ALVAREZ ARELLANO, JUAN ANTONIO
INGENIERÍA · Nivel 1
Desde 2005 es PROFESOR en UNIVERSIDAD AUTÓNOMA DEL CARMEN
Juan Antonio Álvarez Arellano is a Civil Engineer from the Instituto Tecnológico de Villahermosa, Tabasco, Mexico, graduating in 2001. In 2004, he obtained a Master's degree in Science with a Specialization in Structures from the Graduate Studies and Research Section...
Juan Antonio Álvarez Arellano is a Civil Engineer from the Instituto Tecnológico de Villahermosa, Tabasco, Mexico, graduating in 2001. In 2004, he obtained a Master's degree in Science with a Specialization in Structures from the Graduate Studies and Research Section of the School of Engineering and Architecture at the Instituto Politécnico Nacional, Mexico. In 2012, Dr. Álvarez obtained a PhD in Structural Engineering from the Universidad Autónoma Metropolitana, Azcapotzalco Unit. Since 2005, he has been a professor-researcher at the Universidad Autónoma del Carmen, currently holding the position of Professor-Researcher. Since 2002, he has been involved in Wind Engineering, conducting research related to wind actions on structures using numerical methods applied to Wind Engineering (Computational Wind Engineering), as well as experimental methods in wind tunnels and smoke tunnels.
He served as the Director of the Faculty of Engineering at UNACAR, Mexico from 2016 to 2023. Additionally, he has been a reviewer for journals and academic projects within PRODEP and CONAHCYT, Mexico.
He has taught various courses at both undergraduate and postgraduate levels, including Wind Engineering, Seismic Engineering, Reinforced Concrete I, Reinforced Concrete II, Steel Structures, Structural Analysis I, Structural Analysis II, Mechanics of Solids I and Laboratory, Mechanics of Solids II and Laboratory, Prestressed Concrete, Early Research Training Workshop, Probability and Statistics; Master's: Construction of Concrete Structures, Construction of Steel Structures, Thesis Seminar I, Thesis Seminar II, Thesis Development; PhD: Thesis Seminars and Dimensional Analysis. His main research interests or areas of focus are: (1) Structures and advanced materials technology, and (2) Technological development, innovation, and automated learning in Civil Engineering.
His research is oriented towards Wind Engineering, Structural Mechanics, and technological development.
He is a member of the Colegio de Ingenieros Civiles del Carmen A.C. and the Sociedad Mexicana de Ingeniería Estructural A.C., currently serving as the president of the Representation of the State of Campeche.
Información*
| Nombre | JUAN ANTONIO ALVAREZ ARELLANO |
|---|---|
| Área | INGENIERÍAS Y DESARROLLO TECNOLÓGICO |
| Campo | INGENIERÍA |
| Disciplina | INGENIERÍA CIVIL |
| Especialidad | ESTRUCTURAS |
| CVU | 273636 |
| Institución | |
|---|---|
| Dependencia | FACULTAD DE INGENIERIA |
| Entidad | CAMPECHE |
| Nivel | 1 |
| Vigencia | Inicio: 01/01/2022 |
| Fin: 31/12/2026 |
* Información del primer trimestre de 2026.
Fuente: SECIHTI.
Publicaciones ORCID
- 2025
- Artisanal fabrication of prismatic concrete brick units using a modular mold covered by utility model MX 4516 Revista de Ingeniería Mecánica
- Mathematical-model–based alternative for particle size distribution characterization of construction materials from Campeche, México Journal of Quantitative and Statistical Analysis
- Microcrack evaluation using modal analysis under two loading conditions on rotodynamic shafts Journal Mathematical and Quantitative Methods
- Assessment of Structural Integrity Through On-Site Decision-Making Analysis for a Jacket-Type Offshore Platform Applied Sciences
- 2024
- In-situ analysis of an intermodular connection for segmented offshore platforms Revista de Ingenieria Innovativa
- Strength analysis of structural concrete blocks with five mixture materials Revista de Ingeniería Civil
- Structural modeling of steel profiles to infer preventive maintenance in educational infrastructure
- 2023
- Analysis of the compressive strength of commercial concrete blocks for structural masonry in Ciudad del Carmen, Campeche
- 0
- Neural Networks for Statistical Modeling: Achieving Highly Accurate Chemical Oxygen Demand Prediction in Herbicide-Contaminated Wastewater