Modeling and Analysis of Concrete Slump Using Hybrid Artificial Neural Networks
Artificial Neural Networks (ANN) trained using backpropagation
(BP) algorithm are commonly used for modeling
material behavior associated with non-linear, complex or unknown
interactions among the material constituents. Despite multidisciplinary
applications of back-propagation neural networks
(BPNN), the BP algorithm possesses the inherent drawback of
getting trapped in local minima and slowly converging to a global
optimum. The paper present a hybrid artificial neural networks and
genetic algorithm approach for modeling slump of ready mix
concrete based on its design mix constituents. Genetic algorithms
(GA) global search is employed for evolving the initial weights and
biases for training of neural networks, which are further fine tuned
using the BP algorithm. The study showed that, hybrid ANN-GA
model provided consistent predictions in comparison to commonly
used BPNN model. In comparison to BPNN model, the hybrid ANNGA
model was able to reach the desired performance goal quickly.
Apart from the modeling slump of ready mix concrete, the synaptic
weights of neural networks were harnessed for analyzing the relative
importance of concrete design mix constituents on the slump value.
The sand and water constituents of the concrete design mix were
found to exhibit maximum importance on the concrete slump value.
[1] P.K. Mehta and P.J.M. Monteiro, Concrete: Structure, Properties and
Materials. 3rd ed. New York: McGraw Hill, 2006.
[2] Z. Li, Advanced Concrete Technology. 1st ed. New Jersey: John Wiley &
Sons, Inc, 2011.
[3] W.P.S. Dias and S.P. Pooliyadda, "Neural Networks for predicting
properties of concrete with admixtures,” Construction and Building
Materials, vol. 15, no. 7, pp. 371-379, 2001.
[4] I.-C. Yeh, "Exploring concrete slump model using artificial neural
networks,” Journal of Computing in Civil Engineering, vol. 20, no. 3,
pp. 217-221, 2006.
[5] A. Oztas, M. Pala, E. Ozbay, E. Kanca, N. Caglar and M.A. Bhatti,
"Predicting the compressive strength and slump of high strength
concrete using neural network,” Construction and Building Materials,
vol. 20, no. 9, pp. 769-775, 2006.
[6] I.-C.Yeh, "Modeling slump flow of concrete using second-order
regressions and artificial neural networks,” Cement and Concrete
Composites, vol. 29, pp. 474-480, 2007.
[7] A. Jain, S.K. Jha and S. Misra, "Modeling and analysis of concrete
slump using artificial neural networks,” Journal of Materials in Civil
Engineering, vol. 20, no. 9, pp. 628-633, 2008.
[8] M. Saridemir, "Prediction of compressive strength of concretes
containing metakaolin and silica fumes by artificial neural networks,”
Advances in Engineering Software, vol. 40, no. 5, pp. 350-355, 2009.
[9] S.J. Kwon and H.W. Song, "Analysis of carbonation behaviour in
concrete using neural network algorithm and carbonation modeling,”
Cement and Concrete Research, vol. 40, no. 1, pp. 119-127, 2010.
[10] R. Siddique, P. Aggarwal and Y. Aggarwal, "Prediction of compressive
strength of self compacting concrete containing bottom ash using
artificial neural networks,” Advances in Engineering Software, vol. 42,
no. 10, pp. 780-786, 2011.
[11] M.I. Khan, "Predicting properties of high performance concrete
containing composite cementitious materials using Artificial Neural
Networks,” Automation in Construction, vol.22, pp. 516-524, 2012.
[12] O.A. Hodhod and H.I. Ahmed, "Developing an artificial neural network
model to evaluate chloride diffusivity in high performance concrete,”
HBRC Journal, vol.9, no. 1, pp. 15-21, 2013.
[13] A.M. Diab, H.E. Elyamany, A.E.M.A. Elmoaty and A.H. Shalan,
"Prediction of concrete compressive strength due to long term sulphate
attack using neural networks,” Alexandria Engineering Journal, vol.53,
pp. 627-642, 2014.
[14] C.L. Su, S.M. Yang and W.L. Huang, "A two stage algorithm
integrating genetic algorithms and modified Newton method for neural
network training in engineering systems,” Expert Systems with
Applications, vol. 38, no. 10, pp. 12189-12194, 2011.
[15] A. Johari, A.A. Javadi and G. Habibagahi, "Modelling the mechanical
bahaviour of unsaturated soils using a genetic algorithm based neural
network,” Computers and Geotechnics, vol. 38, no. 1, pp. 2-13, 2011.
[16] H. Karimi and F. Yousefi, " Application of artificial neural networkgenetic
algorithm (ANN-GA) to correlation of density in nanofluids,”
Fluid Phase Equilibria, vol. 336, pp. 79-83, 2012.
