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

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی عمران، دانشگاه هرمزگان، بندرعباس، ایران.

2 گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه هرمزگان،

چکیده

برآورد دقیق مقاومت برشی در تیرهای بتن مسلح یک موضوع اساسی در طراحی مهندسی است. با این حال، پیش‌بینی مقاومت برشی در این نوع تیرها دارای دقت بالایی نمی‌باشد. از جمله راهکارهایی که در سال‌های اخیر جهت ارائه یک مدل مناسب برای پیش‌بینی مقاومت برشی تیرهای بتن مسلح پیشنهاد شده است، استفاده از الگوریتم‌های هوش مصنوعی (AI) می‌باشد. در این مطالعه قابلیت کاربرد الگوریتم‌های رگرسیون بردار پشتیبان (SVR) و استنتاج فازی عصبی (ANFIS) برای پیش‌بینی مقاومت برشی تیرهای بتن مسلح بررسی گردید و نتایج حاصله با الگوریتم ANN و آیین‌نامه‌های موجود مقایسه شد. . برای این منظور دهانه برش، طول موثر تیر، عمق موثر، عرض مقطع، مقاومت فشاری 28 روزه بتن، تنش تسلیم آرماتورهای طولی، تنش تسلیم آرماتورهای عرضی، درصد آرماتور طولی و درصد آرماتورهای برشی بعنوان پارامترهای ورودی و مقاومت برشی تیر بتن مسلح بعنوان پارامتر خروجی انتخاب گردید. نتایج مطالعه نشان داد که الگوریتم‌های ANFIS و SVR با خطای مربع میانگین ریشه (RMSE) برابر با 015/0 و 09/0 مقاومت برشی تیرهای بتن مسلح را با دقت بسیار زیادی پیش‌بینی می‌نمایند و از این جهت می‌توانند جایگزین مناسبی برای الگوریتم‌های زمانبر مانند ANN و روش‌های پرهزینه آزمایشگاهی باشند.

کلیدواژه‌ها

عنوان مقاله [English]

Prediction of shear strength of reinforced concrete beams using ANFIS and SVR Algorithms

نویسندگان [English]

  • Mohammad Reza Mohammadizadeh 1
  • Farnaz Esfandnia 2
1 Department of Civil Engineering, Faculty of Technical and Engineering, University of Hormozgan
2 Department of Civil Engineering, Faculty of Technical and Engineering, University of Hormozgan
چکیده [English]

The shear strength of reinforced concrete beams changes depending on the mechanical and geometrical parameters of the beam. Accurate estimation of shear strength in reinforced concrete beams is a fundamental issue in engineering design. However, the prediction of shear strength in these beams does not have very high accuracy. One of the strategies proposed in recent years to provide a suitable model for predicting shear strength of reinforced concrete beams is the use of artificial intelligence (AI) algorithms. In this investigation, the application of adaptive neural fuzzy inference system (ANFIS) and support vector regression (SVR) algorithms for predicting shear strength of reinforced concrete beams was studied and the results were compared with existing regulation. For this purpose, shear span, effective beam length, effective depth, cross-section width, 28-day compressive strength of concrete, yield stress of longitudinal reinforcements, yield stress of transverse reinforcements, percentage of longitudinal reinforcement and shear reinforcement percentage were selected as input parameters and shear strength of concrete beam as output. Using the k fold validation method, educational and test data were defined and based on these data, predictions were made. The results obtained from the prediction show that the mean square root error (RMSE) for ANFIS and SVR methods is 0.1514 and 0.0994, respectively. In general, it can be seen that both ANFIS and SVR algorithms predict the shear strength of reinforced concrete beams with great accuracy. Therefore, they can be a good alternative to time-consuming algorithms such as ANN and expensive laboratory methods.

کلیدواژه‌ها [English]

  • Shear Strength
  • Reinforced Concrete Beams
  • ANFIS
  • SVR

مراجع

[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.
 

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