Bulanık Ters Mantık Yöntemi ve Çelik Boru Profiller ile Teşkil Edilmiş Çelik Kafes Sistem Elemanlarının Tasarımında Kullanımı

Year 2022, Volume: 4 Issue: 3, 120 - 142, 31.12.2022

Ertekin Öztekin

https://doi.org/10.46740/alku.1134295

Abstract

Bu çalışmada, yapay zekâ yöntemlerinden biri olan bulanık mantık yöntemi kullanılarak, çelik boru profillerin çekme ve basınç kuvveti etkisindeki kapasitelerini belirleyebilmek için iki adet bulanık model oluşturulmuştur. 2018 Türk Çelik yapılar Yönetmeliğinde belirtilen GKT yöntemine göre oluşturulan her iki bulanık modelde de, çelik sınıfı S355 olarak sabit olarak tutulurken, kesit çapı (D), profil et kalınlığı (t) ve eleman uzunluğu (L) değişken parametreler olarak dikkate alınmıştır. Eksenel çekme kapasitesi (Tn) ve eksenel basınç kapasitesi (Pn) ayrı ayrı olarak bu modellerin çıktı parametrelerini oluşturmuşlardır. Her iki modelin oluşturulmasında aynı girdi değişkenleri değerlerine sahip ancak çıktı parametreleri farklı olan 1400 ‘er adet örnek çözüm kullanılmıştır. Kullanılan bu örnek çözümlerin haricinde 988 ‘şer adet farklı örnek çözüm ile bu modeller test edilerek, sırasıyla maksimum % 2.764 ve maksimum % 4.076 hata ile eksenel çekme ve basınç dayanımlarının tahminde kullanılabilecekleri ortaya konulmuştur. Daha sonra geliştirilen bulanık modellere, bulanık ters mantık yöntemi 3 farklı izostatik düzlem kafes sistem örneği için uygulanarak bu kafes sistemleri oluşturan çubuk elemanların tasarımları gerçekleştirildikten sonra dayanım kontrolleri karşılaştırmalı olarak 2018 Türk Çelik yapılar Yönetmeliğinde belirtilen GKT yöntemi ile gerçekleştirilmiştir. Sonuç olarak, bulanık mantık ve bulanık ters mantık yöntemlerinin birlikte aynı sayısal veriyi kullanarak boru kesitli çelik kafes sistem elemanların kapasitelerinin belirlenmesinde ve aynı zamanda tasarımlarının gerçekleştirilmesinde model hataları da dikkate alınarak güvenle kullanılabilecekleri ortaya konulmuştur.

Keywords

Bulanık Mantık, Bulanık Ters Mantık, Kafes Sistemler, Tasarım, Kapasite tahmini

Fuzzy Inverse Logic Method and Its Usage in the Design of Steel Truss System Elements Constructed with Steel Pipe Profiles

Abstract

In this study, by using the fuzzy logic method, one of the artificial intelligence methods, two fuzzy models were constituted to determine the capacities of steel pipe profiles under the effect of tensile and compressive forces. In both fuzzy models created according to the ASD method specified in the 2018 Turkish Steel Structures Regulation, while the steel grade was kept constant as S355, section diameter (D), profile wall thickness (t) and element length (L) were taken into account as variable parameters. Axial tensile capacity (Tn) and axial compressive capacity (Pn) separately constituted the output parameters of these models. In the constitution of both models, 1400 sample solutions with the same input variable values but different output parameters were used. Apart from these sample solutions used, these models were tested with 988 sample solutions, and it was revealed that the models could be used to estimate the axial tensile(Tn) and compressive strengths(Pn) with a maximum error of 2.764% and 4.076%. Then, the fuzzy inverse logic method was applied to the developed fuzzy models for 3 different isostatic plane truss systems, and after the design of the elements that make up these truss systems, the strength controls were carried out comparatively according to the ASD method. At the end of the study, it has been revealed that fuzzy logic and fuzzy inverse logic methods can be used safely both in determining the capacities of the truss system elements and in their designs by using the same numerical data and by taking into account model errors.

