Deep Neural Network Regression with Advanced Training Algorithms for Estimating the Compressive Strength of Manufactured-Sand Concrete

Document Type : Regular Article


1 Lecturer, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

2 Lecturer, Faculty of Civil Engineering, Duy Tan University, Da Nang, 550000, Vietnam

3 Lecturer, International School - Duy Tan University, Da Nang, 550000, Vietnam


Manufactured sand has high potential for replacing natural sand and reducing the negative impact of the construction industry on the environment. This paper aims at developing a novel deep learning-based approach for estimating the compressive strength of manufactured-sand concrete. The deep neural networks are trained by  the advanced optimizers of Root Mean Squared Propagation, Adaptive Moment Estimation, and Adaptive Moment Estimation with Nesterov momentum (Nadam). In addition, the activation functions of logistic sigmoid, hyperbolic tangent sigmoid, and rectified linear unit activation are employed. A dataset including 132 samples has been used to train and verify the deep neural networks. Stone powder content, sand ratio, quantity of cement, quantity of water, quantity of coarse aggregate, quantity of water-reducer, quantity of manufactured sand, concrete slump, unit weight of concrete, and curing age are  utilized as predictor variables. Based on experiments, the Nadam-optimized model used with the sigmoid activation function has achieved the most desired performance with root mean square error (RMSE) = 1.95, mean absolute percentage error (MAPE) = 3.04%, and coefficient of determination (R2) = 0.97. Thus, this neural computing model is recommended for practical purposes because it can help to mitigate the time and cost dedicated to laboratory work.


  • Deep learning is used to estimate the compressive strength of manufactured sand concrete.
  • Advanced optimizers are used to train the deep learning models.
  • Adaptive Moment Estimation with Nesterov momentum achieves the best outcome.


Main Subjects

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  • Receive Date: 09 July 2022
  • Revise Date: 25 October 2022
  • Accept Date: 26 December 2022
  • First Publish Date: 26 December 2022