Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models

A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete.

[1]  Chenglin Wang,et al.  Real-time detection of surface deformation and strain in recycled aggregate concrete-filled steel tubular columns via four-ocular vision , 2019, Robotics Comput. Integr. Manuf..

[2]  Emadaldin Mohammadi Golafshani,et al.  Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves , 2018, Journal of Cleaner Production.

[3]  Feng Liu,et al.  Study of seismic behavior of recycled aggregate concrete-filled steel tubular columns , 2018, Journal of Constructional Steel Research.

[4]  Amir Hossein Rafiean,et al.  Compressive strength prediction of environmentally friendly concrete using artificial neural networks , 2018 .

[5]  Huang Hui,et al.  Hybrid PSO-BP Neural Network Approach for Wind Power Forecasting , 2017 .

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

[7]  Arpad Horvath,et al.  Towards sustainable concrete. , 2017, Nature materials.

[8]  Saurav Rukhaiyar,et al.  A PSO-ANN hybrid model for predicting factor of safety of slope , 2017 .

[9]  Zia U. A. Zihan,et al.  Effects of maximum size of brick aggregate on properties of concrete , 2017 .

[10]  Danial Jahed Armaghani,et al.  Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition , 2017 .

[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]  Zong Woo Geem,et al.  Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones , 2016 .

[13]  Mustafa Erdemir,et al.  Utilization and efficiency of ground granulated blast furnace slag on concrete properties – A review , 2016 .

[14]  Osman Turan,et al.  An artificial neural network based decision support system for energy efficient ship operations , 2016, Comput. Oper. Res..

[15]  Eric Mayer,et al.  Properties Of Concrete , 2016 .

[16]  Mahamad Nabab Alam,et al.  A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination , 2015 .

[17]  Ming Diao,et al.  Performance enhancement of INS/CNS integration navigation system based on particle swarm optimization back propagation neural network , 2015 .

[18]  Mandeep Kaur,et al.  A Review of Parameters for Improving the Performance of Particle Swarm Optimization , 2015 .

[19]  Masoud Monjezi,et al.  Prediction of seismic slope stability through combination of particle swarm optimization and neural network , 2015, Engineering with Computers.

[20]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[21]  Habib Rostami,et al.  Application of hybrid neural particle swarm optimization algorithm for prediction of MMP , 2014 .

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

[23]  K. Gnana Sheela,et al.  Review on Methods to Fix Number of Hidden Neurons in Neural Networks , 2013 .

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

[25]  Zhu-guo Li,et al.  Effects of elevated temperatures on properties of concrete containing ground granulated blast furnace slag as cementitious material , 2012 .

[26]  Maysam F. Abbod,et al.  Optimization the Initial Weights of Artificial Neural Networks via Genetic Algorithm Applied to Hip Bone Fracture Prediction , 2012, Adv. Fuzzy Syst..

[27]  Said Kenai,et al.  Prediction of Efficiency Factor of Ground-Granulated Blast-Furnace Slag of Concrete Using Artificial Neural Network , 2011 .

[28]  Ahmed M. Azmy,et al.  Neural networks for predicting compressive strength of structural light weight concrete , 2009 .

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

[30]  Okan Karahan,et al.  Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network , 2009, Adv. Eng. Softw..

[31]  Sing-Wu Liou,et al.  Integrative Discovery of Multifaceted Sequence Patterns by Frame-Relayed Search and Hybrid PSO-ANN , 2009, J. Univers. Comput. Sci..

[32]  S. E. Chidiac,et al.  Evolution of mechanical properties of concrete containing ground granulated blast furnace slag and effects on the scaling resistance test at 28 days , 2008 .

[33]  Turan Özturan,et al.  COMPARISON OF CONCRETE STRENGTH PREDICTION TECHNIQUES WITH ARTIFICIAL NEURAL NETWORK APPROACH , 2008 .

[34]  S. Akyuz,et al.  An experimental study on optimum usage of GGBS for the compressive strength of concrete , 2007 .

[35]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[36]  A. Öztas,et al.  Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks , 2007 .

[37]  Michael R. Lyu,et al.  A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training , 2007, Appl. Math. Comput..

[38]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[39]  Velu Saraswathy,et al.  Studies on the corrosion resistance of reinforced steel in concrete with ground granulated blast-furnace slag--An overview. , 2006, Journal of hazardous materials.

[40]  Gyu-Yong Kim,et al.  Autogenous shrinkage of concrete containing granulated blast-furnace slag , 2006 .

[41]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[42]  Dale P. Bentz,et al.  Influence of Water-to-Cement Ratio on Hydration Kinetics: Simple Models Based on Spatial Considerations , 2006 .

[43]  An Cheng,et al.  Influence of GGBS on durability and corrosion behavior of reinforced concrete , 2005 .

[44]  Leonard Ziemiański,et al.  Neural Networks in the Identification Analysis of Structural Mechanics Problems , 2005 .

[45]  Kwang-Myong Lee,et al.  Characteristics of Autogenous Shrinkage for Concrete Containing Blast-Furnace Slag , 2004 .

[46]  Ragip Ince,et al.  Prediction of fracture parameters of concrete by Artificial Neural Networks , 2004 .

[47]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[48]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[49]  V. Papadakis,et al.  Supplementary cementing materials in concrete: Part I: efficiency and design , 2002 .

[50]  Guido Bugmann,et al.  NEURAL NETWORK DESIGN FOR ENGINEERING APPLICATIONS , 2001 .

[51]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[52]  윤재환,et al.  분말도가 다른 고로슬래그 미분말을 사용한 콘크리트의 압축강도발현성에 관한 연구 ( A Study on the Compressive Strength Development of Concrete Using Ground Granulated Blast-furnace Slag of Different Fineness ) , 2000 .

[53]  P. J. Wainwright,et al.  The influence of ground granulated blastfurnace slag (GGBS) additions and time delay on the bleeding of concrete , 2000 .

[54]  Tabarak M. A. Ballal.  The use of artificial neural networks for modelling buildability in preliminary structural design , 1999 .

[55]  Alice E. Smith,et al.  Bias and variance of validation methods for function approximation neural networks under conditions of sparse data , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[56]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[57]  Shin'ichi Tamura,et al.  Capabilities of a four-layered feedforward neural network: four layers versus three , 1997, IEEE Trans. Neural Networks.

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

[59]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[60]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[61]  D. R. Hush,et al.  Classification with neural networks: a performance analysis , 1989, IEEE 1989 International Conference on Systems Engineering.

[62]  R. Hecht-Nielsen.  Kolmogorov''s Mapping Neural Network Existence Theorem , 1987 .