Research Article
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Year 2024, Volume: 42 Issue: 2, 459 - 474, 30.04.2024

Ertekin Öztekin

Abstract

References

  • [1] Chandwani V, Agrawal V, Nagar R. Applications of soft computing in civil engineering: a review. Int J Comput Appl 2013;81. [CrossRef]
  • [2] Armaghani DJ, Hatzigeorgiou GD, Karamani C, Skentou A, Zoumpoulaki I, Asteris PG. Soft computing-based techniques for concrete beams shear strength. Proced Struct Integr 2019;17:924933. [CrossRef]
  • [3] Naderpour H, Nagai K, Haji M, Mirrashid M. Adaptive neuro‐fuzzy inference modelling and sensitivity analysis for capacity estimation of fiber reinforced polymer‐strengthened circular reinforced concrete columns. Expert Syst 2019;36. [CrossRef]
  • [4] Uzunoğlu M, Kap T. Prediction of concrete compressive strength in buildings that would be reinforced by fuzzy logic. Int J Phys Sci 2012;7:51935201. [CrossRef]
  • [5] Tekeli H, Korkmaz KA, Demir F, Carhoglu AI. Comparison of critical column buckling load in regression, fuzzy logic and ANN based estimations. J Intell Fuzzy Syst 2014;26:10771087. [CrossRef]
  • [6] Mirrashid M, Naderpour H. Recent trends in prediction of concrete elements behavior using soft computing (2010–2020). Arch Comput Methods Eng 2020;121. [CrossRef]
  • [7] Garzón-Roca J, Marco CO, Adam JM. Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic. Eng Struct 2013;48:2127.
  • [8] Ozkul S, Ayoub A, Altunkaynak A. Fuzzy-logic based inelastic displacement ratios of degrading RC structures. Eng Struct 2014;75:590603. [CrossRef]
  • [9] Doran B, Yetilmezsoy K, Murtazaoglu S. Application of fuzzy logic approach in predicting the lateral confinement coefficient for RC columns wrapped with CFRP. Eng Struct 2015;88:7491. [CrossRef]
  • [10] Naderpour H, Alavi SA. A proposed model to estimate shear contribution of FRP in strengthened RC beams in terms of Adaptive Neuro-Fuzzy Inference System. Composite Struct 2017;170:215227. [CrossRef]
  • [11] Golafshani EM, Rahai A, Sebt MH, Akbarpour H. Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic. Construct Build Mater 2012;36:411418. [CrossRef]
  • [12] ud Darain KM, Jumaat MZ, Hossain MA, Hosen MA, Obaydullah M, Huda MN, Hossain I. Automated serviceability prediction of NSM strengthened structure using a fuzzy logic expert system. Exp Syst Appl 2015;42:376389. [CrossRef]
  • [13] Amani J, Moeini R. Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network. Sci Iran 2012;19:242248. [CrossRef]
  • [14] De Iuliis M, Kammouh O, Cimellaro GP, Tesfamariam S. Downtime estimation of building structures using fuzzy logic. Int J Disaster Risk Reduct 2019;34:196208. [CrossRef]
  • [15] Cao Y, Fan Q, Azar SM, Alyousef R, Yousif ST, Wakil K, Alaskar A. Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer. Structures 2020;27:118127. [CrossRef]
  • [16] Allali SA, Abed M, Mebarki A. Post-earthquake assessment of buildings damage using fuzzy logic. Eng Struct 2018;166:117127. [CrossRef]
  • [17] Cao Y, Zandi Y, Rahimi A, Petković D, Denić N, Stojanović J, Assilzadeh H. Evaluation and monitoring of impact resistance of fiber reinforced concrete by adaptive neuro fuzzy algorithm. Structures 2021;34:37503756. [CrossRef]
  • [18] Şen Z. Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modeling. Exp Syst Appl 2010;37:56535660. [CrossRef]
  • [19] Şen Z. Supervised fuzzy logic modeling for building earthquake hazard assessment. Exp Syst Appl 2011;38:1456414573. [CrossRef]
