Abstract
Interest on artificial neural networks (ANN) in infrastructure materials research and practice has increased in recent years. This chapter presents a review of ANN applications in characterization of infrastructure materials focusing on portland cement concrete (PCC) and asphalt concrete (AC) materials. The principles of ANN are briefly introduced and summarized. The strengths and limitations of ANN for modeling behavior of infrastructure materials are discussed. Various applications of the ANN approach in infrastructure materials testing, analysis and design problems are discussed.
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References
Adeli, H., Hung, S.L.: Machine learning: neural networks, genetic algorithms, and fuzzy systems. Wiley, New York (1995)
Adeli, H.: Neural networks in civil engineering: 1989-2000. Computer-Aided Civil and Infrastructure Engineering 16, 126–142 (2001)
Aleksander, I., Morton, H.: An introduction to neural computing. Van Nostrand Reinhold Co., New York (1990)
Andrei, D., Witczak, M.W., Mirza, M.W.: Development of a revised predictive model for the dynamic (complex) modulus of asphalt mixtures. NCHRP 1-37 A Inter Team Report, University of Maryland, College Park, Maryland (1999)
Asphalt Institute, The asphalt handbook. MS-4, Lexington, KY (1989)
Asphalt Institute, Superpave mix design. SP-2, Lexington, KY (2001)
Bari, J., Witczak, M.W.: Development of a new revised version of the Witczak E* predictive model for hot mix asphalt mixtures. Journal of the Association of Asphalt Paving Technologists 75, 381–424 (2006)
Bai, J., Wild, S., Ware, J.A., Sabir, B.B.: Using neural networks to predict workability of concrete incorporating metakaolin and fly ash. Advances in Engineering Software 34, 663–669 (2003)
Basma, A.A., Barakat, S., Al-Oraimi, S.: Prediction of cement degree of hydration using artificial neural networks. ACI Materials Journal 96(2), 167–172 (1999)
Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press Inc., New York (1995)
Ceylan, H., Kim, S., Gopalakrishnan, K.: Hot mix asphalt dynamic modulus prediction models using neural network approach. In: Dagli, C.H. (ed.) Intelligent Engineering Systems through Artificial Neural Networks, Proceedings of the ANNIE 2007, vol. 17, pp. 117–124. American Society of Mechanical Engineers (2007)
Ceylan, H., Gopalakrishnan, K., Kim, S.: Advanced approaches to hot-mix asphalt dynamic modulus prediction. Canadian Journal of Civil Engineering 35(7), 699–707 (2008)
Ceylan, H., Schwartz, C.W., Kim, S., Gopalakrishnan, K.: Accuracy of predictive models for dynamic modulus of hot mix asphalt. ASCE Journal of Materials in Civil Engineering 21(6), 286–293 (2009)
Christensen, D.W., Pellinen, T., Bonaquist, R.F.: Hirsch model for estimating the modulus of asphalt concrete. Journal of the Association of Asphalt Paving Technologists 72, 97–121 (2003)
Commuri, S., Zaman, M.: A novel neural network-based asphalt compaction analyzer. International Journal of Pavement Engineering 9(3), 177–188 (2008)
Dias, W.P.S., Pooliyadda, S.P.: Neural networks for predicting properties of concretes with admixtures. Construction and Building Materials 15(7), 371–379 (2001)
El-Chabib, H., Nehdi, M., Sonebi, M.: Artificial intelligence model for flowable concrete mixtures used in underwater construction and repair. ACI Materials Journal 100(2), 165–173 (2003)
Fahlman, S.E., Lebiere, C.: The cascade-correlation learning architecture. Rep. CMU-CS-90-100. Carnegie Mellon Univ., Pittsburgh (1990)
Fausett, L.V.: Fundamentals of neural networks, 1st edn. Prentice-Hall, NJ (1994)
Fazel Zarandi, M.H., Türksen, I.B., Sobhani, J., Ramezanianpour, A.A.: Fuzzy polynomial neural networks for approximation of the compressive strength of concrete. Applied Soft Computing 8(1), 488–498 (2008)
Fletcher, P., Coveney, P.: Prediction of thickening times of oil field cements using artificial neural networks and fourier transform infrared spectroscopy. Advanced Cement Based Materials 2(1), 21–29 (1995)
Ghaboussi, J., Garrett Jr., J.H., Wu, X.: Knowledge-Based Modeling of Material Behavior with Neural Networks. ASCE J. Engrg. Mech. 117(1), 132–153 (1991)
Gupta, R., Kewalramani, M.A., Goel, A.: Prediction of concrete strength using neural-expert System. ASCE J. Mat. in Civ. Engrg. 18(3), 462–466 (2006)
Haj-Ali, R.M., Kurtis, K.E., Sthapit, A.R.: Neural network modeling of concrete expansion during long-term sulfate exposure. ACI Materials Journal 98(1), 36–43 (2001)
Haykin, S.: Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle River (1998)
Hegazy, T., Fazio, P., Moselhi, O.: Developing practical neural network applications using backpropagation. Microcomputers in Civil Engineering 9(2), 145–159 (1994)
