پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شمارهها | 435 |
| تعداد مقالات | 5,634 |
| تعداد مشاهده مقاله | 7,427,927 |
| تعداد دریافت فایل اصل مقاله | 6,215,980 |
پیش بینی مقاومت برشی تیرهای عمیق بتن مسلح با استفاده از روش ماشین بردار پشتیبان حداقل مربعات وزن دار | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 14، دوره 53، شماره 9، آذر 1400، صفحه 3867-3882 اصل مقاله (2.13 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2020.18122.6775 | ||
| نویسندگان | ||
| محمدرضا محمدی زاده* 1؛ فرناز اسفندنیا2 | ||
| 1استادیار | ||
| 2گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه هرمزگان، بندرعباس، ایران | ||
| چکیده | ||
| مقاومت برشی تیرهای عمیق بتن مسلح (RC) وابسته به پارامترهای مکانیکی و هندسی تیر تغییر مینماید. برآورد دقیق مقاومت برشی در تیرهای عمیق بتن مسلح یکی از اصلیترین موضوعات در طراحی سازههای مهندسی است. با این حال، پیشبینی مقاومت برشی در این نوع تیرها از دقت بالایی برخوردار نیست. یکی از روشهای تخمین نسبتا دقیق مقاومت برشی استفاده از هوش مصنوعی میباشد. هوش مصنوعی دارای روشهای مختلفی است که یکی از این روشها استفاده از تکنیک هوش مصنوعی (AI) مبتنی بر روش ماشین بردار پشتیبان است. در این مطالعه برای پیشبینی ظرفیت برشی تیرهای عمیق بتن مسلح از روش ماشین بردار پشتیبان حداقل مربعات وزندار (WLS-SVM) که روشی نسبتا جدید و کارامد است، استفاده شده است. برای این منظور ابتدا یک بانک اطلاعاتی شامل نتایج آزمایشگاهی مربوط به تیرهای عمیق بتن مسلح جمعآوری شد. سپس پس از تعیین پارامترهای ورودی و خروجی با کمک فرآیند آموزشی در روش WLS-SVM و با استفاده از بخشی از دادههای جمعآوری شده، مدلی برای پیشبینی مقاومت برشی تیرهای عمیق بتن مسلح ایجاد شد. به منظور تعیین دقت روش WLS-SVM، نتایج به دست آمده با نتایج حاصل از سایر روشهای هوش مصنوعی و آییننامههای مختلف مورد ارزیابی و مقایسه قرار گرفت. بررسیهای آماری نشان داد که روش WLS-SVM دارای بهترین عملکرد از نظر پارامترهای ارزیابی آماری (0/9887 = R2 و 107/0=RMSE و 0/478 =MAE و 9/48%=MAPE ) نسبت به سایر روشها هستند. بنابراین این مطالعه نشان میدهد که میتوان از روش WLS-SVM به عنوان ابزاری کارآمد در طراحی تیرهای عمیق استفاده نمود. | ||
| کلیدواژهها | ||
| تیر عمیق بتن مسلح؛ مقاومت برشی؛ هوش مصنوعی؛ ماشین بردار پشتیبان حداقل مربعات وزندار | ||
| موضوعات | ||
| شبکه های عصبی | ||
| عنوان مقاله [English] | ||
| Prediction of shear strength of deep beams of the reinforced concrete using weighted least squares support vector machine method | ||
| نویسندگان [English] | ||
| Mohammad Reza Mohammadizadeh1؛ Farnaz Esfandnia2 | ||
| 1Department of Civil Engineering, Faculty of Technical and Engineering, University of Hormozgan | ||
| 2Department of Civil Engineering, Faculty of Technical and Engineering, University of Hormozgan, Bandar Abbas, Iran | ||
| چکیده [English] | ||
| The shear strength of deep reinforced concrete beams depends on the mechanical and geometrical properties of the beam. Accurate estimation of shear strength in deep reinforced concrete beams is one of the major issues in the design of engineering structures. However, some methods proposed to determine the shear strength in deep reinforced concrete beams do not have high accuracy. One method to accurately estimate shear strength is to use artificial intelligence (AI). Artificial intelligence has many different methods, one of which is the use of artificial intelligence-based on the support vector machine method. In this study, the weighted least squares support vector machine (WLS-SVM), which is a relatively new and efficient method for predicting the shear capacity of reinforced concrete beams, has been used. In this study, a database containing experimental results on deep reinforced concrete beams was first collected. Then, after determining the input and output parameters using a training process in WLS-SVM method and using a part of the collected data, a model was developed to predict the shear strength of deep reinforced concrete beams. In order to determine the accuracy of the WLS-SVM method, the results were compared with those obtained by other AI methods and different regulations. Statistical analysis showed that WLS-SVM has the best performance in terms of statistical evaluation parameters (R2 = 0.9887, RMSE = 0.107, MAE = 0.478 and MAPE = 9.48%) compared to the other method. | ||
| کلیدواژهها [English] | ||
| Deep beam of reinforced concrete, Shear strength, Artificial intelligence, Weighted least squares support vector machine | ||
| مراجع | ||
|
[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. | ||
|
آمار تعداد مشاهده مقاله: 810 تعداد دریافت فایل اصل مقاله: 1,091 |
||