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INDONESIA
Civil Engineering Journal
Published by C.E.J Publishing Group
ISSN : 24763055     EISSN : 24763055     DOI : -
Core Subject : Engineering,
Civil Engineering, Building, Construction & Architecture
Civil Engineering Journal is a multidisciplinary, an open-access, internationally double-blind peer -reviewed journal concerned with all aspects of civil engineering, which include but are not necessarily restricted to: Building Materials and Structures, Coastal and Harbor Engineering, Constructions Technology, Constructions Management, Road and Bridge Engineering, Renovation of Buildings, Earthquake Engineering, Environmental Engineering, Geotechnical Engineering, Highway Engineering, Hydraulic and Hydraulic Structures, Structural Engineering, Surveying and Geo-Spatial Engineering, Transportation Engineering, Tunnel Engineering, Urban Engineering and Economy, Water Resources Engineering, Urban Drainage.
Arjuna Subject : -
Articles 15 Documents
 1   2 
Search results for , issue "Vol 10, No 5 (2024): May" : 15 Documents clear
Ultimate Strength of Internal Ring-Reinforced KT Joints Under Brace Axial Compression Adnan Rasul; Saravanan Karuppanan; Veeradasan Perumal; Mark Ovinis; Mohsin Iqbal
Civil Engineering Journal Vol 10, No 5 (2024): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-05-012

Abstract

Internal ring stiffeners are frequently used to improve the ultimate strength of tubular joints in offshore structures. However, there is a noticeable absence of specific design guidance regarding the assessment of their ultimate strengths in prominent offshore codes and design guides. No equations are available to determine the ultimate strength of internal ring-reinforced KT joints. This work developed equations to determine the ultimate strength and the strength ratio of internal ring-reinforced KT joints based on numerical models and parametric studies comprising ring parameters and joint parameters. Specifically, a finite element model and a response surface approach with eight parameters (λ, δ, ψ, ζ, θ, τ, γ, and β) as inputs and two outputs (ultimate strength and the strength ratio) were evaluated since efficient response surface methodology has been proven to give precise and comprehensive predictions. KT-joint with parameters λ=0.9111, δ=0.2, ψ=0.7030, ζ=0.3, θ=45°, τ=0.90, γ=16.25, and β=0.6 has the maximum ultimate strength, and the KT-joint with parameters: λ=1, δ=0.2, ψ=0.8, ζ=0.5697, θ=45°, τ=0.61, γ=24, and β=0.41 has the maximum strength ratio. The KT-joints with the optimized parameters were validated through finite element analysis. The percentage difference was less than 1.7%, indicating the applicability and high accuracy of the response surface methodology. Doi: 10.28991/CEJ-2024-010-05-012 Full Text: PDF
Unveiling the Impact of Psychological Factors on Consumer Purchase Intentions for Overall Sustainable Success in Green Residential Buildings: Using SEM-ANN Analysis Ahmad M. Zamil; Ahmed Farouk Kineber; Mohammad Alhusban
Civil Engineering Journal Vol 10, No 5 (2024): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-05-07

Abstract

The research problem addressed in this study is the limited understanding of the intricate interactions among emotional, environmental, and psychological factors within organizations and their collective impact on overall sustainable success (OSS). A critical gap exists in the literature, as previous studies often analyze these factors in isolation, leaving an incomplete picture of their interdependence. To fill this gap, this study aims to comprehensively understand the interplay between Psychological Factors (PF) and OSS. The objectives are to identify relevant factors, collect data, and employ a rigorous methodology for analysis. The research methodology involves a three-phase approach: factor identification, data collection, and analysis. This study leverages a unique integration of Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) to deepen the analysis, revealing intricate relationships among identified factors. The study's findings highlight a robust positive association between PF and OSS, underscoring the significance of prioritizing employees' psychological well-being for enhanced workplace satisfaction and performance. These insights have practical implications for organizational leaders and managers, guiding them to cultivate positive emotional climates, instill environmentally conscious practices, and address negative emotional states within their teams. Doi: 10.28991/CEJ-2024-010-05-07 Full Text: PDF
Optimizing Gene Expression Programming to Predict Shear Capacity in Corrugated Web Steel Beams Mazen Shrif; Zaid A. Al-Sadoon; Samer Barakat; Ahed Habib; Omar Mostafa
Civil Engineering Journal Vol 10, No 5 (2024): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-05-02

