Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structures
Abstract In this research paper, the intelligent learning abilities of the gray wolf optimization (GWO), multi-verse optimization (MVO), moth fly optimization, particle swarm optimization (PSO), and whale optimization algorithm (WOA) metaheuristic techniques and the response surface methodology (RSM...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-58666-8 |
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| author | Kennedy C. Onyelowe Ali F. H. Adam Nestor Ulloa Cesar Garcia Alexis Ivan Andrade Valle María Gabriela Zúñiga Rodríguez Andrea Natali Zarate Villacres Jamshid Shakeri Lewechi Anyaogu Mohammadreza Alimoradijazi Nakkeeran Ganasen |
| author_facet | Kennedy C. Onyelowe Ali F. H. Adam Nestor Ulloa Cesar Garcia Alexis Ivan Andrade Valle María Gabriela Zúñiga Rodríguez Andrea Natali Zarate Villacres Jamshid Shakeri Lewechi Anyaogu Mohammadreza Alimoradijazi Nakkeeran Ganasen |
| author_sort | Kennedy C. Onyelowe |
| collection | DOAJ |
| description | Abstract In this research paper, the intelligent learning abilities of the gray wolf optimization (GWO), multi-verse optimization (MVO), moth fly optimization, particle swarm optimization (PSO), and whale optimization algorithm (WOA) metaheuristic techniques and the response surface methodology (RSM) has been studied in the prediction of the mechanical properties of self-healing concrete. Bio-concrete technology stimulated by the concentration of bacteria has been utilized as a sustainable structural concrete for the future of the built environment. This is due to the recovery tendency of the concrete structures after noticeable structural failures. However, it requires a somewhat expensive exercise and technology to create the medium for the growth of the bacteria needed for this self-healing ability. The method of data gathering, analysis and intelligent prediction has been adopted to propose parametric relationships between the bacteria usage and the concrete performance in terms of strength and durability. This makes is cheaper to design self-healing concrete structures based on the optimized mathematical relationships and models proposed from this exercise. The performance of the models was tested by using the coefficient of determination (R2), root mean squared errors, mean absolute errors, mean squared errors, variance accounted for and the coefficient of error. At the end of the prediction protocol and model performance evaluation, it was found that the classified metaheuristic techniques outclassed the RSM due their ability to mimic human and animal genetics of mutation. Furthermore, it can be finally remarked that the GWO outclassed the other methods in predicting the concrete slump (Sl) with R2 of 0.998 and 0.989 for the train and test, respectively, the PSO outclassed the rest in predicting the flexural strength with R2 of 0.989 and 0.937 for train and test, respectively and the MVO outclassed the others in predicting the compressive strength with R2 of 0.998 and 0.958 for train and test, respectively. |
| first_indexed | 2024-04-24T09:53:39Z |
| format | Article |
| id | doaj.art-c8cf6eb2079e40bfa5a1aca19cf550a5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T09:53:39Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c8cf6eb2079e40bfa5a1aca19cf550a52024-04-14T11:15:58ZengNature PortfolioScientific Reports2045-23222024-04-0114114010.1038/s41598-024-58666-8Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structuresKennedy C. Onyelowe0Ali F. H. Adam1Nestor Ulloa2Cesar Garcia3Alexis Ivan Andrade Valle4María Gabriela Zúñiga Rodríguez5Andrea Natali Zarate Villacres6Jamshid Shakeri7Lewechi Anyaogu8Mohammadreza Alimoradijazi9Nakkeeran Ganasen10Department of Civil Engineering, Michael Okpara University of AgricultureDepartment of Civil Engineering, College of Engineering TechnologiesFacultad de Mecánica, Escuela Superior Politécnica de Chimborazo (ESPOCH)Facultad de Ingeniería, Arquitectura, Universidad Nacional de Chimborazo (UNACH)Facultad de Ingeniería, Ingeniería Civil, Universidad Nacional de Chimborazo (UNACH)Facultad de Ingeniería, Ingeniería Civil, Universidad Nacional de Chimborazo (UNACH)Facultad de Ingeniería, Ingeniería Civil, Universidad Nacional de Chimborazo (UNACH)Department of Mining Engineering, Faculty of Engineering, Hamedan University of TechnologyDepartment of Civil Engineering, School of Engineering and Engineering Technology, Federal University of TechnologyDepartment of Computer Engineering, Faculty of Engineering, K. N. Toosi University of TechnologyDepartment of Civil Engineering, SRM Institute of Science and TechnologyAbstract In this research paper, the intelligent learning abilities of the gray wolf optimization (GWO), multi-verse optimization (MVO), moth fly optimization, particle swarm optimization (PSO), and whale optimization algorithm (WOA) metaheuristic techniques and the response surface methodology (RSM) has been studied in the prediction of the mechanical properties of self-healing concrete. Bio-concrete technology stimulated by the concentration of bacteria has been utilized as a sustainable structural concrete for the future of the built environment. This is due to the recovery tendency of the concrete structures after noticeable structural failures. However, it requires a somewhat expensive exercise and technology to create the medium for the growth of the bacteria needed for this self-healing ability. The method of data gathering, analysis and intelligent prediction has been adopted to propose parametric relationships between the bacteria usage and the concrete performance in terms of strength and durability. This makes is cheaper to design self-healing concrete structures based on the optimized mathematical relationships and models proposed from this exercise. The performance of the models was tested by using the coefficient of determination (R2), root mean squared errors, mean absolute errors, mean squared errors, variance accounted for and the coefficient of error. At the end of the prediction protocol and model performance evaluation, it was found that the classified metaheuristic techniques outclassed the RSM due their ability to mimic human and animal genetics of mutation. Furthermore, it can be finally remarked that the GWO outclassed the other methods in predicting the concrete slump (Sl) with R2 of 0.998 and 0.989 for the train and test, respectively, the PSO outclassed the rest in predicting the flexural strength with R2 of 0.989 and 0.937 for train and test, respectively and the MVO outclassed the others in predicting the compressive strength with R2 of 0.998 and 0.958 for train and test, respectively.https://doi.org/10.1038/s41598-024-58666-8Metaheuristic machine learning (MML)Response surface methodology (RSM)GWO, MVO, MFO, PSO, and WOABio-concreteBacteria concentrationSelf-healing concrete (SHC) |
| spellingShingle | Kennedy C. Onyelowe Ali F. H. Adam Nestor Ulloa Cesar Garcia Alexis Ivan Andrade Valle María Gabriela Zúñiga Rodríguez Andrea Natali Zarate Villacres Jamshid Shakeri Lewechi Anyaogu Mohammadreza Alimoradijazi Nakkeeran Ganasen Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structures Scientific Reports Metaheuristic machine learning (MML) Response surface methodology (RSM) GWO, MVO, MFO, PSO, and WOA Bio-concrete Bacteria concentration Self-healing concrete (SHC) |
| title | Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structures |
| title_full | Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structures |
| title_fullStr | Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structures |
| title_full_unstemmed | Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structures |
| title_short | Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structures |
| title_sort | modeling the influence of bacteria concentration on the mechanical properties of self healing concrete shc for sustainable bio concrete structures |
| topic | Metaheuristic machine learning (MML) Response surface methodology (RSM) GWO, MVO, MFO, PSO, and WOA Bio-concrete Bacteria concentration Self-healing concrete (SHC) |
| url | https://doi.org/10.1038/s41598-024-58666-8 |
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