[17] R. Wang,C. Zhou, Z. Deng, B. Ni and Z. Zhao, "Predicting foF2 in China
region using the artificial neural networks improved by the genetic
algorithms,” Journal of Atmospheric and Solar-Terrestrial Physics, vol.
92, pp. 7-17, 2013.
[18] Y. Xue, L. Cheng, J. Mou and W. Zhao, "A new fracture prediction
method by combining genetic algorithm with neural network in lowpermeability
reservoirs,” Journal of Petroleum Science and Engineering,
vol. 121, pp. 159-166, 2014.
[19] M.M. Alshihri, A.M. Azmy and M.S. El-Bisy, "Neural Networks for
predicting compressive strength of structural light weight concrete,”
Construction and Building Materials, vol. 23, no. 6, pp. 2214-2219,
2009.
[20] K. Hornik, M. Stinchcombe and H. White, "Multilayer feed forward
networks are universal approximators,” Neural Networks, vol. 2, no. 5,
pp. 359-366, 1989.
[21] S. Tamura and M. Tateishi, "Capabilities of four layered feedforward
neural network: four layer versus three,” IEEE Trasactions on Neural
Networks, vol. 8, no. 2, pp. 251-255, 1997.
[22] P.C. Pendharkar and J.A.Rodger, "Technical efficiency based selection
of learning cases to improve the forecasting efficiency of neural
networks under monotonicity assumption,”, Decision Support Systems,
vol. 36, no. 1, pp. 117-136, 2003.
[23] K. Jinchuan and L. Xinzhe, "Empirical analysis of optimal hidden layer
neurons in neural network modeling for stock prediction,” in
Proceedings of Pacific-Asia Workshop on Computational Intelligence
and Industrial Applications, vol. 2, pp. 828-832, Dec. 2008.
[24] D. Hunter, Y. Hao, M.S. Pukish, J. Kolbusz and B.M Wilamowski,
"Selection of proper Neural Network sizes and architectures-A
comparative study,” IEEE Transaction on Industrial Informatics, vol. 8,
no. 2, pp. 228-240, 2012.
[25] S. Rajasekaran and G.A.V. Pai, "Neural Networks, Fuzzy Logic and
Genetic Algorithms: Synthesis & Applications,” New Delhi: Prentice-
Hall of India Private Limited, 2003..
[26] B.M. Wilamowski, Y. Chen, A. Malinowski, "Efficient Algorithm for
Training Neural Networks with One Hidden Layer,” in Proceedings of
International Joint Conference on Neural Networks IEEE, pp. 1725-
1728, 1999.
[27] K. Wang, Computational Intelligence in Agile Manufacturing
Engineering, in: Gunasekaran A, editor. Agile Manufacturing The 21st
Century Competitive Strategy, Oxford, UK: Elsevier Science Ltd, 2001,
pp. 297-315.
[28] J.E. Nash and J.V. Sutcliffe, "River flow forecasting through conceptual
models Part I – a discussion of principles,” Journal of Hydrology, vol.
10, no. 3, pp. 282–290, 1970.
[29] S. Srinivasulu and A. Jain, "A comparative analysis of training methods
for artificial neural network rainfall-runoff models,” Applied Soft
Computing, vol. 6, pp. 295-306, 2006.
[30] J.D. Olden and D.A. Jackson, "Illuminating the "Black Box”: a
randomization approach for understanding variable contributions in
artificial neural networks.” Ecological Modeling ,vol. 154, pp. 135-150,
2002.
[31] G. Acciani, E. Chiarantoni and G. Fornarelli, A neural network
approach to study O3 and PM10 concentration, in: Kollias S, Staflopatis
A, Duch W, Oja E, editors. ICANN'06 Proceedings of the 16th
international conference on Artificial Neural Networks - Volume Part II,
Berlin, Germany: Springer Verlag, 2006, pp. 913-922.
[32] G.D. Garson, "Interpreting neural network connection
weights,”Artificial Intelligence Expert , vol. 6, pp. 47-51, 1991.
[33] M. Gevrey, I. Dimopoulos and S. Lek, "Review and comparison of
methods to study the contribution of variables in artificial neural
network models,” Ecological Modeling , vol. 160, pp. 249-264, 2003.
[34] J.J. Montano and A. Palmer, "Numeric sensitivity analysis applied to
feedforward neural networks,” Neural Computing and Applications, vol.
12, pp. 119-125, 2003.
[1] P.K. Mehta and P.J.M. Monteiro, Concrete: Structure, Properties and
Materials. 3rd ed. New York: McGraw Hill, 2006.
