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Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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