Hejazi, S.M., Abtahi, S.M., Sheikhzadeh, M., Semnani, D.: Introducing two simple models for predicting fiber-reinforced asphalt concrete behavior during longitudinal loads. Journal of Applied Polymer Science 109(5), 2872–2881 (2008)
Ji-Zong, W., Hong-Guang, H., Jin-Jun, H.: The application of automatic acquisition of knowledge to mix design of concrete. Cem. Concr. Res. 29(12), 1875–1880 (1999)
Kasperkiewicz, J., Racz, J., Dubrawski, A.: HPC strength prediction using artificial neural network. ASCE J. Comp. in Civ. Engrg. 9(4), 279–284 (1995)
Kasperkiewicz, J.: The applications of ANNs in certain materials-analysis problems. Journal of Materials Processing Technology 106, 74–79 (2000)
Kewalramani, M.A., Gupta, R.: Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Automation in Construction 15(3), 374–379 (2006)
Kim, D.K., Lee, J.J., Lee, J.H., Chang, S.K.: Application of probabilistic neural networks for prediction of concrete strength. ASCE J. Mat. in Civ. Engrg. 17(3), 353–362 (2005)
Kim, J.I., Kim, D.K., Feng, M.Q., Yazdani, F.: Application of neural networks for estimation of concrete strength. ASCE J. Mat. in Civ. Engrg. 16(3), 257–264 (2004)
Kosmatka, S.H., Kerkhoff, B., Panarese, W.C.: Design and control of concrete mixtures, 14th edn. Portland Cement Association, Skokie (2002)
Kutay, M.E., Arambula, E., Gibson, N., Youtcheff, J., Petros, K.: Use of artificial neural networks to detect aggregates in poor-quality X-ray CT images of asphalt concrete. In: Roesler, J.R., Bahia, H.U., Al-Qadi, I.L., Murrell, S.D. (eds.) Airfield and highway pavements: efficient pavements supporting transportation’s future, Proceedings of the 2008 Airfield and Highway Pavements Conference, Bellevue, Washington, pp. 40–51 (2008)
Lai, S., Serra, M.: Concrete strength prediction by means of neural network. Construction and Building Materials 11(2), 93–98 (1997)
Lee, S.C.: Prediction of concrete strength using artificial neural networks. Engineering Structures 25(7), 849–857 (2003)
Livingstone, D.J., Manallack, D.T., Tetko, I.V.: Data modelling with neural networks: Advantages and limitations. Journal of Computer-Aided Molecular Design 11(2), 135–142 (1997)
Mehra, P., Wah, B.W.: Artificial neural networks: concepts and theory. IEEE Computer Society Press, Los Alamitos (1992)
Mehrotra, K., Mohan, C.K., Ranka, S.: Elements of artificial neural networks. MIT Press, Cambridge (1997)
Michon, L., Hanquet, B., Diawara, B., Martin, D., Planche, J.P.: Asphalt study by neuronal networks correlation between chemical and rheological properties. Energy Fuels 11(6), 1188–1193 (1997)
Mukherjee, A., Nag Biswas, S.: Artificial neural networks in prediction of mechanical behavior of concrete at high temperature. Nuclear engineering and design 178(1), 1–11 (1997)
NCHRP, Guide for mechanistic-empirical design of new and rehabilitated pavement structures. National Cooperative Highway Research Program 1-37 A Project Report, Transportation Research Board, National Research Council, Washington DC (2004)
Nehdi, M., Djebbar, Y., Khan, A.: Neural network model for preformed-foam cellular concrete. ACI Materials Journal 98(5), 402–409 (2001a)
Nehdi, M., Chabib, H.E., Naggar, M.H.E.: Predicting performance of self-compacting concrete mixtures using artificial neural networks. ACI Materials Journal 98(5), 394–401 (2001b)
Ni, H.-G., Wang, J.-Z.: Prediction of compressive strength of concrete by neural networks. Cement and Concrete Research 30(8), 1245–1250 (2000)
Oh, J., Lee, I., Kim, J., Lee, G.: Applications of neural networks for proportioning of concrete mixes. ACI Mater. J. 96(1), 51–59 (1999)
Ozsahin, T.S., Oruc, S.: Neural network model for resilient modulus of emulsified asphalt mixtures. Construction and Building Materials 22(7), 1436–1445 (2008)
Öztaş, A., Pala, M., Özbay, E., Kanca, E., Çagľar, N., Bhatti, M.A.: Predicting the compressive strength and slump of high strength concrete using neural network. Construction and Building Materials 20(9), 769–775 (2006)
Pala, M., Özbay, E., Öztaş, A., Yuce, M.I.: Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Construction and Building Materials 21(2), 384–394 (2007)
Parichatprecha, R., Nimityongskul, P.: Analysis of durability of high performance concrete using artificial neural networks. Construction and Building Materials 23(2), 910–917 (2009)
Park, K.B., Noguchi, T., Plawsky, J.: Modeling of hydration reactions using neural networks to predict the average properties of cement paste. Cement and Concrete Research 35(9), 1676–1684 (2005)
Peng, J., Li, J., Ma, B.: Neural network analysis of chloride diffusion in concrete. ASCE J. Mat. in Civ. Engrg. 14(4), 327–333 (2002)
Prasad, B.K.R., Eskandari, H., Reddy, B.V.V.: Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Construction and Building Materials 23(1), 117–128 (2009)
