IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i15p8561-d606007.html
   My bibliography  Save this article

A New Formulation to Estimate the Elastic Modulus of Recycled Concrete Based on Regression and ANN

Author

Listed:
  • Emerson Felipe Felix

    (Department of Structural Engineering, University of São Paulo at São Carlos School of Engineering, São Carlos 13566-590, Brazil)

  • Edna Possan

    (Latin American Institute of Technology, Infrastructure and Territory, Federal University of Latin American Integration, Foz do Iguaçu 85866-000, Brazil)

  • Rogério Carrazedo

    (Department of Structural Engineering, University of São Paulo at São Carlos School of Engineering, São Carlos 13566-590, Brazil)

Abstract

A new formulation to estimate the elastic modulus of concrete containing recycled coarse aggregate is proposed in this work using artificial neural networks (ANN) and nonlinear regression. Up to six predictors variables were used to training 243 ANN. The models were generated based on results obtained from experimental campaigns. Feedforward neural network and Levenberg–Marquardt back propagation algorithm were used for training the ANN. The best ANN was found with the architecture 6-4-2-1 (input -1st hidden layer -2nd hidden layer -output), attaining a root-mean-square error of 2.4 GPa associated with a coefficient of determination of 0.91. Once the ANN model was established, 46,656 concrete samples were created. These were employed to formulate the model using nonlinear regression. The developed model showed a highly efficient performance to predict the elastic modulus. Lastly, considering the parametric study conducted, the results pointed out that the approach can be applied to predict the concrete elastic modulus and can indicate better mix proportions for concretes containing natural and/or recycled coarse aggregates, enabling its use as a simulation tool in the development of engineering projects focused on durability and sustainability.

Suggested Citation

  • Emerson Felipe Felix & Edna Possan & Rogério Carrazedo, 2021. "A New Formulation to Estimate the Elastic Modulus of Recycled Concrete Based on Regression and ANN," Sustainability, MDPI, vol. 13(15), pages 1-21, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8561-:d:606007
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/15/8561/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/15/8561/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tawfiq Al-Mughanam & Theyazn H. H. Aldhyani & Belal Alsubari & Mohammed Al-Yaari, 2020. "Modeling of Compressive Strength of Sustainable Self-Compacting Concrete Incorporating Treated Palm Oil Fuel Ash Using Artificial Neural Network," Sustainability, MDPI, vol. 12(22), pages 1-13, November.
    2. Sara Boudali & Bahira Abdulsalam & Amir Hossein Rafiean & Sébastien Poncet & Ahmed Soliman & Adel ElSafty, 2021. "Influence of Fine Recycled Concrete Powder on the Compressive Strength of Self-Compacting Concrete (SCC) Using Artificial Neural Network," Sustainability, MDPI, vol. 13(6), pages 1-28, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Celal Cakiroglu & Gebrail Bekdaş, 2023. "Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods," Sustainability, MDPI, vol. 15(6), pages 1-21, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohammed A. Mansour & Mohd Hanif Bin Ismail & Qadir Bux alias Imran Latif & Abdullah Faisal Alshalif & Abdalrhman Milad & Walid Abdullah Al Bargi, 2023. "A Systematic Review of the Concrete Durability Incorporating Recycled Glass," Sustainability, MDPI, vol. 15(4), pages 1-33, February.
    2. Elham Alzain & Shaha Al-Otaibi & Theyazn H. H. Aldhyani & Ali Saleh Alshebami & Mohammed Amin Almaiah & Mukti E. Jadhav, 2023. "Revolutionizing Solar Power Production with Artificial Intelligence: A Sustainable Predictive Model," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    3. Fazal Hussain & Shayan Ali Khan & Rao Arsalan Khushnood & Ameer Hamza & Fazal Rehman, 2022. "Machine Learning-Based Predictive Modeling of Sustainable Lightweight Aggregate Concrete," Sustainability, MDPI, vol. 15(1), pages 1-22, December.
    4. Kaiyue Zhao & Peng Zhang & Bing Wang & Yupeng Tian & Shanbin Xue & Yuan Cong, 2021. "Preparation of Electric- and Magnetic-Activated Water and Its Influence on the Workability and Mechanical Properties of Cement Mortar," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    5. 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.
    6. Zhenwen Hu & Zhe Kong & Guisheng Cai & Qiuyi Li & Yuanxin Guo & Dunlei Su & Junzhe Liu & Shidong Zheng, 2021. "Study of the Properties of Full Component Recycled Dry-Mixed Masonry Mortar and Concrete Prepared from Construction Solid Waste," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    7. Mosleh Hmoud Al-Adhaileh & Fawaz Waselallah Alsaade, 2021. "Modelling and Prediction of Water Quality by Using Artificial Intelligence," Sustainability, MDPI, vol. 13(8), pages 1-18, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8561-:d:606007. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.