[1] A.H. Gandomi, G.J. Yun, A.H. Alavi, An evolutionary approach for modeling of shear strength of RC deep beams, Materials and Structures, 46(12), (2013),2109-2119.
[2] D. Tien Bui, V.H. Nhu, N.D. Hoang, Prediction of soil compression coefficient for urban housing project using novel integration machine learning approach of swarm intelligence and Multi-layer Perceptron Neural Network, Advanced Engineering Informatics, 38, (2018), 593–604.
[3] S. Moosazadeh, E. Namazi, H. Aghababaei, A. Marto, H. Mohamad, M. Hajihassani, Prediction of building damage induced by tunnelling through an optimized artificial neural network, Engineering with Computers, 35, (2018), 579–591.
[4] S.V. Alavi Nezhad Khalil Abad, M. Yilmaz, D. Jahed Armaghani, A. Tugrul, Prediction of the durability of limestone aggregates using computational techniques, Neural Computing and Applications, 29(2), (2016), 423–433, 2016.
[5] P.G. Asteris, M. Nikoo, Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures, Neural Computing and Applications, 31(9), (2019), 4837-4847.
[6] J.S. Chou, A.D. Pham, Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength, Construction and Building Materials, 49,(2013), 554–563.
[7] T. Hancock, R. Put, D. Coomans, Y. Vander Heyden, Y. Everingham, A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies, Chemometrics and Intelligent Laboratory Systems, 76(2), (2005), 185–196.
[8] V.N. Vapnik, The nature of statistical learning theory, Springer, New York, 1995.
[9] R. Chen, P. Zhang, H. Wu, Prediction of shield tunneling induced ground settlement using machine learning techniques, Frontiers of Structural and Civil Engineering, 13(6), (2019), 1363-1378.
[10] Z. Liu, D. Wu, Y. Liu, Z. Han, L. Lun, J. Gao, G. Jand, G. Cao, Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction, Energy Exploration & Exploitation, 37(4), (2019),1426-1451.
[11] E. Acar, M. Rais-Rohani, Ensemble of metamodels with optimized weight factors, Structural and Multidisciplinary Optimization, 37(3), (2008), 279–294.
[12] J.S. Chou, K.H. Yang, J.Y. Lin, Peak shear strength of discrete fiber-reinforced soils computed by machine learning and metaensemble methods, Journal of Computing in Civil Engineering, 30(6),(2016).
[13] N.D. Hoang, X.L. Tran, H. Nguyen, Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model, Neural Computing and Applications, (2019), 1-21.
[14] M. Khatibinia, M. Araghi, Modeling of flow number of asphalt mixtures using a multi-kernal based support vector machine approach, International Journal of Optimization in Civil Engineering, 9(2), (2019), 233-250.
[15] J.A.K. Suykens, T.V. Gestel, J.D. Brabanter, B.D. Moor, J. Vandewalle, Least squares support vector machines, World Scientifc Publishing Company, Singapore, 2002.
[16] D. Tien Bui, T.A. Tuan, N.D. Hoang, N.Q. Thanh, D.B. Nguyen, N. Van Liem, B. Pradhan, Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization, Landslides, 14(2), (2016), 447–458.
[17] D. Prayogo, M.Y. Cheng, J. Widjaja, H. Ongkowijoyo, H. Prayogo, Prediction of concrete compressive strength from early age test result using an advanced metaheuristic-based machine learning technique, international symposium on automation and robotics in construction, (2017), 856–863.
[18] M.Y. Cheng, D. Prayogo, Y.W. Wu, Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression, Neural Computing and Applications, 31(10), (2018), 6261.6273.
[19] I. Aljarah, A.M. Al-Zoubi, H. Faris, M.A. Hassonah, S. Mirjalili, H. Saadeh, Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm, Cognitive Computation, 10(3), (2018), 478–495.
[20] H. Faris, M.A. Hassonah, A.M. Al-Zoubi, S. Mirjalili, I. Aljarah, A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture, Neural Computing and Applications,30(8), (2017), 2355–2369.
[21] N.D. Hoang, A.D. Pham, Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: A multinational data analysis, Expert Systems with Applications, 46, (2016), 60–68,
[22] J.A. Suykens, J. De Brabanter, L. Lukas, J. Vandewalle, Weighted least squares support vector machines: robustness and sparse approximation, Neurocomput, 48(1), (2002), 85-105.
[23] ACI-318, ACIC 318-11: Building Code Requirements for Structural Concrete and Commentary. American Concrete Institute, 2011.
[24] Canadian Standards Association (CSA), Design of concrete structures: Structures (design)—a national standard of Canada. CAN-A23.3-94, Clause11.1.2, Toronto, 1994.
[25] A.P. Clark, Diagonal tension in reinforced concrete beams, ACI Journal, 48(10), (1951), 145–156.
[26] F.K. Kong, P.J. Robins, D.F. Cole, Web reinforcement effects on deep beams, ACI Journal, 67(12), (1970), 1010–1018.
[27] K.N. Smith, A.S. Vantsiotis, Shear strength of deep beams, ACI Journal, 79(3), (1982), 201–213.
[28] N.S. Anderson, J.A. Ramirez, Detailling of stirrup reinforcement, ACI Structural Journal, 86(5), (1989), 507–515.
[29] K.H. Tan, F.K. Kong, S. Teng, L. Guan, High-strength concrete deep beams with effective span and shear span variations, ACI Journal, 92(4), (1995), 392-405.
[30] J.K. Oh, S.W. Shin, Shear strength of reinforced highstrength concrete deep beams, ACI Structural Journal, 98(2), (2001), 164–173.
[31] G. Aguilar, A.B. Matamoros, G.J. Parra-Montesinos, J.A. Ramirez, J.K Wight, Experimental evaluation of design procedures for shear strength of deep reinforced concrete beams , ACI Structural Journal, 99(4), (2002), 539–548.
[32] C.G. Quintero-Febres, G. Parra-Montesinos, J.K. Wight, Strength of struts in deep concrete members designed using strut and tie method, ACI Structural Journal, 103(4), (2006), 577–586.
[33] H. Li, Z. Lü, Z. Yue, Support vector machine for structural reliability analysis, Applied Mathematics and Mechanics, 27(10), (2006), 1295–1303.
[34] A. Widodo, B. Yang, Wavelet support vector machine for induction machine fault diagnosis based on transient current signal, Expert Systems with Applications, 35(1-2), (2008), 307–316, 2008.
[35] N.D. Hoang, D. Tien Bui, K.W. Liao, Groutability estimation of grouting processes with cement grouts using Differential Flower Pollination Optimized Support Vector Machine, Applied Soft Computing, 45, (2016), 173–186.
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