Abstract

Corrugated web steel systems, such as corrugated web girders (CWG) and beams (CWSB), have the potential to influence the modern construction industry due to their unique properties, including enhanced shear strength and reduced necessity for transverse stiffeners. Nevertheless, the lack of a rapid and accurate design approach still limits its wide applications. Recently, gene expression programming (GEP) has been employed to predict the shear capacity of cold-formed steel channels, demonstrating superior predictive accuracy and compliance with established standards. This study applies GEP to predict the shear capacity of sinusoidal CWSBs and optimizes its predictive performance by employing a systematic grid search to explore combinations of chromosomes, head sizes, gene counts, and linking functions. The process involved testing 19 different parameter combinations and more than 60 developed models. The findings include the sensitivity of the model's performance to gene count and the critical role of the linking function. The optimal model in the study, GEP13, achieved R² of 0.95, an RMSE of 100.5, and an MAE of 86.6 in the testing dataset with 150 chromosomes, a head size of 12, and four genes using a multiplication linking function. Doi: 10.28991/CEJ-2024-010-05-02 Full Text: PDF
Artificial Neural Network-Based Prediction of Physical and Mechanical Properties of Concrete Containing Glass Aggregates Faroq Maraqa; Amjad A. Yasin; Eid Al-Sahawneh; Jamal Alomari; Jamal Al-Adwan; Ahmad A. Al-Elwan
Civil Engineering Journal Vol 10, No 5 (2024): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-05-018

Abstract

This comprehensive study analyzes the use of crushed glass as both fine and coarse aggregate in concrete, as well as the prediction accuracy of Artificial Neural Networks (ANN). The primary objectives are to understand the interactions between concrete’s constituents and to assess the accuracy of ANN models in predicting concrete’s mechanical and physical properties. This is achieved using a two-decade experimental results dataset of concrete’s compressive and tensile strengths, slump, density, and the corresponding mix design proportions, including waste glass aggregate. A series of 70 concrete samples were carefully built and tested, with compressive strengths varying from 12 to 71 MPa and glass aggregate percentages ranging from 0-100%. These samples served as the basis for the creation of an input dataset and ANN targets. The ANN model underwent intensive training, validation, testing, and statistical regression analysis. The ANN models are exceptionally accurate, with a continuously low error margin of roughly 2%, highlighting their usefulness in matching experimental and predicted results. Validation techniques highlight the models' dependability, with consistently high coefficients of determination (R-values), including 0.99484, demonstrating their robustness in replicating complicated concrete properties. The data analysis shows a unique pattern, with optimum glass aggregate percentages in the range of 10–20%. Beyond this range, there is a noticeable decline in concrete properties. Finally, the study confirms the efficacy of ANN in predictive modeling while also validating the potential of crushed glass to replace natural aggregates in concrete. Doi: 10.28991/CEJ-2024-010-05-018 Full Text: PDF
Investigating the Hydraulic Behaviours of an Alluvial Meandering River Reach Between Two Barrages Zainab D. Abbass; Jaafar S. Maatooq; Mustafa M. Al-Mukhtar
Civil Engineering Journal Vol 10, No 5 (2024): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-05-013

Abstract

The Abassia-Shammia is a meandering stream in Najaf province. Predicting and estimating the flow behavior of meandering rivers is crucial for designing hydraulic structures in an accurate manner in the vicinity and conducting environmental and ecological studies. The hydraulic properties of an alluvial stream are typically subject to change due to its location between two barrages. In this study, HEC RAS 2D, developed by the Hydrologic Engineering Center’s River Analysis System, was employed to simulate the hydraulic performance of the Euphrates River reach between two series of barrages, i.e., Abbassia and Shammia. Reliable input data, such as Digital Elevation Models (DEMs), land cover classification, and data for the 2023 hydrograph as a boundary condition, were utilized to develop the hydraulic model. The model was calibrated by using the observed water surface elevation from field measurements downstream of Abbassia to match the ones calculated by the model. Hence, the hydraulic model of the Euphrates River was created using an appropriate Manning roughness coefficient value (n = 0.04) based on the most suitable values of statistical indices, correlation coefficient (R²), and root mean squared error (RMSE) to assess the agreement between the observed and simulated data throughout the calibration and validation phases. To visualize the HECRAS2D output, the hydraulic maps for the study region were presented. The ten cross-sections from the field study (investigated at the same period of flow hydrograph) were presented for modeling to emphasize the river's hydraulic behaviors. Based on the results, the water surface elevation ranged between 19.1–29.2 m.a.s.l., and the flow velocity was 2.50 m/s. Meanwhile, the values of bed shear stress (Pa) and the water depth (m) ranged between 0.1 Pa and 8.93 m for the entire river. The results also proved the superiority of the HEC RAS2D model to reliably represent the hydraulic performance of the Euphrates River reach located between the two barrages. Doi: 10.28991/CEJ-2024-010-05-013 Full Text: PDF

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