IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2022i1p641-d1019986.html
   My bibliography  Save this article

Machine Learning-Based Predictive Modeling of Sustainable Lightweight Aggregate Concrete

Author

Listed:
  • Fazal Hussain

    (Nust Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan)

  • Shayan Ali Khan

    (Nust Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan)

  • Rao Arsalan Khushnood

    (Nust Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan
    Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy)

  • Ameer Hamza

    (Nust Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan)

  • Fazal Rehman

    (Nust Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan)

Abstract

Nowadays, lightweight aggregate concrete is becoming more popular due to its versatile properties. It mainly helps to reduce the dead loads of the structure, which ultimately reduces design load requirements. The main challenge associated with lightweight aggregate concrete is finding an optimized mix per requirements. However, the conventional material design of this composite is quite costly, time-consuming, and iterative. This research proposes a simplified methodology for the mix designing of structural and non-structural lightweight aggregate concrete by incorporating machine learning. For this purpose, five distinct machine learning algorithms, support vector machine (SVM), artificial neural network (ANN), decision tree (DT), Gaussian process of regression (GPR), and extreme gradient boosting tree (XGBoost) algorithms, were investigated. For the training, testing, and validation process, a total of 420 data points were collected from 43 published journal articles. The performance of models was evaluated based on statistical performance indicators. Overall, 11 input parameters, including ingredients of the concrete mix and aggregate properties were entertained; the only output parameter was the compressive strength of lightweight concrete. The results revealed that the GPR model outperformed the remaining four machine learning models by attaining an R 2 value of 0.99, RMSE of 1.34, MSE of 1.79, and MAE of 0.69. In a nutshell, these simplified modern techniques can be employed to make the design of lightweight aggregate concrete easy without extensive experimentation.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:641-:d:1019986
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/641/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/1/641/
    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. Jesús de-Prado-Gil & Osama Zaid & Covadonga Palencia & Rebeca Martínez-García, 2022. "Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods," Mathematics, MDPI, vol. 10(13), pages 1-21, June.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    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. Araceli Queiruga-Dios & María Jesus Santos Sánchez & Fatih Yilmaz & Deolinda M. L. Dias Rasteiro & Jesús Martín-Vaquero & Víctor Gayoso Martínez, 2022. "Mathematics and Its Applications in Science and Engineering," Mathematics, MDPI, vol. 10(19), pages 1-2, September.
    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. 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:15:y:2022:i:1:p:641-:d:1019986. 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.