  • [20] Harirchian E, Lahmer T. Developing a hierarchical type-2 fuzzy logic model to improve rapid evaluation of earthquake hazard safety of existing buildings. Structures 2020;28:13841399. [CrossRef]
  • [21] Choi SK, Tareen N, Kim J, Park S, Park I. Real-time strength monitoring for concrete structures using EMI technique incorporating with fuzzy logic. Appl Sci 2018;8:75. [CrossRef]
  • [22] Chao CJ, Cheng FP. Fuzzy pattern recognition model for diagnosing cracks in RC structures. J Comput Civil Eng 1998;12:111119. [CrossRef]
  • [23] Elenas A, Vrochidou E, Alvanitopoulos P, Andreadis I. Classification of seismic damages in buildings using fuzzy logic procedures. Comput Methods Stoch Dyn 2013:335344. [CrossRef]
  • [24] Cukaric A, Camagic I, Dutina V, Milkic Z, Jovic S. Parameters ranking based on influence on dynamical strength of ultra-high performance concrete by neuro fuzzy logic. Struct Concr 2019;433:17.
  • [25] Govardhan P, Kalapatapu P, Pasupuleti VDK. Identification of Multiple Cracks on Beam using Fuzzy Logic. In 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI). 2021 Aug;165-169. IEEE. [CrossRef]
  • [26] Khoshnoudian F, Molavi-Tabrizi A. Responses of isolated building with MR Dampers and Fuzzy Logic. Int J Civil Eng 2012;10.
  • [27] Zabihi-Samani M, Ghanooni-Bagha M. Optimal semi-active structural control with a wavelet-based cuckoo-search fuzzy logic controller. Iran J Sci Technol Trans Civil Eng 2019;43:619634. [CrossRef]
  • [28] Elbeltagi E, Hosny OA, Elhakeem A, Abd-Elrazek ME, Abdullah A. Selection of slab formwork system using fuzzy logic. Constr Manag Econ 2011;29:659670. [CrossRef]
  • [29] Sung YC, Su CK. Fuzzy genetic optimization on performance-based seismic design of reinforced concrete bridge piers with single-column type. Optim Eng 2010;11:471496. [CrossRef]
  • [30] Akintunde OP. Fuzzy logic design approach for a singly reinforced concrete beam. J Civil Eng Res Technol 2021;111:3. [CrossRef]
  • [31] Öztekin E. Fuzzy inverse logic: part-1. introduction and bases. Gümüşhane Üniv Fen Bilim Derg 2021;11:675691. [CrossRef]
  • [32] Öztekin E. Fuzzy inverse logic: part-2. validation and evaluation of the method. Gümüşhane Üniv Fen Bilim Derg 2021;11:768791. [CrossRef]
  • [33] Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 1975;7:113. [CrossRef]
  • [34] Mamdani EH. Advances in the linguistic synthesis of fuzzy controllers. Int J Man-Mach Stud 1976;8:669678. [CrossRef]
  • [35] Zadeh LA. Information and control. Fuzzy Sets 1965;8:338353. [CrossRef]
  • [36] Zadeh LA. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 1973:2844. [CrossRef]
  • [37] Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning-III. Inf Sci 1975;9:4380. [CrossRef]
  • [38] Dong WM, Wong FS. Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets Syst 1987;21:183199. [CrossRef]
  • [39] EN 1992-1-2. Eurocode 2: Design of Concrete Structures - Part 1-2. 1st ed. Brussels: BSi; 2004.
  • [40] TS 500-2000. Requirements for design and construction of reinforced concrete structures. Ankara, Türkiye: Turkish Standards Institute; 2000.
  • [41] TBEC. Turkish Building Earthquake Code. Ankara, Turkey: T.C. Resmi Gazete; 2018.
  • [42] Visual Studio: Yazılım Geliştiricileri ve Ekipleri için IDE ve Kod Düzenleyicisi [Internet]. Microsoft. Available at: https://visualstudio.microsoft.com/tr/ Accessed on Apr 28, 2024.

