Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Andrea Natalí Zárate Villacrés"'
Autor:
Alexis Iván Andrade Valle, Jefferson Javier Castillo Cevallos, Mayte Lisbeth Mieles Mariño, Marcelo David Guerra Valladares, Andrea Natalí Zárate Villacrés, María Gabriela Zúñiga Rodríguez, Cristian Andrés Marcillo Zapata, Tito Oswaldo Castillo Campoverde
Publikováno v:
F1000Research, Vol 13 (2024)
Background Geopolymers are alternative materials to cement because they require less energy in their production process; hence, they contribute to the reduction in CO2 emissions. This study aims to evaluate the possibility of using industrial residue
Externí odkaz:
https://doaj.org/article/c655b6ff5c90423aad453e968edd338a
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
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
Autor:
Xinghuang Guo, Cesar Garcia, Alexis Ivan Andrade Valle, Kennedy Onyelowe, Andrea Natali Zarate Villacres, Ahmed M Ebid, Shadi Hanandeh
Publikováno v:
PLoS ONE, Vol 19, Iss 4, p e0301075 (2024)
In the field of soil mechanics, especially in transportation and environmental geotechnics, the use of machine learning (ML) techniques has emerged as a powerful tool for predicting and understanding the compressive strength behavior of soils especia
Externí odkaz:
https://doaj.org/article/4666791a18c84bc6bfa2c52476235d11
Autor:
Xinghuang Guo, Cesar Garcia, Alexis Ivan Andrade Valle, Kennedy Onyelowe, Andrea Natali Zarate Villacres, Ahmed M. Ebid, Shadi Hanandeh
Publikováno v:
PLoS ONE, Vol 19, Iss 4 (2024)
Externí odkaz:
https://doaj.org/article/23e2a5a2462847bc8b440adf60cdc8f1