Keywords

Fuzzy Logic, Fuzzy Inverse Logic, Truss Systems, Design, Capacity estimation

References

• [1] Chandwani, V., Agrawal, V., & Nagar, R. (2013). Applications of soft computing in civil engineering: a review. International Journal of Computer Applications, 81(10).
• [2] Armaghani, D. J., Hatzigeorgiou, G. D., Karamani, C., Skentou, A., Zoumpoulaki, I., & Asteris, P. G. (2019). Soft computing-based techniques for concrete beams shear strength. Procedia Structural Integrity, 17, 924-933.
• [3] 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.
• [4] 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.
• [5] 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.
• [6] Mirrashid, M., & Naderpour, H. (2020). Recent trends in prediction of concrete elements behavior using soft computing (2010–2020). Archives of Computational Methods in Engineering, 1-21.
• [7] 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.
• [8] Ozkul, S., Ayoub, A., & Altunkaynak, A. (2014). Fuzzy-logic based inelastic displacement ratios of degrading RC structures. Engineering structures, 75, 590-603.
• [9] 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.
• [10] 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.
• [11] 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.
• [12] 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.
• [13] 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.
• [14] 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.
• [15] Cao, Y., Fan, Q., Azar, S. M., Alyousef, R., Yousif, S. T., Wakil, K., ... & Alaskar, A. (2020, October). Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer. In Structures (Vol. 27, pp. 118-127). Elsevier.
• [16] Allali, S. A., Abed, M., & Mebarki, A. (2018). Post-earthquake assessment of buildings damage using fuzzy logic. Engineering Structures, 166, 117-127.
• [17] 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. In Structures (Vol. 34, pp. 3750-3756). Elsevier.
• [18] Şen, Z. (2010). Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modeling. Expert systems with Applications, 37(8), 5653-5660.
• [19] Şen, Z. (2011). Supervised fuzzy logic modeling for building earthquake hazard assessment. Expert systems with applications, 38(12), 14564-14573.
• [20] Harirchian, E., & Lahmer, T. (2020, December). Developing a hierarchical type-2 fuzzy logic model to improve rapid evaluation of earthquake hazard safety of existing buildings. In Structures (Vol. 28, pp. 1384-1399). Elsevier.
• [21] 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.
• [22] 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.
• [23] 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 (pp. 335-344). Springer, Dordrecht.
• [24] 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.
• [25] Govardhan, P., Kalapatapu, P., & Pasupuleti, V. D. K. (2021, August). Identification of Multiple Cracks on Beam using Fuzzy Logic. In 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) (pp. 165-169). IEEE.
• [26] Khoshnoudian, F., & Molavi-Tabrizi, A. (2012). Responses of isolated building with MR Dampers and Fuzzy Logic. International Journal of Civil Engineering, 10(3).
• [27] 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.
• [28] 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.
• [29] 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.
• [30] Zadeh, L. A. (1965). Information and control. Fuzzy Sets, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
• [31] 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
• [32] 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
• [33] 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
• [34] 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
• [35] Öztekin, E. (2021). Fuzzy inverse logic: part-1. introduction and bases. Gümüşhane Üniversitesi Fen Bilimleri Dergisi , 11 (3) , 675-691 . DOI: 10.17714/gumusfenbil.894674
• [36] Öztekin, E. (2021). Fuzzy inverse logic: part-2. validation and evaluation of the method. Gümüşhane Üniversitesi Fen Bilimleri Dergisi , 11 (3) , 768-791 . DOI: 10.17714/gumusfenbil.894879
• [37] Dong, W. M., & Wong, F. S. (1987). Fuzzy weighted averages and implementation of the extension principle. Fuzzy sets and systems, 21(2), 183-199.
• [38] Çelik Yapıların Tasarım, Hesap ve Yapım Esaslarına Dair Yönetmelikte Değişiklik Yapılmasına Dair Yönetmelik, 2018. T.C. Çevre ve Şehircilik Bakanlığı, Ankara