BM ve BTM yöntemleri ile sınırlı sünekliğe sahip betonarme kirişlerde kesme dayanımı tahmini ve kesme tasarımı

Year 2023, Volume: 13 Issue: 1, 1 - 22, 15.01.2023

Ertekin Öztekin

https://doi.org/10.17714/gumusfenbil.1115693

Cited By: 1

Abstract

Bu çalışmada, sünekliği sınırlı betonarme kirişlerin kesme dayanımını tahmin etmek için geleneksel yapay zekâ yöntemlerinden biri olan Bulanık mantık (BM) yöntemi kullanılarak bir bulanık mantık modeli oluşturulmuştur. Bu modelde kiriş genişliği(bw), kiriş yüksekliği(h), karakteristik beton basınç dayanımı(fck), enine donatı çapı(T), enine donatının kesme kuvvetini taşıyan kol sayısı(n) ve enine donatı aralığı(s) değişken parametreler olarak dikkate alınmıştır. Farklı kesit özelliklerine sahip 2640 kirişin kesme kuvveti dayanımı çözümlerini içeren problem verileri kullanılarak geliştirilen model, bu verilerden farklı olarak 480 kiriş çözümü ile test edilmiştir. Geliştirilen bulanık mantık modelinin testlerinde maksimum yüzde hata, minimum yüzde hata, ortalama yüzde hata ve korelasyon katsayı değerleri 3.604, -0.091, 1.514 ve R2=0.999678 olarak elde edilmiştir. Bu çalışmanın yazarı tarafından yakın zamanda geliştirilen bulanık ters mantık (BTM) yöntemi, test sonuçlarından oldukça hassas bir şekilde geliştirildiği görülen bulanık mantık modeli üzerinde uygulanarak, kesme kuvveti etkisindeki 15 adet sınırlı sünekliğe sahip farklı betonarme kiriş için toplam 521 tasarım elde edilmiştir. Bu tasarımların doğruluğunu kontrol etmek için, bu tasarımlar için geleneksel hesaplamalarla kesme dayanımları elde edildikten sonra, elde edilen dayanım değerleri ile tasarım için dikkate alınan kesme kuvveti değerleri arasında % hata ve korelasyon katsayıları hesaplanmıştır. Elde edilen umut verici sonuçlar, bulanık ters mantık yönteminin kesme kuvveti etkisi altındaki betonarme kirişlerin tasarımında ve diğer bilimsel alanlardaki tasarım, optimizasyon ve kontrol çalışmalarında da kullanılabileceğini göstermiştir.

Keywords

Yapay Zeka, Bulanık Mantık, Bulanık Ters Mantık, Ters mantık, Betonarme Kiriş, Kesme tasarımı

Shear strength estimations and shear designs on RC beams with limited ductility by FL and FIL methods

Abstract

In this study, a fuzzy logic model was constituted by using the Fuzzy Logic (FL) method, which is one of the traditional artificial intelligence (AI) methods, in order to estimate the shear strength of reinforced concrete (RC) beams with limited ductility. In this model, beam width(bw), beam height(h), characteristic concrete compressive strength(fck), transverse reinforcement diameter(T), the number of arms bearing the shear force of the transverse reinforcement(n) and transverse reinforcement spacing(s) were taken into account as variable parameters. The model developed by using the problem data containing the solutions of shear force strength of 2640 beams with different cross-section properties were tested with 480 beam solutions different from these data. In the tests of the developed FL model, maximum percentage error, minimum percentage error, average percentage error and correlation coefficient values were obtained as 3.604, -0.091, 1.514 and R2=0.999678. By applying the fuzzy inverse logic method (FIL), which was recently developed by the author of this study, on the FL model, which is seen to have been developed quite sensitively from the test results, a total of 521 designs were obtained for 15 different RC beams with limited ductility subjected to shear. In order to check the accuracy of these designs, after shear strengths were obtained by conventional computations for these designs, % error and correlation coefficients were computed between the obtained strength values and the shear force values taken into account for the design. The promising results show that the FIL method can be used in the design of RC beams under shear force and even in other scientific studies such as design, optimization and control.

