پیش‌بینی مقاومت برشی تیرهای بتن مسلح با استفاده از الگوریتم‌های ANFIS و SVR

[1] Babar, V.T., Joshi, P.K., Shinde, D.N., "Shear strength of steel fiber reinforced concrete beam without stirrups", International Journal of Advanced Engineering Technology. 5(2), 15-18, 2015.

[2] Adolfo, B.M., Wong, K.H., "Design of simply supported deep beams using strut-and-tie models", ACI Structural Journal, 100(6), 704-712, 2003.

[3] Boyan, I.M., Evan, C.B., Michael. P.C., "Two-parameter kinematic theory for shear behavior of beep beams", ACI Structural Journal, 110(3), 447-456, 2013.

[4] Vapnik, V.N., "Statistical learning theory" , John Wiley and Sons; New York:1998.

[5] Cortes, C., Vapnik, V.N., "Support vector networks", Machine Learning. 20(3), 273-297, 1995.

[6] Toghroli, A., Mohammadhassani, M., Suhatril, M., Shariati, M., Ibrahim, Z., "Prediction of shear capacity of channel shear connectors using the ANFIS model", Steel and Composite Structures. 17(5), 623-639, 2014.

[7] Ozcan, F., Atis, C.D., Karahan, O., Uncuoglu, E. Tanyildizi,H., "Comparison of artificial neural network and fuzzylogic models for prediction of long-term compressive strengthof silica fume concrete", Advances in Engineering Software, 40(9), 856-863, 2009.

[8] Keshavarz, Z., Torkian, H., "Application of ANN and ANFIS models in determining compressive strength of concrete", Journal of Soft Computing in Civil Engineering. 2(1), 62-70, 2018.

 [9] Mansour, M.Y., Dicleli, M., Lee, J.Y., Zhang, J., "Predicting the shear strength of reinforced concrete beams using artificial neural networks", Engineering Structures. 26(6), 781-799, 2003.

 [10] Prayogo, D., Cheng, M.Y., Wu, Y.W., Tran, D.H., "Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams", Engineering with Computers. 1-19, 2019.

[11] Lee, J.J, Kim, D.K., Chang, S.K., Lee, J.H., "Application of support vector regression for the prediction of concrete strength", Computers and Concrete. 4(4), 299-316,2007.

[12] Mozumder, R.A, Roy, B., Laskar, A.L., "Support Vector Regression Approach to Predict the Strength of FRP Confined Concrete", Arabian Journal for Science and Engineering, 42, 1129-1146 ,2017.

[13] Pham, B.T., Hoang, T.A., Nguyen, D.M., Bui, D.T., "Prediction of shear strength of soft soil using machine learning methods", Catena, 166, 181-191, 2018.

[14] American Concrete Institute (ACI). Committee 318-11: Building Code Requirements for Structural Concrete and Commentary, American Concrete Institute, 2011.

[15] Canadian Standards Association (CSA). Design of concrete structures: Structures (design), A national standard of Canada. CAN-A23.3-94, Clause11.1.2, Toronto, 1994.

[16] Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J., Vapnik, V., "Support vector regression machines", In Advances in Neural Information Processing Systems, 28(7) 779-784, 1997.

[17] Guan, J., Zurada, J., Levitan, A., "An Adaptive Neuro fuzzy inference system based approach to real estate property assessment", Journal of Real Estate Research, 30(4), 395-422, 2008.

[18] Jang, J.S., "ANFIS: adaptive-network-based fuzzy inference system", IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685, 1993.

[19] Zhou, Q., Zhu, F., Yang, X., Wang, F., Chi, B., Zhang, Z., "Shear capacity estimation of fully grouted reinforced concrete masonry walls using neural network and adaptive neuro-fuzzy inference system models", Construction and Building Materials, 153, 937-947, 2017.

[20] Mohammadhassani, M., Nezamabadi-Pour, H., Suhatril, M., Shariati, M., "An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups", Smart Structures and systems,14(5) 785-809, 2014.

 [21] Chai, T., Draxler, R.R., "Root mean square error (RMSE) or mean absolute error (MAE)–arguments against avoiding RMSE in the literature", Geoscientific model development, 7)3(,  1247-1250, 2014.

