IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i4p1691-d493409.html
   My bibliography  Save this article

Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

Author

Listed:
  • Xu Huang

    (Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B152TT, UK)

  • Jiaqi Zhang

    (Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B152TT, UK)

  • Jessada Sresakoolchai

    (Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B152TT, UK)

  • Sakdirat Kaewunruen

    (Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B152TT, UK)

Abstract

Not only can waste rubber enhance the properties of concrete (e.g., its dynamic damping and abrasion resistance capacity), its rational utilisation can also dramatically reduce environmental pollution and carbon footprint globally. This study is the world’s first to develop a novel machine learning-aided design and prediction of environmentally friendly concrete using waste rubber, which can drive sustainable development of infrastructure systems towards net-zero emission, which saves time and cost. In this study, artificial neuron networks (ANN) have been established to determine the design relationship between various concrete mix composites and their multiple mechanical properties simultaneously. Interestingly, it is found that almost all previous studies on the ANNs could only predict one kind of mechanical property. To enable multiple mechanical property predictions, ANN models with various architectural algorithms, hidden neurons and layers are built and tailored for benchmarking in this study. Comprehensively, all three hundred and fifty-three experimental data sets of rubberised concrete available in the open literature have been collected. In this study, the mechanical properties in focus consist of the compressive strength at day 7 (CS7), the compressive strength at day 28 (CS28), the flexural strength (FS), the tensile strength (TS) and the elastic modulus (EM). The optimal ANN architecture has been identified by customising and benchmarking the algorithms (Levenberg–Marquardt (LM), Bayesian Regularisation (BR) and Scaled Conjugate Gradient (SCG)), hidden layers (1–2) and hidden neurons (1–30). The performance of the optimal ANN architecture has been assessed by employing the mean squared error (MSE) and the coefficient of determination ( R 2 ) . In addition, the prediction accuracy of the optimal ANN model has ben compared with that of the multiple linear regression (MLR).

Suggested Citation

  • Xu Huang & Jiaqi Zhang & Jessada Sresakoolchai & Sakdirat Kaewunruen, 2021. "Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete," Sustainability, MDPI, vol. 13(4), pages 1-26, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:1691-:d:493409
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/4/1691/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/4/1691/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. K. Gnana Sheela & S. N. Deepa, 2013. "Review on Methods to Fix Number of Hidden Neurons in Neural Networks," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, June.
    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.

    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. Martín Pensado-Mariño & Lara Febrero-Garrido & Pablo Eguía-Oller & Enrique Granada-Álvarez, 2021. "Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    2. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    3. Luis Alfonso Menéndez García & Fernando Sánchez Lasheras & Paulino José García Nieto & Laura Álvarez de Prado & Antonio Bernardo Sánchez, 2020. "Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms," Mathematics, MDPI, vol. 8(12), pages 1-22, December.
    4. Ayub, Yousaf & Hu, Yusha & Ren, Jingzheng, 2023. "Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models," Renewable Energy, Elsevier, vol. 215(C).
    5. Wang, Sen & Qin, Chaoxu & Feng, Qihong & Javadpour, Farzam & Rui, Zhenhua, 2021. "A framework for predicting the production performance of unconventional resources using deep learning," Applied Energy, Elsevier, vol. 295(C).
    6. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    7. Warut Pannakkong & Thanyaporn Harncharnchai & Jirachai Buddhakulsomsiri, 2022. "Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models," Energies, MDPI, vol. 15(9), pages 1-21, April.
    8. Anh-Tu Nguyen & Shih-Hao Lu & Phuc Thanh Thien Nguyen, 2021. "Validating and Forecasting Carbon Emissions in the Framework of the Environmental Kuznets Curve: The Case of Vietnam," Energies, MDPI, vol. 14(11), pages 1-38, May.
    9. Osamah Basheer Shukur & Muhammad Hisyam Lee, 2015. "Imputation of Missing Values in Daily Wind Speed Data Using Hybrid AR-ANN Method," Modern Applied Science, Canadian Center of Science and Education, vol. 9(11), pages 1-1, October.
    10. Liébana-Cabanillas, Francisco & Marinković, Veljko & Kalinić, Zoran, 2017. "A SEM-neural network approach for predicting antecedents of m-commerce acceptance," International Journal of Information Management, Elsevier, vol. 37(2), pages 14-24.
    11. Jihoon Moon & Sungwoo Park & Seungmin Rho & Eenjun Hwang, 2019. "A comparative analysis of artificial neural network architectures for building energy consumption forecasting," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    12. Muhammad Noman Shafique & Ammar Rashid & Sook Fern Yeo & Umar Adeel, 2023. "Transforming Supply Chains: Powering Circular Economy with Analytics, Integration and Flexibility Using Dual Theory and Deep Learning with PLS-SEM-ANN Analysis," Sustainability, MDPI, vol. 15(15), pages 1-23, August.
    13. Li, Hangxin & Wang, Shengwei, 2022. "Two-time-scale coordinated optimal control of building energy systems for demand response considering forecast uncertainties," Energy, Elsevier, vol. 253(C).
    14. Maiorino, Angelo & Del Duca, Manuel Gesù & Aprea, Ciro, 2022. "ART.I.CO. (ARTificial Intelligence for COoling): An innovative method for optimizing the control of refrigeration systems based on Artificial Neural Networks," Applied Energy, Elsevier, vol. 306(PB).
    15. Zheng Zeng & Wei-Ge Luo & Zhe Wang & Fa-Cheng Yi, 2021. "Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis," Sustainability, MDPI, vol. 13(2), pages 1-17, January.
    16. Menéndez-García, Luis Alfonso & García-Nieto, Paulino José & García-Gonzalo, Esperanza & Sánchez Lasheras, Fernando, 2024. "Time series analysis for COMEX platinum spot price forecasting using SVM, MARS, MLP, VARMA and ARIMA models: A case study," Resources Policy, Elsevier, vol. 95(C).

    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:4:p:1691-:d:493409. 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.