پیش‌بینی ظرفیت برشی تیرهای بتن مسلح با استفاده از الگوریتم‌های رگرسیون بردار پشتیبان و سیستم استنتاج تطبیقی فازی-عصبی بهینه شده با الگوریتم‌های فرا ابتکاری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه هرمزگان، بندرعباس، ایران

2 استادیار

چکیده

با توجه به پیچیدگی مکانیزم­های برشی تیرهای بتن مسلح و تأثیرگذاری پارامترهای مختلف، ایجاد یک مدل کلی جهت تخمین دقیق ظرفیت برشی، دشوار می­باشد. همچنین اکثر دستورالعمل­های تعریف شده برای تعیین ظرفیت برشی تیرهای بتن مسلح در آیین‌نامه‌های طراحی به صورت تجربی بدست آمده­ است. در سال­های اخیر الگوریتم­های هوش مصنوعی در این زمینه بسیار مورد استفاده واقع شده است. در این مطالعه از الگوریتم‌های رگرسیون بردار پشتیبان و سیستم استنتاج تطبیقی فازی-عصبی بهینه شده با دو الگوریتم انبوه ذرات و الگوریتم ژنتیک برای پیش­بینی ظرفیت برشی تیرهای بتن مسلح استفاده شده است. در این الگوریتم‌ها، مقادیر 9 پارامتر تأثیر‌گذار در ظرفیت برشی به عنوان ورودی و ظرفیت برشی تیرهای بتن مسلح به عنوان پارامتر خروجی مورد استفاده قرار گرفته است. با استفاده از روش اعتبارسنجی Kfold، داده­های آموزشی و تستی تعریف شده و بر اساس این داده­ها پیش­بینی صورت گرفته است. نتایج بدست آمده از پیش­بینی نشان داد که مدل سیستم استنتاج فازی عصبی با الگوریتم بهینه‌سازی ژنتیک با ریشه دوم میانگین مربعات خطا برابر 0/06634 و ضریب همبستگی0/996 نسبت به سایر الگوریتم‌ها از دقت بالاتری برخوردار است. همچنین جهت تعیین حساسیت پارامتری متغیرهای مورد بررسی بر روی ظرفیت برشی تیرهای بتن مسلح از تئوری سیستم خاکستری (GST) استفاده شد. بررسی نتایج حاصل از این آنالیز نشان می‌دهد که میانگین ضریب آنالیز حساسیت پارامتر درصد آرماتورهای طولی ( ) نسبت به سایر پارامترها بزرگ‌تر است که نشان از تأثیر بیشتر پارامتر درصد آرماتورهای طولی بر روی ظرفیت برشی دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prediction of Shear Capacity of Reinforced Concrete Beams using Support Vector Regression and Adaptive Neuro-Fuzzy Inference Algorithms Optimized with Meta-Heuristic Algorithms

نویسنده [English]

  • Farnaz Esfandnia 1
1 Department of Civil Engineering, Faculty of Technical and Engineering, University of Hormozgan, Bandar Abbas, Iran
چکیده [English]

Considering the complexity of shear mechanisms of reinforced concrete beams and the effects of various parameters, creating a general model for the accurate estimation of the shear capacity is difficult. In addition, most guidelines for the determination of the shear capacity of reinforced concrete beams in empirical design codes have been obtained experimentally. Artificial intelligence algorithms have been widely used in this area in recent years. In this study, SVR, PANFIS, and GANFIS algorithms were used to predict the shear capacity of reinforced concrete beams. In this regard, the data of 175 experimental RC beam samples were collected. In these algorithms, values ​​of nine parameters affecting shear capacity were used as the input parameter and the shear capacity of the reinforced concrete beams as the output parameter. Using the Kfold validation method, training and test data were defined, and the predictions were performed accordingly. The results of predictions showed that the neuro-fuzzy inference system model with the genetic optimization algorithm had a higher accuracy than other algorithms with a second root mean square error of 0.06634 and a correlation coefficient of 0.996. Also, the grey system theory was used to determine the parametric sensitivity of the study variables on the shear capacity of reinforced concrete beams. The results showed that the mean coefficient of sensitivity analysis of the longitudinal rebar percentage parameter is greater than other parameters, indicating that the longitudinal rebar percentage parameter had more effects on shear capacity.
 