[1] Babar, V.T., Joshi, P.K., Shinde, D.N., "Shear strength of steel fiber reinforced concrete beam without stirrups", International Journal of Advanced Engineering Technology. 5(2), 15-18, 2015.

[2] Adolfo, B.M., Wong, K.H., "Design of simply supported deep beams using strut-and-tie models", ACI Structural Journal, 100(6), 704-712, 2003.

[3] Boyan, I.M., Evan, C.B., Michael. P.C., "Two-parameter kinematic theory for shear behavior of beep beams", ACI Structural Journal, 110(3), 447-456, 2013.

[4] Vapnik, V.N., "Statistical learning theory" , John Wiley and Sons; New York:1998.

[5] Cortes, C., Vapnik, V.N., "Support vector networks", Machine Learning. 20(3), 273-297, 1995.

[6] Toghroli, A., Mohammadhassani, M., Suhatril, M., Shariati, M., Ibrahim, Z., "Prediction of shear capacity of channel shear connectors using the ANFIS model", Steel and Composite Structures. 17(5), 623-639, 2014.

[7] Ozcan, F., Atis, C.D., Karahan, O., Uncuoglu, E. Tanyildizi,H., "Comparison of artificial neural network and fuzzylogic models for prediction of long-term compressive strengthof silica fume concrete", Advances in Engineering Software, 40(9), 856-863, 2009.

[8] Keshavarz, Z., Torkian, H., "Application of ANN and ANFIS models in determining compressive strength of concrete", Journal of Soft Computing in Civil Engineering. 2(1), 62-70, 2018.

 [9] Mansour, M.Y., Dicleli, M., Lee, J.Y., Zhang, J., "Predicting the shear strength of reinforced concrete beams using artificial neural networks", Engineering Structures. 26(6), 781-799, 2003.

 [10] Prayogo, D., Cheng, M.Y., Wu, Y.W., Tran, D.H., "Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams", Engineering with Computers. 1-19, 2019.

[11] Lee, J.J, Kim, D.K., Chang, S.K., Lee, J.H., "Application of support vector regression for the prediction of concrete strength", Computers and Concrete. 4(4), 299-316,2007.

[12] Mozumder, R.A, Roy, B., Laskar, A.L., "Support Vector Regression Approach to Predict the Strength of FRP Confined Concrete", Arabian Journal for Science and Engineering, 42, 1129-1146 ,2017.

[13] Pham, B.T., Hoang, T.A., Nguyen, D.M., Bui, D.T., "Prediction of shear strength of soft soil using machine learning methods", Catena, 166, 181-191, 2018.

[14] American Concrete Institute (ACI). Committee 318-11: Building Code Requirements for Structural Concrete and Commentary, American Concrete Institute, 2011.

[15] Canadian Standards Association (CSA). Design of concrete structures: Structures (design), A national standard of Canada. CAN-A23.3-94, Clause11.1.2, Toronto, 1994.

[16] Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J., Vapnik, V., "Support vector regression machines", In Advances in Neural Information Processing Systems, 28(7) 779-784, 1997.

[17] Guan, J., Zurada, J., Levitan, A., "An Adaptive Neuro fuzzy inference system based approach to real estate property assessment", Journal of Real Estate Research, 30(4), 395-422, 2008.

[18] Jang, J.S., "ANFIS: adaptive-network-based fuzzy inference system", IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685, 1993.

[19] Zhou, Q., Zhu, F., Yang, X., Wang, F., Chi, B., Zhang, Z., "Shear capacity estimation of fully grouted reinforced concrete masonry walls using neural network and adaptive neuro-fuzzy inference system models", Construction and Building Materials, 153, 937-947, 2017.

[20] Mohammadhassani, M., Nezamabadi-Pour, H., Suhatril, M., Shariati, M., "An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups", Smart Structures and systems,14(5) 785-809, 2014.

 [21] Chai, T., Draxler, R.R., "Root mean square error (RMSE) or mean absolute error (MAE)–arguments against avoiding RMSE in the literature", Geoscientific model development, 7)3(,  1247-1250, 2014.