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Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

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  • Jesús de-Prado-Gil

    (Department of Applied Physics, Campus of Vegazana s/n, University of León, 24071 León, Spain)

  • Osama Zaid

    (Department of Structure Engineering, Military College of Engineering, Risalpur, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Covadonga Palencia

    (Department of Applied Physics, Campus of Vegazana s/n, University of León, 24071 León, Spain)

  • Rebeca Martínez-García

    (Department of Mining Technology, Topography and Structures, Campus de Vegazana s/n, University of León, 24071 León, Spain)

Abstract

The composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the prediction of concrete properties using soft computing techniques, which will eventually reduce environmental degradation and other material waste. There have been very limited and contradicting studies regarding prediction using different ANN algorithms. This paper aimed to predict the 28-day splitting tensile strength of SCC with RA using the artificial neural network technique by comparing the following algorithms: Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB). There have been very limited and contradicting studies regarding prediction by using and comparing different ANN algorithms, so a total of 381 samples were collected from various published journals. The input variables were cement, admixture, water, fine and coarse aggregates, and superplasticizer; the data were randomly divided into three sets—training (60%), validation (10%), and testing (30%)—with 10 neurons in the hidden layer. The models were evaluated by the mean squared error (MSE) and correlation coefficient (R). The results indicated that all three models have optimal accuracy; still, BR gave the best performance (R = 0.91 and MSE = 0.2087) compared with LM and SCG. BR was the best model for predicting TS at 28 days for SCC with RA. The sensitivity analysis indicated that cement (30.07%) was the variable that contributed the most to the prediction of TS at 28 days for SCC with RA, and water (2.39%) contributed the least.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2245-:d:848864
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    References listed on IDEAS

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    1. Ahmad Nayyar Hassan & Ayman El-Hag, 2020. "Two-Layer Ensemble-Based Soft Voting Classifier for Transformer Oil Interfacial Tension Prediction," Energies, MDPI, vol. 13(7), pages 1-11, April.
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    Cited by:

    1. 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.
    2. 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.

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