Wang, Yule and AL-Huqail, Arwa Abdulkreem and Salimimoghadam, Shadi and Mohammed, Khidhair Jasim and Jan, Amin and Ali, H. Elhosiny and Khadimallah, Mohamed Amine and Assilzadeh, Hamid (2022) The metaheuristic optimization of the mechanical properties of sustainable energies using artificial neural networks and genetic algorithm: A case study by eggshell fine waste. INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 46 (15). pp. 21338-21352. ISSN 1099-114X, DOI https://doi.org/10.1002/er.8255.
Full text not available from this repository.Abstract
Eggshell concrete is a novel green material that aids the recycling of eggshell powder (ESP) waste while decreasing the environmental damage due to higher manufacture to develop sustainable energies. Nevertheless, current investigations on eggshell concrete are limited, and the results might vary according to admixture design variations. Despite the fact that the design of experiments is utilized to simplify and optimize the research of sustainable energies, the studies employing eggshell concrete are still uncommon. The powdered egg shells were employed as fine concrete aggregate as a tool of sustainable energies. The flexural and compressive strength of concrete with (5%, 10%, and 15%) and without egg shell are examined, and the findings are predicted by artificial neural network (ANN) and genetic algorithm (GA) as a hybridized model of ANN-GA. The contour plot research revealed that eggshell powder boosted the energy stability in an appropriate replacement proportion of 5% to 10%. Conversely, for mix designs with a larger water ratio, the partial substitution with eggshell powder is preferable. The findings demonstrate that with 5% ESP replacement, the strengths were greater than in control concrete, indicating that 5% ESP is an ideal content for maximal strength. Furthermore, in terms of transport qualities, the performance of ESP concretes was equivalent to control concrete up to 15% ESP substitution. The statistical regression indices as determination coefficient (R-2) and root-mean-square error demonstrated that the ANN-GA model is an effective tool for formulating and predicting the flexural and compressive strength of eggshell concrete to develop sustainable energies.
| Item Type: | Article |
|---|---|
| Funders: | Princess Nourah bint Abdulrahman University [PNURSP2022R9], Deanship of Scientific Research at King Khalid University, Saudi Arabia |
| Uncontrolled Keywords: | artificial neural network; concrete; eggshell waste; genetic algorithm; sustainable energy |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Koh Ai Peng |
| Date Deposited: | 23 Jul 2024 04:08 |
| Last Modified: | 23 Jul 2024 04:08 |
| URI: | http://eprints.um.edu.my/id/eprint/46274 |
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