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Modeling of Compressive Strength of Sustainable Self-Compacting Concrete Incorporating Treated Palm Oil Fuel Ash Using Artificial Neural Network

Author

Listed:
  • Tawfiq Al-Mughanam

    (Mechanical Engineering Department, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia)

  • Theyazn H. H. Aldhyani

    (Community College of Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia)

  • Belal Alsubari

    (Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
    Department of Civil Engineering, Miami College of Henan University, Jinming Ave No.1, Kaifeng 475000, Henan, China)

  • Mohammed Al-Yaari

    (Chemical Engineering Department, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia)

Abstract

The utilization of a high-volume of treated palm oil fuel ash (T-POFA) as a partial cement substitution is one of the solutions presented to reduce carbon dioxide emissions (CO 2 ) and improve concrete sustainability. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is adapted as an artificial neural network (ANN) modeling tool to predict the compressive strength of self-compacting concrete (SCC) containing T-POFA. The ANFIS model has been developed and validated using concrete mixtures incorporating 0%, 10 wt%, 20 wt%, 30 wt%, 50 wt%, 60 wt%, and wt 70% T-POFA as a replacement of ordinary Portland cement (OPC) at a constant water/binder (W/B) ratio of 0.35. The experimental data were divided into 70% training data and 30% testing data. The experimental results of self-compacting concrete (SCC) containing T-POFA ensured comparable or higher compressive strengths, especially at later ages, when compared to the control SCC. However, the prediction results of the compressive strength of SCC samples using the ANFIS model are very close to the experimental values. The developed ANFIS model showed a highly-efficient performance to predict the SCC compressive strength. In addition, the obtained accurate predicted results using the developed ANN model will significantly affect the current experimental protocols, especially for costly and unsafe experiments.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9322-:d:442660
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    Citations

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    Cited by:

    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. 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.
    3. 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.
    4. 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.
    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. 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.

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