Rafiq, M.Y., Bugmann, G., Easterbrook, D.J.: Neural network design for engineering applications. Computers & Structures 79(17), 1541–1552 (2001)
Sakhaei Far, M.S., Underwood, B.S., Kim, R.: Application of artificial neural networks for estimating dynamic modulus of asphalt concrete. Paper #09-3799, Proceedings of Transportation Research Board Annual Meeting, Transportation Research Board, National Research Council, Washington DC (2009)
Sebastiá, M., Fernández Olmo, I., Irabien, A.: Neural network prediction of unconfined compressive strength of coal fly ash–cement mixtures. Cement and Concrete Research 33(8), 1137–1146 (2003)
Sergio, L., Mauro, S.: Concrete strength prediction by means of neural network. Constr. Build. Mater. 11(2), 93–98 (1997)
Shahin, M.A., Jaksa, M.B., Maier, H.R.: Artificial neural network applications in geotechnical engineering. Australian Geomechanics 36(1), 49–62 (2001)
Specht, L.P., Khatchatourian, O., Brito, L.A.T., Ceratti, J.A.P.: Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks. Materials Research 10(1), 69–74 (2007)
Stegemann, J.A., Buenfeld, N.R.: Neural network modelling of the effects of inorganic impurities on calcium aluminate cement setting. Advances in Cement Research 13(3), 101–114 (2001)
Swingler, K.: Applying neural networks: a practical guide. Academic Press, London (1996)
Tarefder, R.A., White, L., Zaman, M.: Neural network model for asphalt concrete permeability. ASCE Journal of Materials in Civil Engineering 17, 19–27 (2005)
Topçu, İ.B., Sarıdemir, M.: Prediction of properties of waste AAC aggregate concrete using artificial neural network. Computational Materials Science 41(1), 117–125 (2007)
Topçu, İ.B., Sarıdemir, M.: Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Construction and Building Materials 22(4), 532–540 (2008)
Topçu, İ.B., Karakurt, C., Sarıdemira, M.: Predicting the strength development of cements produced with different pozzolans by neural network and fuzzy logic. Materials & Design 29(10), 1986–1991 (2008)
Topping, B.H.V., Bahreininejad, A.: Neural computing for structural mechanics. Saxe-Coburg Publications, Edinburgh (1997)
TRB Circular. Use of artificial neural networks in geomechanical and pavement systems. Number E-C012. Transportation Research Board, National Research Council, Washington DC (1999)
Tsoukalas, L.H., Uhrig, R.E.: Fuzzy and neural approaches in engineering. Wiley, New York (1997)
Tu, J.V.: Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49(11), 1225–1231 (1996)
Uomoto, T., Ohya, T., Tsutsumi, T.: Development of new concrete mixing system using neural network. In: Proceedings of the CONSEC Conference, pp. 282–290 (1998)
Ukrainczyk, N., Ukrainczyk, V.: A neural network method for analysing concrete durability. Magazine of Concrete Research 60(7), 475–486 (2008)
Wilson, J.D., Koltz, L.D., Nagaraj, C.: Automated measurement of aggregate indices of shape. Report FHWA-RD-95-116. FHWA, U.S. Department of Transportation, Washington DC (1995)
Wittmann, F.H., Martinola, G.: Optimization of concrete properties by neural networks. In: Dhir, R.K., Jones, M.R. (eds.) Concrete 2000-economic and durable construction through excellence, Proceedings of the International Conference, Dundee, E &FN Spon, London, pp. 1889–1898 (1993)
Wu, X., Garrett Jr., J.H., Ghaboussi, J.: Representation of material behavior: neural network-based models. In: IJCNN, International Joint Conference on Neural Networks, vol. 1, pp. 229–234 (1990)
Xiao, F., Amirkhanian, S.N.: Effects of binders on resilient modulus of rubberized mixtures containing RAP using artificial neural network approach. ASTM Journal of Testing and Evaluation 37(2) (2009)
Yeh, I.-C.: Modeling concrete strength with augment-neuron networks. ASCE Journal of Materials in Civil Engineering 10(4), 263–268 (1998a)
Yeh, I.-C.: Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete Research 28(12), 1797–1808 (1998b)
Yeh, I.-C.: Design of high-performance concrete mixture using neural networks and nonlinear programming. ASCE J. Comput. Civil Eng. 13(1), 36–42 (1999)
Zeghal, M.: Modeling the creep compliance of asphalt concrete using the artificial neural network technique. In: Annual Congress of the Geo-Institute of ASCE, GeoCongress 2008, New Orleans, pp. 1–7 (2008)
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Kim, S., Gopalakrishnan, K., Ceylan, H. (2009). Neural Networks Application in Pavement Infrastructure Materials. In: Gopalakrishnan, K., Ceylan, H., Attoh-Okine, N.O. (eds) Intelligent and Soft Computing in Infrastructure Systems Engineering. Studies in Computational Intelligence, vol 259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04586-8_3
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