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Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS

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  • Van Quan Tran
  • Hai-Van Thi Mai
  • Thuy-Anh Nguyen
  • Hai-Bang Ly

Abstract

An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete containing Ground Granulated Blast Furnace Slag (GGBFS). To accomplish this purpose, an experimental database of 595 samples is compiled from the literature and utilized to find the best ANN architecture. The cement content, water content, coarse aggregate content, fine aggregate content, GGBFS content, carboxylic type hyper plasticizing content, superplasticizer content, and testing age are the eight inputs in this database. As a result, the optimal selection of the ANN design is carried out and evaluated using conventional statistical metrics. The results demonstrate that utilizing the best architecture [8–14–4–1] among the 240 investigated architectures, and the best ANN model, is a very efficient predictor of the compressive strength of concrete using GGBFS, with a maximum R2 value of 0.968 on the training part and 0.965 on the testing part. Furthermore, a sensitivity analysis is performed over 500 Monte Carlo simulations using the best ANN model to determine the reliability of ANN model in predicting the compressive strength of concrete. The findings of this research may make it easier and more efficient to apply the ANN model to many civil engineering challenges.

Suggested Citation

  • Van Quan Tran & Hai-Van Thi Mai & Thuy-Anh Nguyen & Hai-Bang Ly, 2021. "Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-21, December.
    • Handle: RePEc:plo:pone00:0260847
    DOI: 10.1371/journal.pone.0260847
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    References listed on IDEAS

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    1. Thuy-Anh Nguyen & Hai-Bang Ly & Binh Thai Pham, 2020. "Backpropagation Neural Network-Based Machine Learning Model for Prediction of Soil Friction Angle," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, December.
    2. Giuseppe Jurman & Samantha Riccadonna & Cesare Furlanello, 2012. "A Comparison of MCC and CEN Error Measures in Multi-Class Prediction," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-8, August.
    3. Hai-Bang Ly & Tien-Thinh Le & Huong-Lan Thi Vu & Van Quan Tran & Lu Minh Le & Binh Thai Pham, 2020. "Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams," Sustainability, MDPI, vol. 12(7), pages 1-34, March.
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