کلیدواژه‌ها [English]

  • Shear Capacity
  • Reinforced Concrete Beam
  • GST
  • PANFIS
  • GANFIS
  • SVR
[1] V.T.  Babar, P.K, Joshi, D.N. Shinde, Shear strength of steel fiber reinforced concrete beam without stirrups, International Journal of Advanced Engineering Technology, 5(2), (2015), 15-18.
[2] B.M. Adolfo, K.H. Wong, Design of simply supported deep beams using strut-and-tie models, ACI Structural Journal, 100(6), (2003), 704-712.
[3] I.M. Boyan, C.B.  Evan, P.C. Michael, Two-parameter kinematic theory for shear behavior of beep beams, ACI Structural Journal, 110(3), (2013), 447-456.
[4] F. Danglade, J.P. Pernot, P. Ve´ron, L. Fine, A priori evaluation of simulation models preparation processes using artificial intelligence techniques, Computers in Industry, 91, (2017), 45-61.
[5] H.G. Ni, J.Z. Wang, Prediction of compressive strength of concrete by neural networks, Cement and Concrete Research, 30(8), (2000), 1245-1250.
[6] M.Y. Mansour, M. Dicleli, J.Y. Lee, J. Zhang, Predicting the shear strength of reinforced concrete beams using artificial neural networks, Engineering Structures, 26(6), (2004), 781-799.
[7] A. Toghroli, M. Mohammadhassani, M. Suhatril, M. Shariati, Z. Ibrahim, Prediction of shear capacity of channel shear connectors using the ANFIS model, Steel and Composite Structures, 17(5), (2014), 623-639.
[8] Mansouri, M. Shariati, M. Safa, Z. Ibrahim, M.M. Tahir, D. Petković, Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique, Journal of Intelligent Manufacturing, 30(3), (2019), 1247-1257.
[9] M. Safa, M. Shariati, Z. Ibrahim, A. Toghroli, S.B. Baharom, N.M. Nor, D. Petkovic, Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength, Steel and Composite Structures, 21(3), (2016), 679-688.
[10] Y. Sedghi, Y. Zandi, A. Toghroli, M. Safa, E.T. Mohamad, M. Khorami, K. Wakil, Application of ANFIS technique on performance of C and L shaped angle shear connectors, Smart Structures and Systems, 22(3), (2018), 335-340.
[11] D.V. Dao, H.B. Ly, S.H. Trinh, T.T. Le, B.T. Pham, Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials, 12(6), (2019), 983-990.
[12] M.A Mashrei, M.M. Alaa, An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs, Journal of Applied Sciences, 9(4), (2019), 809-829.
[13] H. Naderpour, M. Mirrashid, Shear Strength Prediction of RC Beams Using      Adaptive Neuro-Fuzzy Inference System, Sharif University of technology, (2018).
[14] K.P.N Suguna, J.K. Raghunath, R.U. Maheswari, ANN based modeling for high strength concrete beams with surface mounted FRP laminates. International Jornal of Optimization in Civil Engineering, (2019).
[15] Z. Keshavarz, H. Torkian, Application of ANN and ANFIS models in determining compressive strength of concrete, Journal of Soft Computing in Civil Engineering, 2(1), (2018), 62-70.
[16] V.R.A Saathappan, P.N. Raghunath, K. Suguna, Adaptive neuro-fuzzy model for performance evaluation of  RC T-beams with externally bonded GFRP reinforcement, Journal of Reinforced Plastics and Composites, 30(24), (2011), 2015-2023.
[17] E.M. Golafshani, A. Rahai, M.H. Sebt, H. Akbarpour, Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic, Construction and Building Materials, 36, (2012),411-418.
[18] M.M. Alshihri, A.M. Azmy, M.S. El-Bisy, Neural networks for predicting compressive strength of structural light weight concrete, Construction and Building Materials, 23(6), (2009), 2214-2219.
[19] F. Ozcan, C.D. Atis, O. Karahan, E. Uncuoglu, H. Tanyildizi, Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete, Advances in Engineering Software, 40(9), (2009), 856-863
[20] L. Bal, F. Buyle-Bodin, Artificial neural network for predicting drying shrinkage of concrete, Construction and Building Materials, 38, (2013), 246-254.
[21] J. Sobhani, M. Najimi, A.R. Pourkhorshidi, T. Parhizkar, Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models, Construction and Building Materials, 24(5), (2010), 709-718.
[22] N.H. Guang, W.J. Zong, Prediction of compressive strength of concrete by neural network, Cement and Concrete Research, 30(8), (2000), 1245-1250.