1D fuzzy inverse logic method and its use in the design of thick reinforced concrete columns

Year 2024, Volume: 42 Issue: 2, 459 - 474, 30.04.2024

Ertekin Öztekin

Abstract

In this study, without using any uniaxial force and bending moment (N-M) interaction diagrams, designs were carried out on thick columns subjected to uniaxial bending and compression by a novel 1 dimensional (1D) Fuzzy Inverse Logic (FIL) method. For this purpose, firstly, a Fuzzy Logic (FL) model was developed and the FIL method was applied to it there-after. While, the cross-section width (b), the cross-section height (h), the rebar diameter(f), the numbers of reinforcement rows (Rx and Ry) placed into the cross-section in X and Y directions, the characteristic concrete compressive strength(fck) and the axial force ratio Nr=N/(b.h.(fck/1.5)) were taken as variable parameters, concrete cover thickness (c), rebar strength (fyd) and k1 parameter defined for the concrete pressure block were kept constant in the developed FL model. After designs were performed on 15 columns having different variable variations by the 1D FIL method, moment bearing capacities of the obtained 9737 alternative designs determined conventionally were compared with the desired moment values. The evaluations made on the comparisons show that the FIL method is not only a very effective artificial intelligence method for the design of reinforced concrete thick columns but also a promising method for many other problems such as control, optimization, design, etc.

Keywords

Column; Design Fuzzy Logic Fuzzy Inverse Logic Inverse Logic Reinforced Concrete Reverse Logic

