An ANN Model for Predicting the Compressive Strength of Concrete

Applied Sciences - Tập 11 Số 9 - Trang 3798
Chia‐Ju Lin1, Nan‐Jing Wu1
1Department of Civil and Water Resources Engineering, National Chiayi University, Chiayi 600355, Taiwan

Tóm tắt

An artificial neural network (ANN) model for predicting the compressive strength of concrete is established in this study. The Back Propagation (BP) network with one hidden layer is chosen as the structure of the ANN. The database of real concrete mix proportioning listed in earlier research by another author is used for training and testing the ANN. The proper number of neurons in the hidden layer is determined by checking the features of over-fitting while the synaptic weights and the thresholds are finalized by checking the features of over-training. After that, we use experimental data from other papers to verify and validate our ANN model. The final result of the synaptic weights and the thresholds in the ANN are all listed. Therefore, with them, and using the formulae expressed in this article, anyone can predict the compressive strength of concrete according to the mix proportioning on his/her own.

Từ khóa


Tài liệu tham khảo

Abrams, D. (1918). Design of Concrete Mixtures, Structural Materials Laboratory, Lewis Institute. Bulletin No. 1.

Lin, 2002, Neural network based methodology for estimating bridge damage after major earthquakes, J. Chin. Inst. Eng., 25, 415, 10.1080/02533839.2002.9670716

Lee, 2003, Prediction of concrete strength using artificial neural networks, Eng. Struct., 25, 849, 10.1016/S0141-0296(03)00004-X

Hola, 2005, Methodology of neural identification of strength of concrete, ACI Mater. J., 102, 459

Kewalramani, 2006, Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks, Autom. Constr., 15, 374, 10.1016/j.autcon.2005.07.003

Pala, 2006, Predicting the compressive strength and slump of high strength concrete using neural network, Constr. Build. Mater., 20, 769, 10.1016/j.conbuildmat.2005.01.054

2008, Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic, Comput. Mater. Sci., 41, 305, 10.1016/j.commatsci.2007.04.009

Bilim, 2009, Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network, Adv. Eng. Softw., 40, 334, 10.1016/j.advengsoft.2008.05.005

Trtnik, 2009, Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks, Ultrasonics, 49, 53, 10.1016/j.ultras.2008.05.001

Atici, 2011, Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network, Expert Syst. Appl., 38, 9609, 10.1016/j.eswa.2011.01.156

Alexandridis, 2012, A neural network approach for compressive strength prediction in cement-based materials through the study of pressure stimulated electrical signals, Constr. Build. Mater., 30, 294, 10.1016/j.conbuildmat.2011.11.036

Shen, C.-H. (2013). Application of Neural Networks and ACI Code in Pozzolanic Concrete Mix Design. [Master’s Thesis, Department of Civil Engineering, Nation Chiao Tung University]. (In Chinese).

Chopra, 2015, Artificial Neural Networks for the Prediction of Compressive Strength of Concrete, Int. J. Appl. Sci. Eng., 13, 187

Nikoo, 2015, Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks, Adv. Mater. Sci. Eng., 2015, 849126, 10.1155/2015/849126

Chopar, 2016, Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming, Adv. Mater. Sci. Eng., 2016, 7648467

Hao, 2018, A Computer-Aided Approach to Pozzolanic Concrete Mix Design, Adv. Civ. Eng., 2018, 4398017, 10.1155/2018/4398017

Cuingnet, 2011, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Med. Image Anal., 15, 729, 10.1016/j.media.2011.05.007

Singer, 2011, Pegasos: Primal estimated sub-gradient solver for SVM, Math. Program., 127, 3, 10.1007/s10107-010-0420-4

Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput., 18, 1527, 10.1162/neco.2006.18.7.1527

Hinton, 2012, Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups, IEEE Signal Process. Mag., 29, 82, 10.1109/MSP.2012.2205597

Sainath, T.N., Mohamed, A., Kingsbury, B., and Ramabhadran, B. (2013, January 26–31). Deep convolutional neural networks for LVCSR. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.

LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539

He, K., and Sun, J. (2015, January 7–12). Convolutional neural networks at constrained time cost. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.

Segal, 2000, Radial basis function (RBF) network adaptive power system stabilizer, IEEE Trans. Power Syst., 15, 722, 10.1109/59.867165

2001, Numerical solution of differential equations using multiquadric radial basis function networks, Neural Netw., 14, 185, 10.1016/S0893-6080(00)00095-2

Ding, 2012, An optimizing method of RBF neural network based on genetic algorithm, Neural Comput. Appl., 21, 333, 10.1007/s00521-011-0702-7

Li, 2017, Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks, Transp. Res. Part C Emerg. Technol., 77, 306, 10.1016/j.trc.2017.02.005

Aljarah, 2018, Training radial basis function networks using biogeography-based optimizer, Neural Comput. Appl., 29, 529, 10.1007/s00521-016-2559-2

Karamichailidou, 2021, Wind turbine power curve modeling using radial basis function neural networks and tabu search, Renew. Energy, 163, 2137, 10.1016/j.renene.2020.10.020

Winiczenko, 2018, Optimisation of ANN topology for predicting the rehydrated apple cubes colour change using RSM and GA, Neural Comput. Appl., 30, 1795, 10.1007/s00521-016-2801-y

Rumelhart, 1986, Learning representations by back propagating errors, Nature, 323, 533, 10.1038/323533a0

Ham, F.M., and Kostanic, I. (2001). Principles of Neurocomputing for Science & Engineering, McGraw-Hill Higher Education.