Exploración con redes neuronales artificiales para estimar la resistencia a la compresión, en concretos fibroreforzados con acero

  • Luis Octavio González Salcedo Universidad del Valle
  • Aydée Patricia Guerrero Zúñiga Universidad del Valle
  • Silvio Delvasto Arjona Universidad del Valle
  • Adrián Luis Ernesto Will Universidad Tecnología Nacional
Palabras clave: resistencia a la compresión, concreto reforzado con fibras, fibra de acero, predicción, inteligencia artificial, redes neuronales artificiales

Resumen

En diseño y construcción de estructuras de concreto, la resistencia a la compresión a 28 días de curado es la especificación de control de estabilidad de la obra. La inclusión de fibras como reforzamiento de la matriz cementicia permite una ganancia en sus propiedades, además de obtener un material de alto desempeño. En las normativas, se plantean formulaciones predictivas de la resistencia a la compresión basadas en unos pocos parámetros de composición del concreto, tales como la relación agua/cemento y el contenido de cemento Portland. Por otra parte, también se han planteado métodos de diseños de concreto para definir la ponderación de sus materiales componentes, teniendo como referencia la resistencia a la compresión del concreto simple. Además, las redes neuronales artificiales, como un símil de las neuronas biológicas, han sido utilizadas como herramientas de predicción de la resistencia a la compresión en el concreto, también con referencia al concreto simple, sin reforzamiento con fibras. Los antecedentes en este uso muestran que es interesante desarrollar aplicaciones en los concretos reforzados con fibras. En el presente trabajo se elaboraron redes neuronales artificiales para predecir la resistencia a la compresión en concretos reforzados con fibras de acero. Los resultados de los indicadores de desempeño mostraron que las redes neuronales artificiales elaboradas pueden realizar una aproximación adecuada al valor real de la propiedad mecánica.

Biografía del autor/a

Luis Octavio González Salcedo, Universidad del Valle

Ing. Civil, M.Sc., Profesor Asociado, Departamento de Ingeniería, Grupo de Investigación en Materiales y Medio Ambiente. Facultad de Ingeniería y Administración, Universidad Nacional de Colombia sede Palmira; Grupo de Materiales Compuestos, Facultad de Ingeniería, Universidad del Valle, Cali, Colombia.

Aydée Patricia Guerrero Zúñiga, Universidad del Valle

Ing. Civil, MSc., Ph.D., Profesora Titular, Escuela de Geomática e Ingeniería Civil, Grupo de Investigación en Ingeniería Sísmica, Ingeniería Eólica y Estructuras Inteligentes,
Facultad de Ingeniería, Universidad del Valle, Cali, Colombia.

Silvio Delvasto Arjona, Universidad del Valle

Ing. Químico, MSc., Ph.D., Profesor Titular, Escuela de Ingeniería de Materiales, Grupo de Materiales Compuestos, Facultad de Ingeniería, Universidad del Valle, Cali, Colombia.

Adrián Luis Ernesto Will, Universidad Tecnología Nacional

Matemático, Ph.D., Departamento de Matemáticas, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán; Centro de Investigación en Tecnologías Avanzadas de Tucumán, Universidad Tecnología Nacional – Facultad Regional Tucumán, San Miguel de Tucumán, Argentina.

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Biografía del autor/a

Luis Octavio González Salcedo, Universidad del Valle

Ing. Civil, M.Sc., Profesor Asociado, Departamento de Ingeniería, Grupo de Investigación en Materiales y Medio Ambiente. Facultad de Ingeniería y Administración, Universidad Nacional de Colombia sede Palmira; Grupo de Materiales Compuestos, Facultad de Ingeniería, Universidad del Valle, Cali, Colombia.

Aydée Patricia Guerrero Zúñiga, Universidad del Valle

Ing. Civil, MSc., Ph.D., Profesora Titular, Escuela de Geomática e Ingeniería Civil, Grupo de Investigación en Ingeniería Sísmica, Ingeniería Eólica y Estructuras Inteligentes,
Facultad de Ingeniería, Universidad del Valle, Cali, Colombia.

Silvio Delvasto Arjona, Universidad del Valle

Ing. Químico, MSc., Ph.D., Profesor Titular, Escuela de Ingeniería de Materiales, Grupo de Materiales Compuestos, Facultad de Ingeniería, Universidad del Valle, Cali, Colombia.

Adrián Luis Ernesto Will, Universidad Tecnología Nacional

Matemático, Ph.D., Departamento de Matemáticas, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán; Centro de Investigación en Tecnologías Avanzadas de Tucumán, Universidad Tecnología Nacional – Facultad Regional Tucumán, San Miguel de Tucumán, Argentina.

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Cómo citar
González Salcedo, L. O., Guerrero Zúñiga, A. P., Delvasto Arjona, S., & Ernesto Will, A. L. (2012). Exploración con redes neuronales artificiales para estimar la resistencia a la compresión, en concretos fibroreforzados con acero. Ciencia E Ingeniería Neogranadina, 22(1), 19–41. https://doi.org/10.18359/rcin.247
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2012-06-01
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