TY - JOUR
ID - 163708
TI - Deep Neural Network Regression with Advanced Training Algorithms for Estimating the Compressive Strength of Manufactured-Sand Concrete
JO - Journal of Soft Computing in Civil Engineering
JA - SCCE
LA - en
SN -
AU - Hoang, Nhat-Duc
AU - Tran, Van-Duc
AD - Lecturer, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
AD - Lecturer, Faculty of Civil Engineering, Duy Tan University, Da Nang, 550000, Vietnam
Y1 - 2023
PY - 2023
VL - 7
IS - 1
SP - 114
EP - 134
KW - Compressive strength
KW - Manufactured-Sand Concrete
KW - Deep Learning
KW - Neural Network
KW - Advanced Optimizers
DO - 10.22115/scce.2022.349837.1485
N2 - 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.
UR - http://www.jsoftcivil.com/article_163708.html
L1 - http://www.jsoftcivil.com/article_163708_91c6fe6dfd6d00aa41ee12f22dcd4de2.pdf
ER -