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Optimized stress-strain modeling of eco-friendly fiber-reinforced concrete members using meta-heuristic algorithms
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Eco-friendly concrete is produced using the waste of many industries. It reduces the fears concerning energy utilization, raw materials, and mass-produced cost of common concrete. Several stress-strain models documented in the literature can be utilized to estimate the ultimate strength of concrete components reinforced with fibers. Unfortunately, there is a lack of data on how non-metallic fibers, such as polypropylene (PP), affect the properties of concrete, especially eco-friendly concrete. This study presents a novel approach to modeling the stress-strain behavior of eco-friendly polypropylene fiber-reinforced concrete (PFRC) using meta-heuristic particle swarm optimization (PSO) employing 26 PFRC various mixtures. The cement was partially replaced by ground granulated blast furnace slag (GGBFS) with various amounts to make the concrete eco-friendly. The concrete was reinforced with several quantities of PP fiber. Specific cases of beams and cylinders made from PFRC were examined to learn more about their performance. The research contributes valuable insights to eco-friendly concrete design by integrating industrial byproducts (GGBFS) and non-metallic fibers, aligning with sustainable construction trends. The study demonstrates that adding sustainable fibers to concrete improves its structural integrity while lessening its environmental impact. Experimental testing validates the proposed model, showing a significant connection between the expected and actual stress-strain behavior. In terms of absolute relative error (ARE), the dataset proves that the suggested model has both the greatest (ARE 5 %) and worst (ARE > 15 %) frequencies. The proposed model demonstrates promising accuracy (R-value = 0.9975) and highlights the effectiveness of PSO in parameter optimization. Additionally, the usage of GGBFS instead of OPC resulted in CO2 reduction up to 42 %. Comparative analysis of the proposed model against existing models registered an excellent forecasted accuracy.

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Publication Date
Wed Jan 01 2020
Journal Name
Iraqi Geological Journal
Reservoir modeling for mishrif formation in Nasiriyah oilfield
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Publication Date
Thu Sep 01 2022
Journal Name
Journal Of Engineering
Properties of Roller-Compacted Concrete Pavement Containing Different Waste Material Fillers
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Waste materials might be utilized in various applications, such as sustainable roller compacted concrete pavements (RCCP), to lessen the negative environmental consequences of construction waste. The impacts of utilizing (brick, thermostone, granite, and ceramic) powders on the mechanical characteristics of RCCP are investigated in this study. To achieve this, the waste materials were crushed, grounded, and blended before being utilized as filler in the RCCP. After the mixes were prepared, compressive strength, splitting tensile strength, flexural strength, water absorption, density, and porosity were all determined. According to the research results, adding some of these powders, mainly brick and granite powder, enhances the mechanical

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Publication Date
Sun Oct 02 2022
Journal Name
Engineering, Technology & Applied Science Research
Statistical Modeling for Traffic Noise: The Case of Kirkuk City
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The auditory system can suffer from exposure to loud noise and human health can be affected. Traffic noise is a primary contributor to noise pollution. To measure the noise levels, 3 variables were examined at 25 locations. It was found that the main factors that determine the increase in noise level are traffic volume, vehicle speed, and road functional class. The data have been taken during three different periods per day so that they represent and cover the traffic noise of the city during heavy traffic flow conditions. Analysis of traffic noise prediction was conducted using a simple linear regression model to accurately predict the equivalent continuous sound level. The difference between the predicted and the measured noise shows that

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Publication Date
Tue Mar 16 2021
Journal Name
2021 4th International Conference On Energy Conservation And Efficiency (icece)
Finite Element Modeling Of Finned Double-Pass Solar Air Heaters
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Publication Date
Sun Dec 30 2018
Journal Name
Advances In Remote Sensing And Geo Informatics Applications
Correlation Between Surface Modeling and Pulse Width of FWF-Lidar
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Publication Date
Tue Sep 02 2014
Journal Name
Arab J Sci Eng
Modeling of Trichloroethylene Migration in Three-Dimensional Saturated Sandy Soil
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Publication Date
Tue Oct 01 2019
Journal Name
2019 12th International Conference On Developments In Esystems Engineering (dese)
Structural Modeling of Cross-Frame Behavior in Steel Girder Bridges
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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
MODELING DEATH RATE OF THE COVID-19 PANDEMIC IN IRAQ
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Publication Date
Sat Mar 30 2019
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Interaction of Aqueous Cu2+ Ions with Granules of Crushed Concrete
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The sorption of Cu2+ ions from synthetic wastewater using crushed concrete demolition waste (CCDW) which collected from a demolition site was investigated in a batch sorption system. Factors influencing on sorption process such as shaking time (0-300min), the initial concentration of contaminant (100-750mg/L), shaking speed (0-250 rpm), and adsorbent dosage (0.05-3 g/ml) have been studied. Batch experiments confirmed that the best values of these parameters were (180 min, 100 mg/l, 250 rpm, 0.7 g CCDW/100 ml) respectively where the achieved removal efficiency is equal to 100%. Sorption data were described using four isotherm models (Langmuir, Freundlich, Redlich-Peterson, and Radke-Prausnitz). Results proved that the pure ads

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Publication Date
Mon Nov 11 2019
Journal Name
Day 3 Wed, November 13, 2019
Drill Bit Selection Optimization Based on Rate of Penetration: Application of Artificial Neural Networks and Genetic Algorithms
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Abstract<p>The drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the</p> ... Show More
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