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All Journal Civil Engineering Journal Journal of Applied Engineering and Technological Science (JAETS)
Abdulhameed, Ali A.
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Using Fuzzy Cognitive Maps For Modeling Environmental Aspect of Sustainable Development in Construction Projects Alsaadi, Atheer M.; Abdulhameed, Ali A.; Alsaadi, Farah M.; Alhashmi, Heba A.
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.7041

Abstract

The pillars of sustainable development are representing the interface between environmental, economic, and social sustainability. Sustainable development is a method of planning and managing construction projects to reduce the effect of the construction process on the environment so that there is a balance between environmental capabilities and the human needs of present and future generations. Usually, Environmental sustainability is most important and effective in construction projects. The environment suffers from significant negative impacts as a result of the implementation of construction projects; therefore, this study aims to identify the effecting factors on environmentally sustainable development. The methodology of this study used fuzzy cognitive maps (FCMs) because of adopted simulation approach, after selecting the factors that have RII more than 65% and determine causal relationship between factors by applying fuzzy logic using MATLAB program. Then the effecting factors were analyzed and ranked by static and dynamic analysis. The results showed the static analysis of effecting factors on ESD in first quarter are characterized by influential and affected by other factors of (ESD), were include (C2.4, C4.6, C1.6, C2.1, C3.3, C3.7, C3.6, C6.2), When comparing between dynamic analysis and RII of the factors, it has been noticed a difference in the importance. This is an essential finding in the understanding that dynamic analysis considers the interactions between factors, while the RII takes the reasons independently and neglects interactions between them. The study has provided recommendations for the application of (FCM) model that was proposed depend on these factors in building projects to improve the environment and reduce its negative effects.  
AI-Driven Shear Capacity Model of Steel Studs in Composite Structural Systems Hanoon, Ammar N.; Abdulhameed, Haider A.; Abdulhameed, Ali A.; Hason, Mahir M.; Abbas, Rafaa M.; Mansi, Aseel S.
Civil Engineering Journal Vol. 12 No. 2 (2026): February
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-02-04

Abstract

In composite steel-concrete structures, shear connectors in the form of headed steel studs are commonly utilized to transfer longitudinal shear force developed at the interface between the two materials. To overcome the shortcomings of design codes, which frequently understate shear capacity and fail to take advantage of sophisticated computational methods, this paper presents an optimization attempt to estimate the shear strength of headed steel studs utilizing the Grey Wolf Optimizer (GWO) technique using MATLAB software. Data from 234 experimental tests are employed to identify and highlight key input parameters influencing the shear strength of headed steel studs. These key parameters include concrete compressive strength (f’c), diameter (D), and tensile strength of the steel stud shank (fu). After identifying and examining the limits of the experimental data, the proposed model has been developed using about 80% of the mixed raw dataset. The remaining 20% of the raw data is utilized to validate the proposed model. The predicted shear strength of headed steel studs closely matched the experimental results. This research offers an innovative strategy to measure the steel stud's shear capacity employing GWO, showing the current code's limitations. The GWO model showed excellent accuracy in predicting the shear strength with an R-value of 0.9922, indicating that the predicted value is in good agreement with experimental observations. Interestingly, the model's mean absolute error with 100 wolves in the GWO method was only 7.51%, showing the proposed model provides an improvement in shear capacity forecasting for practical structural engineering applications.
Co-Authors Abbas, Rafaa M. Abdulhameed, Haider A. Alhashmi, Heba A. Alsaadi, Atheer M. Alsaadi, Farah M. Hanoon, Ammar N. Hason, Mahir M. Mansi, Aseel S.