1 |
Kim IS, Lee JH, Yang DS, Park SK. Prediction on mix proportion factor and strength of concrete using neural network. Journal of the Korea Concrete Institute. 2002 Aug;14(4):457-66. https://doi.org/10.4334/JKCI.2002.14.4.457
DOI
|
2 |
Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence. 2013 Mar;35(8):1798-828. https://doi.org/10.1109/TPAMI.2013.50
DOI
|
3 |
Bello SA, Oyedele L, Olaitan OK, Olonade KA, Olajumoke AM, Ajayi A, Akanbi L, Akinade O, Sanni ML, Bello AL. A deep learning approach to concrete water-cement ratio prediction. Results in Materials. 2022 Sep;15:100300. https://doi.org/10.1016/j.rinma.2022.100300
DOI
|
4 |
Werbos PJ. Generalization of backpropagation with application to a recurrent gas market model. Neural networks. 1988;1(4):339-56. https://doi.org/10.1016/0893-6080(88)90007-X
DOI
|
5 |
Squartini S, Hussain A, Piazza F. Preprocessing based solution for the vanishing gradient problem in recurrent neural networks. Proceedings of the 2003 International Symposium on Circuits and Systems; 2003 May 25-28. Bangkok (Thailand): NY: Institute of Electrical and Electronics Engineers; 2003. p. 7762895. https://doi.org/10.1109/ISCAS.2003.1206412
DOI
|
6 |
Chicco D, Warrens M J, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science. 2021 Jul;7:e623. https://doi.org/10.7717/peerj-cs.623
DOI
|
7 |
Im CH, Jee NY, Cho HB. The estimation of compressive strength of concrete used admixture on the basis of mix design. Spring Annual Conference of Architectural Institute of Korea; 2003 Apr 26; Yongin (Korea): Seoul (Korea): Architectural Institute of Korea; 2003. p. 251-4.
|