Zobrazeno 1 - 10
of 7 831
pro vyhledávání: '"KENNEDY, C"'
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:
Ahmed M. Ebid, Nestor Ulloa, Kennedy C. Onyelowe, Maria Gabriela Zuñiga Rodriguez, Alexis Iván Andrade Valle, Andrea Natali Zárate Villacres
Publikováno v:
Cogent Engineering, Vol 11, Iss 1 (2024)
To address the growing concerns about the environmental impact and construction costs, there has been an increasing interest in the use of recycled aggregates in concrete applications. Among the mechanical properties of concrete, compressive strength
Externí odkaz:
https://doaj.org/article/c6e249c527bb4437ba06dc25c9e24986
Publikováno v:
Cogent Engineering, Vol 11, Iss 1 (2024)
AbstractFilling ability is one of the prominent rheological properties of the self-compacting concrete (SCC), which has been studied in this research work deploying the functional behavior of the concrete through the Orimet apparatus using the couple
Externí odkaz:
https://doaj.org/article/e8ed182affa7433a8143e91154d03b9c
Autor:
Luis Velastegui, Nancy Velasco, Hugo Rolando Sanchez Quispe, Fredy Barahona, Kennedy C. Onyelowe, Shadi Hanandeh, Ahmed M. Ebid, TrustGod A. John
Publikováno v:
Frontiers in Built Environment, Vol 10 (2024)
The structural design standards, particularly in concrete technology, heavily rely on the mechanical attributes of concrete. Utilizing dependable predictive models for these properties can minimize the need for extensive laboratory testing, evaluatio
Externí odkaz:
https://doaj.org/article/74b8d99d737f40df94ad989b0fbf20fb
Autor:
Néstor Ulloa, Kennedy C. Onyelowe, Ahmed M. Ebid, Carlos Santiago Curay Yaulema, Maia Gabriela Zuiga Rodguez, Aleis Ivan Adrade Vally, Michael E. Onyia
Publikováno v:
Frontiers in Built Environment, Vol 10 (2024)
The compressive strength behavior of high-strength geopolymer concrete (HSGPC) has been studied in this research work with varying alkali concentration using the novel machine learning techniques. The alkali concentration in the activation solution p
Externí odkaz:
https://doaj.org/article/9bdb6b65a74443a8a28c9ca90f0437f5