Shirgir, B., Mamdoohi, A., Hassani, A. (2015). Prediction of Pervious Concrete Permeability and Compressive Strength Using Artificial Neural Networks. International Journal of Transportation Engineering, 2(4), 307-316. doi: 10.22119/ijte.2015.10444

Behrooz Shirgir; Amir Reza Mamdoohi; Abolfazl Hassani. "Prediction of Pervious Concrete Permeability and Compressive Strength Using Artificial Neural Networks". International Journal of Transportation Engineering, 2, 4, 2015, 307-316. doi: 10.22119/ijte.2015.10444

Shirgir, B., Mamdoohi, A., Hassani, A. (2015). 'Prediction of Pervious Concrete Permeability and Compressive Strength Using Artificial Neural Networks', International Journal of Transportation Engineering, 2(4), pp. 307-316. doi: 10.22119/ijte.2015.10444

Shirgir, B., Mamdoohi, A., Hassani, A. Prediction of Pervious Concrete Permeability and Compressive Strength Using Artificial Neural Networks. International Journal of Transportation Engineering, 2015; 2(4): 307-316. doi: 10.22119/ijte.2015.10444

Prediction of Pervious Concrete Permeability and Compressive Strength Using Artificial Neural Networks

^{1}Faculty of Engineering, Kharazmi University, Tehran, Iran

^{2}Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Pervious concrete is a concrete mixture prepared from cement, aggregates, water, little or no fines, and in some cases admixtures. The hydrological property of pervious concrete is the primary reason for its reappearance in construction. Much research has been conducted on plain concrete, but little attention has been paid to porous concrete, particularly to the analytical prediction modeling of its permeability. In this paper, two important aspects of pervious concrete due to permeability and compressive strength are investigated using artificial neural networks (ANN) based on laboratory data. The proposed network is intended to represent a reliable functional relationship between the input independent variables accounting for the variability of permeability and compressive strength of a porous concrete. Results of the Back Propagation model indicate that the general fit and replication of the data regarding the data points are quite fine. The R-square goodness of fit of predicted versus observed values range between 0.879 and 0.918 for the final model; higher values were observed for the permeability as compared with compressive strength and for the train data set rather than the test data set. The findings can be employed to predict these two important characteristics of pervious concrete when there are no laboratorial data available.

-Aamer Rafique Bhutta, M., Tsuruta, K. and Mirza, J. (2012) “Evaluation of high-performance porous concrete properties”, Construction and Building Materials, vol.31, pp.67- 73.

-Adhikary, B. B. and Mutsuyoshi H. (2006) “Prediction of shear strength of steel fiber RC beams using neural networks”, Construction & Building Materials. vol. 20, pp. 801-811.

-Anderson, J. A. and Brown, P. (1983) “Cognitive and psychological computation with neural models”, IEEE Trans. Syst., Man, Cybern., vol. 5, pp. 799-815.

-Ferguson, B. K. (2005) “Porous pavements”, Boca Raton, Fla.: CRC Press, Taylor & Francis.

-Fukute, T. (1998) “Reduction of environmental load by water penetration”, Concrete Journal, vol. 36:3, pp. 16–18.

-Gesog˘lu, M., Guneyisi, E., Khoshnaw G. and Ipek, S. (2014) “Investigating properties of pervious concretes containing waste tire rubbers”, Construct Build Mater; vol. 63, pp. 206–13.

-Ghafoori, N. and Dutta, S. (1995) “Development of no-fines concrete pavement applications”, Journal of Transportation Engineering, vol. 121, p. 283.

-Hagan, M. T., Demuth, H. B. and Beale, M. H. (1996) “Neural Network Design”, Boston: PWS Pub.

-Hola. J. and Schabowicz, K. (2005) “New technique of nondestructive assessment of concrete strength using artificial intelligence”, NDT & E International, vol. 38, pp. 251-259.-Hopfield, J. (1982) “Neural networks and physical systems with emergent collective computational abilities”, Proceedings of the National Academy of Sciences of the United States of America, vol. 79, pp. 2554-8.

-John, T. and Vernon, R. (2013) “Mixture properties considerations for improved freeze–thaw durability of pervious concrete”, Planning for Sustainable Cold Regions, Proceedings.

-Kewalramani, M. A. and Gupta, R. (2006) “Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks”, Automation in Construction, vol. 15, pp. 374-379.

-Lee, S. C. (2003) “Prediction of concrete strength using artificial neural networks”, Engineering Structures, vol. 25, pp. 849-857.

-Lippmann, R. (1987) “An introduction to computing with neural nets”, ASSP Magazine, IEEE, vol. 4, pp. 4-22.

-McCulloch, W. S. and Pitts, W. (1990) “A logical calculus of the ideas immanent in nervous activity”, Bulletin of Mathematical Biology, vol. 52, pp. 1-2.

-Neithalath, N., Weiss, J. and Olek, J. (2006). “Characterizing Enhanced Porosity Concrete using electrical impedance to predict acoustic and hydraulic performance”, Cement and Concrete Research, vol. 36, pp. 2074-2085.

-Nguyen, D. H., Boutouil, M., Sebaibi, N., Leleyter, L. and Baraud, F. (2013) “Valorization of seashell by-products in pervious concrete pavers”, Constr Build Mater; 49:PP.151–60.

-Pala, M., Ozbay, E., Oztas A. and Yuce M. I. (2007) “Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks”, Construction and Building Materials, vol. 21, pp. 384-394.

-Schaefer, V. R. (2006) “Mix design development for pervious concrete in cold weather climates”, Center for Transportation Research and Education, Iowa State University.

-Shirgir, B., Hassani, A. and Khodadadi, A. (2011) “Experimental study on permeability and mechanical properties of nano-modified porous concrete”, Transportation Research Record: Journal of the Transportation Research Board , 2011, Vol.2240 (-1), pp.30-35.

-Tennis, P. D., Leming M. L. and Akers D. J. (2004) “Pervious concrete pavements”, Skokie, I11.: Portland Cement Association.

-Topçu, I. B. and SarIdemir, M. (2008) “Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic”, Computational Materials Science, vol. 41, pp. 305-311.

-Wu X. and Lim, S.Y. (1993) “Prediction maximum scour depth at the spur dikes with adaptive neural networks”, in Neural Networks and Combinatorial Optimization in Civil and Structural Engineering, Edinburgh, pp. 61–66.