Keywords

Artificial intelligence, Fuzzy logic, Fuzzy inverse logic, Inverse logic, Reinforced concrete beam, Shear design

References

• Allali, S. A., Abed, M., & Mebarki, A. (2018). Post-earthquake assessment of buildings damage using fuzzy logic, Engineering Structures, 166, 117-127. https://doi.org/10.1016/j.engstruct.2018.03.055
• Akintunde, O. P. (2021). Fuzzy Logic design approach for a singly reinforced concrete beam, Journal of Civil Engineering Research & Technology. SRC/JCERT-111, 3. https://doi.org/10.47363/JCERT/2021(3)111.
• Amani, J., & Moeini, R. (2012). Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network, Scientia Iranica, 19(2), 242-248. https://doi.org/10.1016/j.scient.2012.02.009
• Cao, Y., Zandi, Y., Rahimi, A., Petković, D., Denić, N., Stojanović, J., ... & Assilzadeh, H. (2021, December). Evaluation and monitoring of impact resistance of fiber reinforced concrete by adaptive neuro fuzzy algorithm, Structures, 34. 3750-3756. https://doi.org/10.1016/j.istruc.2021.09.072
• Cao, Y., Fan, Q., Azar, S. M., Alyousef, R., Yousif, S. T., Wakil, K., ... & Alaskar, A. (2020). Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer, Structures, 27, 118-127. https://doi.org/10.1016/j.istruc.2020.05.031
• Chao, C. J., & Cheng, F. P. (1998). Fuzzy pattern recognition model for diagnosing cracks in RC structures. Journal of computing in civil engineering, 12(2), 111-119.
• Choi, S. K., Tareen, N., Kim, J., Park, S., & Park, I. (2018). Real-time strength monitoring for concrete structures using EMI technique incorporating with fuzzy logic, Applied Sciences, 8(1), 75. https://doi.org/10.3390/app8010075
• Cukaric, A., Camagic, I., Dutina, V., Milkic, Z., & Jovic, S. (2019). Parameters ranking based on influence on dynamical strength of ultra-high performance concrete by neuro fuzzy logic, Struct Concr, 433, 1-7. https://doi.org/10.1002/suco.201900433
• De Iuliis, M., Kammouh, O., Cimellaro, G. P., & Tesfamariam, S. (2019). Downtime estimation of building structures using fuzzy logic, International journal of disaster risk reduction, 34, 196-208. https://doi.org/10.1016/j.ijdrr.2018.11.017
• Doğangün, Adem, Betonarme yapıların hesap ve tasarımı(Turkish), Birsen yayınevi 17th edition, 2021, İstanbul/Turkey.
• Doran, B., Yetilmezsoy, K., & Murtazaoglu, S. (2015). Application of fuzzy logic approach in predicting the lateral confinement coefficient for RC columns wrapped with CFRP, Engineering Structures, 88, 74-91. https://doi.org/10.1016/j.engstruct.2015.01.039
• Elbeltagi, E., Hosny, O. A., Elhakeem, A., Abd-Elrazek, M. E., & Abdullah, A. (2011). Selection of slab formwork system using fuzzy logic, Construction Management and Economics, 29(7), 659-670. https://doi.org/10.1080/01446193.2011.590144
• Elenas, A., Vrochidou, E., Alvanitopoulos, P., & Andreadis, I. (2013). Classification of seismic damages in buildings using fuzzy logic procedures, In Computational Methods in Stochastic Dynamics. 335-344. https://doi.org/10.1007/978-94-007-5134-7_20
• Garzón-Roca, J., Marco, C. O., & Adam, J. M. (2013). Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic, Engineering Structures, 48, 21-27. https://doi.org/10.1016/j.engstruct.2012.09.029
• Golafshani, E. M., Rahai, A., Sebt, M. H., & Akbarpour, H. (2012). Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic, Construction and building materials, 36, 411-418. https://doi.org/10.1016/j.conbuildmat.2012.04.046
• Govardhan, P., Kalapatapu, P., & Pasupuleti, V. D. K. (2021). Identification of Multiple Cracks on Beam using Fuzzy Logic, 2021 International Conference on Emerging Techniques in Computational Intelligence, 165-169. https://doi.org/10.1109/ICETCI51973.2021.9574059
• Harirchian, E., & Lahmer, T. (2020). Developing a hierarchical type-2 fuzzy logic model to improve rapid evaluation of earthquake hazard safety of existing buildings, Structures 28, 1384-1399. https://doi.org/10.1016/j.istruc.2020.09.048
• Khoshnoudian, F., & Molavi-Tabrizi, A. (2012). Responses of isolated building with MR Dampers and Fuzzy Logic, International Journal of Civil Engineering, 10(3).
