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Predicting the Effect of Fly Ash on Concrete’s Mechanical Properties by ANN

Author

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  • Mohammad Mehdi Roshani

    (Department of Civil Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah 6718997551, Iran)

  • Seyed Hamidreza Kargar

    (Department of Civil Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah 6718997551, Iran)

  • Visar Farhangi

    (Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USA)

  • Moses Karakouzian

    (Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USA)

Abstract

Fly ash, as a supplemental pozzolanic material, reduces concrete’s adverse environmental footprint by decreasing the emission of carbon dioxide (CO 2 ) during the cement manufacturing process. Fly ash, which is a waste material, can enhance both the mechanical characteristics and durability of concrete, and has the capability to play an important role in sustainable design. Considering the widespread interest in applying Fly ash, and despite research studies, the level of replacement is still unclear. In this paper, a novel method using artificial neural networks (ANN) is presented to predict concrete’s mechanical characteristics by adding Fly ash. In this regard, a host of available experimental data, such as the properties of Fly ash, along with concrete additives, was fed into an ANN model. Concrete samples’ tensile and compressive strengths, in addition to their modulus of elasticity, were defined as outputs. It was observed that the predicted outcomes agreed well with the experimental results. To further enhance the research outcomes, simple but practical equations are presented to assess the effect of using Fly ash on concrete’s mechanical characteristics.

Suggested Citation

  • Mohammad Mehdi Roshani & Seyed Hamidreza Kargar & Visar Farhangi & Moses Karakouzian, 2021. "Predicting the Effect of Fly Ash on Concrete’s Mechanical Properties by ANN," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1469-:d:490430
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    References listed on IDEAS

    as
    1. Mohammad Reza Mahmoudi & Mohsen Maleki & Abbas Pak, 2018. "Testing the equality of two independent regression models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(12), pages 2919-2926, June.
    2. Priyanka Morla & Rishi Gupta & Peiman Azarsa & Ashutosh Sharma, 2021. "Corrosion Evaluation of Geopolymer Concrete Made with Fly Ash and Bottom Ash," Sustainability, MDPI, vol. 13(1), pages 1-16, January.
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    Cited by:

    1. Amr El-said & Ahmed Awad & Mahmood Ahmad & Mohanad Muayad Sabri Sabri & Ahmed Farouk Deifalla & Maged Tawfik, 2022. "The Mechanical Behavior of Sustainable Concrete Using Raw and Processed Sugarcane Bagasse Ash," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    2. Xuhong Yang & Haoxu Fang & Yaxiong Wu & Wei Jia, 2022. "RBF Neural Network Fractional-Order Sliding Mode Control with an Application to Direct a Three Matrix Converter under an Unbalanced Grid," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    3. Sergiu-Mihai Alexa-Stratulat & Daniel Covatariu & Ana-Maria Toma & Ancuta Rotaru & Gabriela Covatariu & Ionut-Ovidiu Toma, 2022. "Influence of a Novel Carbon-Based Nano-Material on the Thermal Conductivity of Mortar," Sustainability, MDPI, vol. 14(13), pages 1-14, July.

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