[2] Z. Li, Advanced Concrete Technology. 1st ed. New Jersey: John Wiley &
Sons, Inc, 2011.
[3] W.P.S. Dias and S.P. Pooliyadda, "Neural Networks for predicting
properties of concrete with admixtures,” Construction and Building
Materials, vol. 15, no. 7, pp. 371-379, 2001.
[4] I.-C. Yeh, "Exploring concrete slump model using artificial neural
networks,” Journal of Computing in Civil Engineering, vol. 20, no. 3,
pp. 217-221, 2006.
[5] A. Oztas, M. Pala, E. Ozbay, E. Kanca, N. Caglar and M.A. Bhatti,
"Predicting the compressive strength and slump of high strength
concrete using neural network,” Construction and Building Materials,
vol. 20, no. 9, pp. 769-775, 2006.
[6] I.-C.Yeh, "Modeling slump flow of concrete using second-order
regressions and artificial neural networks,” Cement and Concrete
Composites, vol. 29, pp. 474-480, 2007.
[7] A. Jain, S.K. Jha and S. Misra, "Modeling and analysis of concrete
slump using artificial neural networks,” Journal of Materials in Civil
Engineering, vol. 20, no. 9, pp. 628-633, 2008.
[8] M. Saridemir, "Prediction of compressive strength of concretes
containing metakaolin and silica fumes by artificial neural networks,”
Advances in Engineering Software, vol. 40, no. 5, pp. 350-355, 2009.
[9] S.J. Kwon and H.W. Song, "Analysis of carbonation behaviour in
concrete using neural network algorithm and carbonation modeling,”
Cement and Concrete Research, vol. 40, no. 1, pp. 119-127, 2010.
[10] R. Siddique, P. Aggarwal and Y. Aggarwal, "Prediction of compressive
strength of self compacting concrete containing bottom ash using
artificial neural networks,” Advances in Engineering Software, vol. 42,
no. 10, pp. 780-786, 2011.
[11] M.I. Khan, "Predicting properties of high performance concrete
containing composite cementitious materials using Artificial Neural
Networks,” Automation in Construction, vol.22, pp. 516-524, 2012.
[12] O.A. Hodhod and H.I. Ahmed, "Developing an artificial neural network
model to evaluate chloride diffusivity in high performance concrete,”
HBRC Journal, vol.9, no. 1, pp. 15-21, 2013.
[13] A.M. Diab, H.E. Elyamany, A.E.M.A. Elmoaty and A.H. Shalan,
"Prediction of concrete compressive strength due to long term sulphate
attack using neural networks,” Alexandria Engineering Journal, vol.53,
pp. 627-642, 2014.
[14] C.L. Su, S.M. Yang and W.L. Huang, "A two stage algorithm
integrating genetic algorithms and modified Newton method for neural
network training in engineering systems,” Expert Systems with
Applications, vol. 38, no. 10, pp. 12189-12194, 2011.
[15] A. Johari, A.A. Javadi and G. Habibagahi, "Modelling the mechanical
bahaviour of unsaturated soils using a genetic algorithm based neural
network,” Computers and Geotechnics, vol. 38, no. 1, pp. 2-13, 2011.
[16] H. Karimi and F. Yousefi, " Application of artificial neural networkgenetic
algorithm (ANN-GA) to correlation of density in nanofluids,”
Fluid Phase Equilibria, vol. 336, pp. 79-83, 2012.
[17] R. Wang,C. Zhou, Z. Deng, B. Ni and Z. Zhao, "Predicting foF2 in China
region using the artificial neural networks improved by the genetic
algorithms,” Journal of Atmospheric and Solar-Terrestrial Physics, vol.
92, pp. 7-17, 2013.
[18] Y. Xue, L. Cheng, J. Mou and W. Zhao, "A new fracture prediction
method by combining genetic algorithm with neural network in lowpermeability
reservoirs,” Journal of Petroleum Science and Engineering,
vol. 121, pp. 159-166, 2014.
[19] M.M. Alshihri, A.M. Azmy and M.S. El-Bisy, "Neural Networks for
predicting compressive strength of structural light weight concrete,”
Construction and Building Materials, vol. 23, no. 6, pp. 2214-2219,
2009.
[20] K. Hornik, M. Stinchcombe and H. White, "Multilayer feed forward
networks are universal approximators,” Neural Networks, vol. 2, no. 5,
pp. 359-366, 1989.
[21] S. Tamura and M. Tateishi, "Capabilities of four layered feedforward
neural network: four layer versus three,” IEEE Trasactions on Neural
Networks, vol. 8, no. 2, pp. 251-255, 1997.
