Revista UIS Ingenierías

Vol. 25 Núm. 2 (2026): Revista UIS Ingenierías
Artículos

Cuantificación de la incertidumbre mediante redes bayesianas en la planificación temprana de proyectos de vivienda de interés social

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  • Guillermo Mejía-Aguilar,
  • Jaime Gutiérrez-Prada,
  • Oscar Portilla-Carreño,
  • Jonathan Soto-Paz
Guillermo Mejía-Aguilar
Universidad Industrial de Santander
Jaime Gutiérrez-Prada
Universidad Industrial de Santander
Oscar Portilla-Carreño
Universidad Industrial de Santander
Jonathan Soto-Paz
Universidad del Valle
DOI: https://doi.org/10.18273/revuin.v25n2-2026006

Publicado 2026-05-29

Palabras clave

  • Redes Bayesianas,
  • Índice de Definición de Proyectos (PDRI),
  • Cuantificación de la Incertidumbre,
  • Planificación Temprana (Front-End Planning),
  • Proyectos de vivienda de interés social,
  • Modelado probabilístico,
  • Evaluación de riesgos,
  • MICMAC,
  • Conocimiento de expertos,
  • Toma de decisiones
    • ...Más
    Menos

Cómo citar

Mejía-Aguilar, G., Gutiérrez-Prada, J., Portilla-Carreño, O., & Soto-Paz, J. (2026). Cuantificación de la incertidumbre mediante redes bayesianas en la planificación temprana de proyectos de vivienda de interés social. Revista UIS Ingenierías, 25(2), 61–80. https://doi.org/10.18273/revuin.v25n2-2026006

Derechos de autor 2026 Revista UIS Ingenierías

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Resumen

La Planificación de la Etapa Inicial (FEP) y herramientas tradicionales como el Índice de Definición de Proyectos (PDRI) son fundamentales para reducir riesgos y estructurar evaluaciones, pero su enfoque determinista limita la consideración de la incertidumbre y de las interdependencias entre variables. Este estudio propone una metodología probabilística basada en Redes Bayesianas (BNs) para cuantificar la incertidumbre en las fases tempranas de planificación. El modelo integra datos empíricos de 14 proyectos de vivienda de interés social y conocimiento experto, lo que permite análisis basados en escenarios que respaldan la toma de decisiones iniciales. Entrenado con el algoritmo de Expectación-Maximización, el modelo alcanzó una precisión predictiva aceptable (R² = 0.76; MSE = 0.214;               RMSE = 0.463). Los resultados muestran que las BNs mejoran la fiabilidad de la planificación y facilitan la mitigación proactiva de riesgos al incorporar dinámicamente la incertidumbre. La metodología cierra la brecha entre herramientas deterministas y sistemas probabilísticos de apoyo a la decisión, ofreciendo un marco escalable y replicable para la planificación temprana en proyectos de vivienda de interés social y otros contextos constructivos de alta incertidumbre.

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