Modeling the compressive strength of high-strength concrete: An extreme learning approach

Abstract Compressive strength is a major and significant mechanical property of concrete which is considered as one of the important parameters in many design codes and standards. Early and accurate estimation of it can save in time and cost. In this study, extreme learning machine (ELM) was used to predict the compressive strength of high-strength concrete (HSC). ELM is a relatively new method for training artificial neural networks (ANN), showing good generalization performance and fast learning speed in many regression applications. ELM model was developed using 324 data records obtained from laboratory experiments. The compressive strength was modeled as a function of five input variables: water, cement, fine aggregate, coarse aggregate, and superplasticizer. The performance of the developed ELM model was compared with that of ANN model trained by using back propagation (BP) algorithm. The simulation results show that the proposed ELM model has a strong potential for predicting the compressive strength of HSC.

[1]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Yuan Lan,et al.  Constructive hidden nodes selection of extreme learning machine for regression , 2010, Neurocomputing.

[3]  Alok Singh,et al.  Two swarm intelligence approaches for tuning extreme learning machine , 2018, Int. J. Mach. Learn. Cybern..

[4]  Ahmet Tortum,et al.  Neural networks analysis of compressive strength of lightweight concrete after high temperatures , 2013 .

[5]  C. Poon,et al.  Prediction of compressive strength of recycled aggregate concrete using artificial neural networks , 2013 .

[6]  M. A. Bhatti,et al.  Predicting the compressive strength and slump of high strength concrete using neural network , 2006 .

[7]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[8]  Zhiping Lin,et al.  Self-Adaptive Evolutionary Extreme Learning Machine , 2012, Neural Processing Letters.

[9]  D. Serre.  Matrices: Theory and Applications , 2002 .

[10]  Manish A. Kewalramani,et al.  Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks , 2006 .

[11]  S. Chithra,et al.  A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks , 2016 .

[12]  Stephen I. Gallant,et al.  Neural network learning and expert systems , 1993 .

[13]  Mônica Batista Leite,et al.  Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks , 2013 .

[14]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[15]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[16]  J. Sobhani,et al.  Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models , 2010 .

[17]  İlker Bekir Topçu,et al.  Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic , 2008 .

[18]  Amaury Lendasse,et al.  OP-ELM: Optimally Pruned Extreme Learning Machine , 2010, IEEE Transactions on Neural Networks.

[19]  Mustafa Saridemir,et al.  Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks , 2009, Adv. Eng. Softw..

[20]  İlker Bekir Topçu,et al.  Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic , 2009 .

[21]  Zexuan Zhu,et al.  A fast pruned-extreme learning machine for classification problem , 2008, Neurocomputing.

[22]  Jui-Sheng Chou,et al.  Machine learning in concrete strength simulations: Multi-nation data analytics , 2014 .

[23]  Guang-Bin Huang,et al.  Learning capability and storage capacity of two-hidden-layer feedforward networks , 2003, IEEE Trans. Neural Networks.

[24]  Hosein Naderpour,et al.  Prediction of FRP-confined compressive strength of concrete using artificial neural networks , 2010 .

[25]  Arthur H. Nilson,et al.  PROPERTIES OF HIGH - STRENGTH CONCRETE SUBJECT TO SHORT - TERM LOADS , 1981 .

[26]  Jui-Sheng Chou,et al.  Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength , 2013 .

[27]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[28]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[29]  A. Kai Qin,et al.  Evolutionary extreme learning machine , 2005, Pattern Recognit..

[30]  J. Pera,et al.  Durability of high-strength concrete in ammonium sulfate solution , 2001 .

[31]  Henry G. Russell,et al.  ACI Defines High-Performance Concrete , 1999 .

[32]  Rafat Siddique,et al.  Model for mix design of brick aggregate concrete based on neural network modelling , 2017 .