Optimización del rendimiento de morteros sostenibles con nanosílice mediante técnicas de inteligencia artificial

Mohammadfarid Alvansazyazdi; Michael David Tuarez Zambrano; Nathalia Cedeño Aldean; Nelson Salgado Reyes

doi:10.31243/id.v22.2026.3188

Autores/as

Mohammadfarid Alvansazyazdi Universidad Laica Eloy Alfaro de Manabí https://orcid.org/0000-0001-8797-5705
Michael David Tuarez Zambrano Universidad Laica Eloy Alfaro de Manabí https://orcid.org/0009-0009-8515-4044
Nathalia Cedeño Aldean Universidad Laica Eloy Alfaro de Manabí https://orcid.org/0009-0009-0226-3326
Nelson Salgado Reyes Instituto Universitario Japón https://orcid.org/0000-0001-8908-7613

DOI:

https://doi.org/10.31243/id.v22.2026.3188

Palabras clave:

nanosílice, morteros sostenibles, redes neuronales artificiales, materiales cementosos suplementarios, durabilidad, modelos predictivos.

Resumen

La industria de la construcción enfrenta una creciente presión para reducir su impacto ambiental sin comprometer los estándares de desempeño estructural. Esta revisión sistemática de literatura examina el papel combinado de la nanosílice (nSiO₂) como material cementicio suplementario y la inteligencia artificial (IA) como herramienta predictiva y de optimización en el diseño de morteros de cemento sostenibles. Siguiendo el protocolo PRISMA 2020, se seleccionaron 112 estudios revisados por pares, publicados entre 2012 y 2025, provenientes de Scopus, Web of Science, ScienceDirect, SpringerLink y Google Scholar. La evidencia demuestra que la incorporación de nSiO₂ en dosificaciones de entre 2.5% y 5% en peso del material cementicio mejora consistentemente la resistencia a compresión, la resistencia a tracción y los indicadores de durabilidad, incluyendo reducciones de absorción de agua de hasta 21.8% y mayor resistencia a la penetración de cloruros y a la exposición térmica de hasta 800°C. Estas mejoras se atribuyen a los mecanismos pozzolánico y de nucleación de la nanosílice, que densifican el gel C-S-H y refinan la porosidad capilar. Además, los modelos híbridos de IA que combinan redes neuronales artificiales con algoritmos de optimización metaheurística alcanzan precisiones de predicción superiores a R² = 0.97, reduciendo significativamente los costos del diseño experimental. La convergencia entre nanotecnología e inteligencia computacional establece un marco sólido para el desarrollo de materiales cementosos de alto desempeño y ambientalmente responsables.

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