[23] S. Wild, J. Bai, J.A. Ware, B.B. Sabir, Using neural networks to predict workability of concrete incorporating metakaolin and fly ash, Advances in Engineering Software, 34(11), (2003), 663-669.
[24] M. Khatibinia, M.R. Mohammadizadeh, Intelligent fuzzy inference system approach for modeling of debonding strength in FRP retrofitted masonry elements, Structural Engineering and Mechanics, 56(5), (2017) ,787-796.
[25] 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.
[26] Z. MundherYaseen, M.T. Tran, S. Kim, T. Bakhshpoori, R.C. Deo, Shear strength prediction of steel reinforced concrete beam using hybrid intelligence models: A new approach, Engineering Structures, 177, (2013), 244-255.
[27] B. Keshtegar, M. Bagheri, Z. Mundher Yaseen, Shear strength of steel fiber-unconfined reinforced concrete beam simulation: Application of novel intelligent model, Composite Structures, 212, (2019), 230-242.
[28] F. Khademi, S.M. Jamal, N. Deshpande, S.  Londhe, Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression, International Journal of Sustainable Built Environment, 5(2), (2016), 355-369.
[29] M. Pal, S. Deswal, Support vector regression based shear strength modelling of deep beams, Computers and Structures, 89(13), (2011), 1430-1439.
[30] J.S. Chou, N.T. Ngo, A.D. Pham, Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression, Journal of Computing in Civil Engineering, 30(1), (2015), 107-115.
[31] 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.
[32] L. Li, W. Zheng, Y. Wang, Prediction of Moment Redistribution in Statically Indeterminate Reinforced Concrete Structures Using Artificial Neural Network and Support Vector Regression”, Applied Sciences, 9(1), (2019), 28-51.
[33] A. Gholampour, I. Mansouri, O. Kisi, T. Ozbakkaloglu, Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models, Neural Computing and Applications, (2018).
[34] B.T. Pham, T.A. Hoang, D.M. Nguyen, D.T. Bui, Prediction of shear strength of soft soil using machine learning methods, Catena, 166, (2018), 181-191.
[35] S.F. Liu, Y.J. Yang, J. Forrest, Grey Data Analysis: Methods, Models and Applications, Springer Singapore, (2017).
[36] J.J. Xu, Z.P. Chen, T. Ozbakkaloglu, X.Y. Zhao, C.  Demartino, A critical assessment of the compressive behavior of reinforced recycled aggregate concrete columns”, Engineering Structures, 161, (2018), 161-175.
[37] S. Liu, H. Zhang, Y. Yang, Explanation of terms of grey incidence analysis models”, Grey Systems: Theory and Application, 7(1), (2017), 136-142.
[38] H. Yu, S. Kim, SVM tutorial: classification, regression, and ranking”, Handbook of Natural Computing, Springer Berlin Heidelberg, (2012), 479-506.
[39] H. Drucker, C.J. Burges, L. Kaufman, A.J. Smola, V. Vapnik, Support vector regression machines”, In Advances in Neural Information Processing Systems, 28(7), (1997), 779-784.
[40] J. Guan, J. Zurada, A. Levitan, An Adaptive Neuro fuzzy inference system based approach to real estate property assessment”, Journal of Real Estate Research, 30(4), (2008), 395-422.
[41] J. Kennedy, R.C. Eberhart, Y. Shiny, Swarm intelligence, Morgan Kaufmann Publishers, (2011).
[42] A.N. Hanoon, M.S. Jaafar, F. Hejazi, F.N.A. Abdul Aziz, Energy absorption evaluation of reinforced concrete beams under various loading rates based on particle swarm optimization technique, Engineering Optimization, 49(9), (2016), 1483-1501.
[43] J.H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press, (1992).
[44] H. Garg, A hybrid GSA-GA algorithm for constrained optimization problems, Information Sciences, 478, (2018), 499-523.
[45] T. Chai, R.R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE)–arguments against avoiding RMSE in the literature, Geoscientific model development,7(3), (2014), 1247-1250.
[46] American Concrete Institute (ACI), Committee 318-11: Building Code Requirements for Structural Concrete and Commentary, American Concrete Institute, (2011).
[47] Canadian Standards Association (CSA), Design of concrete structures: Structures (design), A national standard of Canada. CAN-A23.3-94, Clause11.1.2, Toronto, (1994).