystyrene beads (EPS) to produce flowable lightweight concrete reinforced with steel fibers. In this paper, the effect of steel fibers and EPS content on the fresh and hardened mechanical properties of SCC specimens, using two percentages for Polystyrene aggregate replacement (25% and 50%) and three values for volume fraction of steel fiber content (0%, 0.75%, and 1.5%), were examined. Fresh mixture properties were determined using slump flow, L-box, and V-funnel tests. Mechanical properties for hardened samples were obtained using standard specimens for compressive strength, density, split, and flexural strength. The study showed that EPS content has no adverse effect on the rheological features of the SCC. However, workability falls below specification limits when adding steel fibers to SCC. Results revealed that using 25% of EPS content resulted in lightweight structural concrete, while lightweight moderate-strength concrete was produced using 50% of EPS content. Furthermore, the study has shown that the split and flexural tensile strength were reduced substantially by 53% and 60% due to EPS addition. However, adding steel fibers remarkably improved the indirect tensile strength and Modulus of rupture by 46% and 80%, respectively. The mode of failure of the concrete specimens containing EPS beads and steel fibers did not show brittle failure behavior generally encountered in normal-weight concrete, indicating a more ductile behavior.

Copy-move forgery is prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Standard Deviation (STD) and Angular Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different resolutions. By considering features from multiple levels, the detection algorithm can better capture both global and local characteristics of the manipulated regions, enhancing the accuracy of forgery detection. To achieve a high accuracy rate, this paper presents a variety of scenarios based on a machine-learning approach. In Copy-Move detection, artifacts and their properties are used as image features and support Vector Machine (SVM) to determine whether an image is tampered with. The dataset is manipulated to train and test each classifier; the target is to learn the discriminative patterns that detect instances of copy-move forgery. Media Integration and Call Center Forgery (MICC-F2000) were utilized in this paper. Experimental evaluation demonstrates the effectiveness of the proposed methodology in detecting copy-move. The implementation phases in the proposed work have produced encouraging outcomes.

Flat slabs made of two-way reinforced concrete are a common, effective, and affordable structural method. Thin layer slabs and lightweight are of great importance in modern building construction without the need to increase the cross-section of columns, walls, and foundations. Consequently, it becomes more important to cover all aspects related to stiffness, vibration, and strength of lightweight concrete slabs. To achieve serviceable flat slabs from lightweight concrete (LWC), the comparison between LWC flat plate and flat slab is studied in this article for strength and stiffness. The construction of the flat plate is much easier than other slabs even though the problem facing the flat plate is the punching shear. Hence, the addition of the drop panel as a solution. However, the two LWC slabs are exposed to a uniform pressure of dead load and human live load. The vibration of the slab is related to the stiffness in the form of the natural frequency. These floor systems should satisfy walking excitation criteria of acceleration limit, which is equal to 0.50% of g for office occupancies. This study aims to analyze a flat plate and flat slab with a drop panel for strength and stiffness. The analysis is carried out in ABAQUS software. The results of the analysis show that the flat slab has an effective increase in strength of about 60% compared to the stiffness which was lower by about 2.2 %. However, the stiffness of both slabs is within the limits of walking excitation criteria.

This research provides information on the heat transfer in the cavity by natural convection which has a partially open side with a ratio (A = 1, 0.75, 0.5, 0.25) to the surroundings for cooling. It is filled with porous media (glass beads) and saturated with air. The bottom wall was heated with a constant heat flux (q = 1500, 3000, 4500, 6000) W/m² while the top and other walls of the cavity were well insulated. The porous media had small porosity (0.418), a range of Rayleigh number Ra (57.6-1470). The distribution of temperatures, the local Nusselt number, and the average Nusselt number were all extracted from the testing rig's temperature data. It is clear that the fluid flow and heat transfer are affected by heat flux and the ratio of partially open side. Observed that, the greatest temperature values at maximum heat flux (q) and minimum open ratio (A). Thus, the temperature rising at all values of the constant heat flux and the enhancement of the local Nusselt number at (q = 6000) W/m² about (5.47%, 3.85%, 1.76%) for (A = 1, 0.75, 0.5) respectively, when compared with (A = 0.25). The enhancement of the average Nusselt number at (q = 6000) W/m² is about (7.28%, 4.55%, 2.27%) for (A = 1, 0.75, 0.5) respectively, compared with (A = 0.25).

When designing the pavement layers, a suitable thickness must be chosen to protect the pavement from environmental conditions and traffic loads and ensure the structure's durability up to the design life. To investigate the behavior of flexible pavement, the characteristics and thickness of each layer are programmed into the finite element method (FEM). The Abaqus program is one of the infinite-element analysis programs. The use of the Abaqus program leads to a reduction in cost and time compared to laboratory tests. In this study, the Abaqus program analyzed a three-dimensional model of a multi-layered road section, and all materials have elastic behavior. The model comprises five layers (wearing, binder, base, subbase, and subgrade). The model was looked at with different base layer thicknesses (15, 25, and 30 cm) and elasticity moduli (1655, 2070, and 3000 MPa). Critical parameters were looked at in the present research: vertical displacement at the wearing layer's top, horizontal tensile strain in the asphalt layer's lowest point, and vertical compressive strain at the subgrade's surface. The outcomes indicated that the pavement is more susceptible to rutting than fatigue as a result of static load. An increase in thickness and modulus of elasticity for the base layer leads to a reduction in rutting risks.