• Mamdani, E. H. and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13. https://doi.org/10.1016/S0020-7373(75)80002-2
• Mamdani, E. H. (1976). Advances in the linguistic synthesis of fuzzy controllers, International Journal of Man-Machine Studies, 8(6), 669-678. https://doi.org/10.1016/S0020-7373(76)80028-4.
• Mirrashid, M., & Naderpour, H. (2020). Recent trends in prediction of concrete elements behavior using soft computing (2010–2020), Archives of Computational Methods in Engineering, 4,1-21. https://doi.org/10.1007/s11831-020-09500-7
• Naderpour, H., & Alavi, S. A. (2017). A proposed model to estimate shear contribution of FRP in strengthened RC beams in terms of Adaptive Neuro-Fuzzy Inference System, Composite Structures, 170, 215-227. https://doi.org/10.1016/j.compstruct.2017.03.028
• Naderpour, H., Nagai, K., Haji, M., & Mirrashid, M. (2019). Adaptive neuro‐fuzzy inference modelling and sensitivity analysis for capacity estimation of fiber reinforced polymer‐strengthened circular reinforced concrete columns, Expert Systems, 36(4), e12410. https://doi.org/10.1111/exsy.12410
• Ozkul, S., Ayoub, A., & Altunkaynak, A. (2014). Fuzzy-logic based inelastic displacement ratios of degrading RC structures, Engineering structures, 75, 590-603. https://doi.org/10.1016/j.engstruct.2014.06.030
• Öztekin, E. (2021). Fuzzy inverse logic: part-1. Introduction and bases. Gümüşhane Üniversitesi Fen Bilimleri Dergisi , 11 (3) , 675-691 . https://doi.org/10.17714/gumusfenbil.894674
• Öztekin, E. (2021). Fuzzy inverse logic: part-2. Validation and evaluation of the method. Gümüşhane Üniversitesi Fen Bilimleri Dergisi , 11 (3) , 768-791 . https://doi.org/10.17714/gumusfenbil.894879
• Sung, Y. C., & Su, C. K. (2010). Fuzzy genetic optimization on performance-based seismic design of reinforced concrete bridge piers with single-column type, Optimization and Engineering, 11(3), 471-496. https://doi.org/10.1007/s11081-009-9092-4
• Şen, Z. (2010). Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modeling, Expert systems with Applications, 37(8), 5653-5660. https://doi.org/10.1016/j.eswa.2010.02.046
• Şen, Z. (2011). Supervised fuzzy logic modeling for building earthquake hazard assessment, Expert systems with applications, 38(12), 14564-14573. https://doi.org/10.1016/j.eswa.2011.05.026
• TBEC. Turkish Building Earthquake Code; T.C. Resmi Gazete: Ankara, Turkey, 2018.
• Tekeli, H., Korkmaz, K. A., Demir, F., & Carhoglu, A. I. (2014). Comparison of critical column buckling load in regression, fuzzy logic and ANN based estimations, Journal of Intelligent & Fuzzy Systems, 26(3), 1077-1087. https://doi.org/10.3233/IFS-120701
• TS 500, Requirements for design and construction of reinforced concrete structures, Turkish Standarts, Institute: Ankara, Türkiye, 2000.
• Ud Darain, K. M., Jumaat, M. Z., Hossain, M. A., Hosen, M. A., Obaydullah, M., Huda, M. N., & Hossain, I. (2015). Automated serviceability prediction of NSM strengthened structure using a fuzzy logic expert system, Expert systems with applications, 42(1), 376-389. https://doi.org/10.1016/j.eswa.2014.07.058
• Uzunoğlu, M., & Kap, T. (2012). Prediction of concrete compressive strength in buildings that would be reinforced by fuzzy logic, International Journal of Physical Sciences, 7(31), 5193-5201. https://doi.org/10.5897/IJPS12.155
• Zabihi-Samani, M., & Ghanooni-Bagha, M. (2019). Optimal semi-active structural control with a wavelet-based cuckoo-search fuzzy logic controller, Iranian Journal of Science and Technology, Transactions of Civil Engineering, 43(4), 619-634. https://doi.org/10.1007/s40996-018-0206-0
• Zadeh, L. A. (1965), Fuzzy Sets, Information and control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
• Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on systems, Man and Cybernetics, (1), 28-44. https://doi.org/10.1109/TSMC.1973.5408575
• Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-III, Information Siences, 9(1), 43-80. https://doi.org/10.1016/0020-0255(75)90036-5