مدل سازی انرژی ضربه ی فولادهای مرتبه ای با استفاده از شبکه های عصبی مصنوعی

 

[1] Sankar, B.V. (2001). An elasticity solution for functionally graded beams, Composites Science and Technology (Elsevier), Vol. pp. 689–696.

[2] Li, H., Lambros, J., Cheeseman, B.A., Santare, M.H. (2000). Experimental investigation of the quasi-static fracture of functionally graded materials, International Journal of Solids and Structures, Vol. 37, pp. 3715-3732, 2000.

[3] Nagata, F. (1999). Intelligent modelling mechanisms and design concepts of FGMs in natural composites, Materials Science Forum, Vol. 308-311, pp. 331-337.

[4] Gibson, R.E. (1967). Some results concerning displacements and stresses in a nonhomogeneous elastic half space, Geotechnique, Vol. 17, pp. 58-67.

[5] Ferrari, M., Rooney, F., Nadeau, J.C., (1999). Optimal FGMs and plain awful composites, Materials Science Forum, Vol. 308-311, pp. 989-994.

[6] Aghazadeh, J.-M., Shahosseinie, M.H. (2005). Transformation Characteristics of Functionally Graded Steels Produced By Electrosag Remelting, Metallurgical and Materials Transactions A (Springer), Vol. 36A, pp. 3471-3476.

[7] Nazari, A., Aghazadeh, J.-M. (2010). Modelling impact resistance of functionally graded steels with crack divider configuration, Materials Science and Technology (Maney), Vol. 26, pp. 1377-1383.

[8] Nazari, A., Aghazadeh, J.-M., Hamid, M.-V., Abedi, M. (2011). Simulation of impact energy in functionally graded steels, Computational Materials Science (Elsevier), Vol. 50, pp. 1187–1196.

[9] Nazari, A., Aghazadeh, J.-M., Riahi, S. (2011). Modeling fracture toughness of functionally graded steels in crack arrester configuration, Computational Materials Science (Elsevier), Vol. 50, pp. 1578–1586.

[10] Pala, M., Ozbay, O., Oztas, A., Yuce, M.I. (2005). Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks, Construction and Building Materials (Elsevier) Vol. 21, pp. 384–394.

[11] Akkurt, S., Ozdemir, S., Tayfur, G., Akyol, B. (2003). The use of GA-ANNs in the modelling of compressive strength of cement mortar, Cement and Concrete Research (Elsevier), Vol. 33, pp. 973–979.

[12] شفابخش، غ، نادرپور، ح، فصیحی, ف. (1389). انتخاب الگوریتم بهینه شبکه عصبی در تحلیل روسازی های انعطاف پذیر راه ها، مجله مدل سازی در مهندسی، دانشگاه سمنان، سال هشتم، شماره  21.

[13] Nazari, A., Pacheco-Torgal, F. (2012). Predicting compressive strength of different geopolymers by artificial neural networks, Ceramics International (Elsevier), DOI: 10.1016/j.ceramint.2012.08.070.

[14] Nazari, A., Sedghi, A., Didehvar, N. (2012) Modeling impact resistance of aluminum-epoxy laminated composites by artificial neural networks, Journal of Composite Materials (sage), Vol. 46, pp. 1593–1605.

[15] Bohlooli, H., Nazari, A., Kaykha, M.M. (2012) Microhardness profile prediction of functionally graded steels by artificial neural networks, International Journal of Damage Mechanics (Sage), Vol. 21, pp. DOI: 10.1177/1056789511432653.

[16] Topcu, I.B., Sarıdemir, M. (2008). Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic, Computational Materials Science (Elsevier) Vol. 42, pp. 74–82.

[17] Dias, W.P.S., Pooliyadda, S.P. (2001). Neural networks for predicting properties of concretes with admixtures, Construction and Building Materials (Elsevier), Vol. 15, pp. 371–379.

[18] Bai, J., Wild, S., Ware, J.A., Sabir, B.B. (2003). Using neural networks to predict workability of concrete incorporating metakaolin and fly ash, Advances in Engineering Software, Vol. 34, pp. 663–669.

[19] Lai, S., Serra, M. (1997) Concrete strength prediction by means of neural network, Construction and Building Materials (Elsevier), Vol. 11, pp. 93–98.

[20] Sebastia, M., Olmo, I.F., Irabien, A. (2003). Neural network prediction of unconfined compressive strength of coal fly ash–cement mixtures, Cement and Concrete Research, Vol. 33, pp. 1137–1146.

[21] Nazari, A., Milani, A.A. (2011), Modeling ductile to brittle transition temperature of functionally graded steels by fuzzy logic, Journal of Materials Science (Springer), Vol. 46, pp. 6007–6017.

[22] Mukherjee, A. Biswas, S.N. (1997). Artificial neural networks in prediction of mechanical behavior of concrete at high temperature, Nuclear Engineering Design (Elsevier), Vol. 178,  pp. 1–11.

[23] Ince, R. (2004). Prediction of fracture parameters of concrete by artificial neural networks, Engineering Fracture Mechanics (Elsevier), Vol. 71, pp. 2143–2159.

[24] McCulloch, W.S., Pitts, W. (1943). A logical calculus of the ideas immanent in neural nets, Bulletin of Mathematics Biophysics, Vol. 5, pp. 115–137.

[25] Rosenblatt, F. (1962). Principles of neuro dynamics: perceptrons and the theory of brain mechanisms, Washington, DC, Spartan Book.

[26] Rumelhart, D.E., Hinton, G.E., William, R.J. (1986). Learning internal representation by error propagation, In: Rumelhart DE, McClelland JL, editors. Proceeding parallel distributed processing foundation, Vol. 1. Cambridge: MIT Press.

[27] Liu, S.W., Huang, J.H., Sung, J.C., Lee, C.C. (2002). Detection of cracks using neural networks and computational mechanics, Computer Methods in Applied Mechanics and Engineering (Elsevier), Vol. 191, pp. 2831–2845.

[28] Anderson, J.A. (1983). Cognitive and psychological computation with neural models, IEEE Transactions on Systems and Manufacturing Cybernetics, V.SMC-13, Vol. 5, pp. 799–814.

[29] Suratgar, A.A., Tavakoli, M.B., Hoseinabadi, A. (2005). Modified Levenberg–Marquardt method for neural networks training, World Academy of Science, Engineering and Technology, Vol. 6, pp. 46–48.

[30] Guzelbey, I.H., Cevik, A., Erklig, A. (2006). Prediction of web crippling strength of cold-formed steel sheetings using neural Networks, Journal of Constructional Steel Research (Elsevier), Vol. 62, pp. 962-973.