Zobrazeno 1 - 10
of 3 800
pro vyhledávání: '"Kennedy, C. A."'
Autor:
Majed Alzara, Kennedy C. Onyelowe, Ahmed M. Ebid, Shadi Hanandeh, Ahmed M. Yosri, Talal O. Alshammari
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-29 (2024)
Abstract The California bearing ratio (CBR) of a granular materials are influence by the soil particle distribution indices such as D10, D30, D50, and D60 and also the compaction properties such as the maximum dry density (MDD) and the optimum moistu
Externí odkaz:
https://doaj.org/article/9a927bd0f5f6411483d8915d326873ae
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-31 (2024)
Abstract Particle size is considered one of the significant characteristics used in geotechnical practices. Traditionally, sieve analysis is utilized for coarse-grained soil. However, this method could be time consuming and take much effort, especial
Externí odkaz:
https://doaj.org/article/dc153da7d72d48bcb5445f7a674a1ecf
Autor:
Aishwarya Sathyanarayanan, Balasubramanian Murugesan, Narayanamoorthi Rajamanickam, Christian Ordoñez, Kennedy C. Onyelowe, Nestor Ulloa
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-21 (2024)
Abstract Solar energy is the most promising source for generating residential, commercial, and industrial electricity. However, solar panels should be eco-friendly to increase sustainability during manufacturing and recycling. This study investigates
Externí odkaz:
https://doaj.org/article/d342fbfc125b40dbac6c091e76750554
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Steel construction is increasingly using thin-walled profiles to achieve lighter, more cost-effective structures. However, analyzing the behavior of these elements becomes very complex due to the combined effects of local buckling in the thi
Externí odkaz:
https://doaj.org/article/92d020a171dd41b189039ff4a4ae5032
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-27 (2024)
Abstract In this work, intelligent numerical models for the prediction of debris flow susceptibility using slope stability failure factor of safety (FOS) machine learning predictions have been developed. These machine learning techniques were trained
Externí odkaz:
https://doaj.org/article/8f004291eb684a4a952e02aff208b100
Autor:
Kennedy C. Onyelowe, Arif Ali Baig Moghal, Ahmed Ebid, Ateekh Ur Rehman, Shadi Hanandeh, Vishnu Priyan
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-29 (2024)
Abstract It has been imperative to study and stabilize cohesive soils for use in the construction of pavement subgrade and compacted landfill liners considering their unconfined compressive strength (UCS). As long as natural cohesive soil falls below
Externí odkaz:
https://doaj.org/article/bf609a1e78dd491688506f365f8da5f2
Autor:
Carlos Roberto López Paredes, Cesar García, Kennedy C. Onyelowe, Maria Gabriela Zuniga Rodriguez, Tammineni Gnananandarao, Alexis Ivan Andrade Valle, Nancy Velasco, Greys Carolina Herrera Morales
Publikováno v:
Frontiers in Built Environment, Vol 10 (2024)
Industrial wastes have found great use in the built environment due to the role they play in the sustainable infrastructure development especially in green concrete production. In this research investigation, the impact of wastes from the industry on
Externí odkaz:
https://doaj.org/article/1cb85e7fab8542f58b75fc20e816d4d2
Autor:
Cesar Garcia, Alexis Ivan Andrade Valle, Sabih Hashim Muhodir, Kennedy C. Onyelowe, Hamza Imran, Sadiq N. Henedy, Bala Mahesh Chilakala, Manvendra Verma
Publikováno v:
Frontiers in Built Environment, Vol 10 (2024)
The production of geopolymer concrete (GPC) with the addition of industrial wastes as the formulation base is of interest to sustainable built environment. However, repeated experimental trials costs a huge budget, hence the prediction and validation
Externí odkaz:
https://doaj.org/article/6e7997b900cf42e59818ee11eebb2fab
Autor:
Hassan, Rana1 (AUTHOR) rar.hassan@zu.edu.eg, Onyelowe, Kennedy C.2,3 (AUTHOR) konyelowe@mouau.edu.ng, Zamel, Amr A.4 (AUTHOR) amrzamel@eng.zu.edu.eg
Publikováno v:
Scientific Reports. 9/30/2024, Vol. 14 Issue 1, p1-31. 31p.
Autor:
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
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-40 (2024)
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 te
Externí odkaz:
https://doaj.org/article/c8cf6eb2079e40bfa5a1aca19cf550a5