[22] P.C. Pendharkar and J.A.Rodger, "Technical efficiency based selection
of learning cases to improve the forecasting efficiency of neural
networks under monotonicity assumption,”, Decision Support Systems,
vol. 36, no. 1, pp. 117-136, 2003.
[23] K. Jinchuan and L. Xinzhe, "Empirical analysis of optimal hidden layer
neurons in neural network modeling for stock prediction,” in
Proceedings of Pacific-Asia Workshop on Computational Intelligence
and Industrial Applications, vol. 2, pp. 828-832, Dec. 2008.
[24] D. Hunter, Y. Hao, M.S. Pukish, J. Kolbusz and B.M Wilamowski,
"Selection of proper Neural Network sizes and architectures-A
comparative study,” IEEE Transaction on Industrial Informatics, vol. 8,
no. 2, pp. 228-240, 2012.
[25] S. Rajasekaran and G.A.V. Pai, "Neural Networks, Fuzzy Logic and
Genetic Algorithms: Synthesis & Applications,” New Delhi: Prentice-
Hall of India Private Limited, 2003..
[26] B.M. Wilamowski, Y. Chen, A. Malinowski, "Efficient Algorithm for
Training Neural Networks with One Hidden Layer,” in Proceedings of
International Joint Conference on Neural Networks IEEE, pp. 1725-
1728, 1999.
[27] K. Wang, Computational Intelligence in Agile Manufacturing
Engineering, in: Gunasekaran A, editor. Agile Manufacturing The 21st
Century Competitive Strategy, Oxford, UK: Elsevier Science Ltd, 2001,
pp. 297-315.
[28] J.E. Nash and J.V. Sutcliffe, "River flow forecasting through conceptual
models Part I – a discussion of principles,” Journal of Hydrology, vol.
10, no. 3, pp. 282–290, 1970.
[29] S. Srinivasulu and A. Jain, "A comparative analysis of training methods
for artificial neural network rainfall-runoff models,” Applied Soft
Computing, vol. 6, pp. 295-306, 2006.
[30] J.D. Olden and D.A. Jackson, "Illuminating the "Black Box”: a
randomization approach for understanding variable contributions in
artificial neural networks.” Ecological Modeling ,vol. 154, pp. 135-150,
2002.
[31] G. Acciani, E. Chiarantoni and G. Fornarelli, A neural network
approach to study O3 and PM10 concentration, in: Kollias S, Staflopatis
A, Duch W, Oja E, editors. ICANN'06 Proceedings of the 16th
international conference on Artificial Neural Networks - Volume Part II,
Berlin, Germany: Springer Verlag, 2006, pp. 913-922.
[32] G.D. Garson, "Interpreting neural network connection
weights,”Artificial Intelligence Expert , vol. 6, pp. 47-51, 1991.
[33] M. Gevrey, I. Dimopoulos and S. Lek, "Review and comparison of
methods to study the contribution of variables in artificial neural
network models,” Ecological Modeling , vol. 160, pp. 249-264, 2003.
[34] J.J. Montano and A. Palmer, "Numeric sensitivity analysis applied to
feedforward neural networks,” Neural Computing and Applications, vol.
12, pp. 119-125, 2003.
@article{"International Journal of Architectural, Civil and Construction Sciences:68031", author = "Vinay Chandwani and Vinay Agrawal and Ravindra Nagar", title = "Modeling and Analysis of Concrete Slump Using Hybrid Artificial Neural Networks", abstract = "Artificial Neural Networks (ANN) trained using backpropagation
(BP) algorithm are commonly used for modeling
material behavior associated with non-linear, complex or unknown
interactions among the material constituents. Despite multidisciplinary
applications of back-propagation neural networks
(BPNN), the BP algorithm possesses the inherent drawback of
getting trapped in local minima and slowly converging to a global
optimum. The paper present a hybrid artificial neural networks and
genetic algorithm approach for modeling slump of ready mix
concrete based on its design mix constituents. Genetic algorithms
(GA) global search is employed for evolving the initial weights and
biases for training of neural networks, which are further fine tuned
using the BP algorithm. The study showed that, hybrid ANN-GA
model provided consistent predictions in comparison to commonly
used BPNN model. In comparison to BPNN model, the hybrid ANNGA
model was able to reach the desired performance goal quickly.
Apart from the modeling slump of ready mix concrete, the synaptic
weights of neural networks were harnessed for analyzing the relative
importance of concrete design mix constituents on the slump value.
The sand and water constituents of the concrete design mix were
found to exhibit maximum importance on the concrete slump value.
", keywords = "Artificial neural networks, Genetic algorithms,
Back-propagation algorithm, Ready Mix Concrete, Slump value.", volume = "8", number = "9", pages = "987-8", }
