Properties Prediction of Environmentally Friendly Ultra-High-Performance Concrete using Artificial Neural Networks

Abellán García, Joaquín and Fernández Gómez, Jaime Antonio and Torres Castellanos, Nancy (2020). Properties Prediction of Environmentally Friendly Ultra-High-Performance Concrete using Artificial Neural Networks. "European Journal of Environmental and Civil Engineering" ; pp. 1-20. ISSN 1964-8189. https://doi.org/10.1080/19648189.2020.1762749.

Description

Title: Properties Prediction of Environmentally Friendly Ultra-High-Performance Concrete using Artificial Neural Networks
Author/s:
  • Abellán García, Joaquín
  • Fernández Gómez, Jaime Antonio
  • Torres Castellanos, Nancy
Item Type: Article
Título de Revista/Publicación: European Journal of Environmental and Civil Engineering
Date: 15 May 2020
ISSN: 1964-8189
Subjects:
Freetext Keywords: UHPC, ANN, Compressive Strength, Slump Flow, SCM, Virtual Packing Density
Faculty: E.T.S.I. Caminos, Canales y Puertos (UPM)
Department: Ingeniería Civil: Construcción
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Ultra-high-performance concrete (UHPC) results from the mixture of several constituents, leading to a highly complex material in both, fresh and hardened state. The higher number of constituents, together with a higher number of possible combinations, relative proportioning and characteristics, makes the behavior of this type of concrete more difficult to predict. The objective of the research is to build four analytical models, based on artificial neural networks (ANN), to predict the 1-day, 7-day, and 28-day compressive strengths and slump flow. Recycled glass powder milled to different particle size, fluid catalytic cracking residue (FCC) and different particle size limestone powder was used as partial replacements for Portland cement and silica fume. The ANN models predicted the 1-day, 7- day, and 28-day compressive strengths and slump flow of the test set with prediction error values (RMSE) of 2.400MPa, 2.638MPa, 2.064MPa and 7.245mm respectively. The results indicated that the developed ANN models are an efficient tool for predicting the slump flow and compressive strengths of UHPC while incorporating silica fume, limestone powder, recycled glass powder and FCC.

More information

Item ID: 65091
DC Identifier: http://oa.upm.es/65091/
OAI Identifier: oai:oa.upm.es:65091
DOI: 10.1080/19648189.2020.1762749
Official URL: https://www.tandfonline.com/doi/full/10.1080/19648189.2020.1762749?scroll=top&needAccess=true
Deposited by: Memoria Investigacion
Deposited on: 02 Nov 2020 15:27
Last Modified: 02 Nov 2020 15:27
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