References

  • [1] Chandwani V, Agrawal V, Nagar R. Applications of soft computing in civil engineering: a review. Int J Comput Appl 2013;81. [CrossRef]
  • [2] Armaghani DJ, Hatzigeorgiou GD, Karamani C, Skentou A, Zoumpoulaki I, Asteris PG. Soft computing-based techniques for concrete beams shear strength. Proced Struct Integr 2019;17:924933. [CrossRef]
  • [3] Naderpour H, Nagai K, Haji M, Mirrashid M. Adaptive neuro‐fuzzy inference modelling and sensitivity analysis for capacity estimation of fiber reinforced polymer‐strengthened circular reinforced concrete columns. Expert Syst 2019;36. [CrossRef]
  • [4] Uzunoğlu M, Kap T. Prediction of concrete compressive strength in buildings that would be reinforced by fuzzy logic. Int J Phys Sci 2012;7:51935201. [CrossRef]
  • [5] Tekeli H, Korkmaz KA, Demir F, Carhoglu AI. Comparison of critical column buckling load in regression, fuzzy logic and ANN based estimations. J Intell Fuzzy Syst 2014;26:10771087. [CrossRef]
  • [6] Mirrashid M, Naderpour H. Recent trends in prediction of concrete elements behavior using soft computing (2010–2020). Arch Comput Methods Eng 2020;121. [CrossRef]
  • [7] Garzón-Roca J, Marco CO, Adam JM. Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic. Eng Struct 2013;48:2127.
  • [8] Ozkul S, Ayoub A, Altunkaynak A. Fuzzy-logic based inelastic displacement ratios of degrading RC structures. Eng Struct 2014;75:590603. [CrossRef]
  • [9] Doran B, Yetilmezsoy K, Murtazaoglu S. Application of fuzzy logic approach in predicting the lateral confinement coefficient for RC columns wrapped with CFRP. Eng Struct 2015;88:7491. [CrossRef]
  • [10] Naderpour H, Alavi SA. A proposed model to estimate shear contribution of FRP in strengthened RC beams in terms of Adaptive Neuro-Fuzzy Inference System. Composite Struct 2017;170:215227. [CrossRef]
  • [11] Golafshani EM, Rahai A, Sebt MH, Akbarpour H. Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic. Construct Build Mater 2012;36:411418. [CrossRef]
  • [12] ud Darain KM, Jumaat MZ, Hossain MA, Hosen MA, Obaydullah M, Huda MN, Hossain I. Automated serviceability prediction of NSM strengthened structure using a fuzzy logic expert system. Exp Syst Appl 2015;42:376389. [CrossRef]
  • [13] Amani J, Moeini R. Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network. Sci Iran 2012;19:242248. [CrossRef]
  • [14] De Iuliis M, Kammouh O, Cimellaro GP, Tesfamariam S. Downtime estimation of building structures using fuzzy logic. Int J Disaster Risk Reduct 2019;34:196208. [CrossRef]
  • [15] Cao Y, Fan Q, Azar SM, Alyousef R, Yousif ST, Wakil K, Alaskar A. Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer. Structures 2020;27:118127. [CrossRef]
  • [16] Allali SA, Abed M, Mebarki A. Post-earthquake assessment of buildings damage using fuzzy logic. Eng Struct 2018;166:117127. [CrossRef]
  • [17] Cao Y, Zandi Y, Rahimi A, Petković D, Denić N, Stojanović J, Assilzadeh H. Evaluation and monitoring of impact resistance of fiber reinforced concrete by adaptive neuro fuzzy algorithm. Structures 2021;34:37503756. [CrossRef]
  • [18] Şen Z. Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modeling. Exp Syst Appl 2010;37:56535660. [CrossRef]
  • [19] Şen Z. Supervised fuzzy logic modeling for building earthquake hazard assessment. Exp Syst Appl 2011;38:1456414573. [CrossRef]
  • [20] Harirchian E, Lahmer T. Developing a hierarchical type-2 fuzzy logic model to improve rapid evaluation of earthquake hazard safety of existing buildings. Structures 2020;28:13841399. [CrossRef]
  • [21] Choi SK, Tareen N, Kim J, Park S, Park I. Real-time strength monitoring for concrete structures using EMI technique incorporating with fuzzy logic. Appl Sci 2018;8:75. [CrossRef]
  • [22] Chao CJ, Cheng FP. Fuzzy pattern recognition model for diagnosing cracks in RC structures. J Comput Civil Eng 1998;12:111119. [CrossRef]
  • [23] Elenas A, Vrochidou E, Alvanitopoulos P, Andreadis I. Classification of seismic damages in buildings using fuzzy logic procedures. Comput Methods Stoch Dyn 2013:335344. [CrossRef]
  • [24] Cukaric A, Camagic I, Dutina V, Milkic Z, Jovic S. Parameters ranking based on influence on dynamical strength of ultra-high performance concrete by neuro fuzzy logic. Struct Concr 2019;433:17.
  • [25] Govardhan P, Kalapatapu P, Pasupuleti VDK. Identification of Multiple Cracks on Beam using Fuzzy Logic. In 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI). 2021 Aug;165-169. IEEE. [CrossRef]
  • [26] Khoshnoudian F, Molavi-Tabrizi A. Responses of isolated building with MR Dampers and Fuzzy Logic. Int J Civil Eng 2012;10.
  • [27] Zabihi-Samani M, Ghanooni-Bagha M. Optimal semi-active structural control with a wavelet-based cuckoo-search fuzzy logic controller. Iran J Sci Technol Trans Civil Eng 2019;43:619634. [CrossRef]
  • [28] Elbeltagi E, Hosny OA, Elhakeem A, Abd-Elrazek ME, Abdullah A. Selection of slab formwork system using fuzzy logic. Constr Manag Econ 2011;29:659670. [CrossRef]
  • [29] Sung YC, Su CK. Fuzzy genetic optimization on performance-based seismic design of reinforced concrete bridge piers with single-column type. Optim Eng 2010;11:471496. [CrossRef]
  • [30] Akintunde OP. Fuzzy logic design approach for a singly reinforced concrete beam. J Civil Eng Res Technol 2021;111:3. [CrossRef]
  • [31] Öztekin E. Fuzzy inverse logic: part-1. introduction and bases. Gümüşhane Üniv Fen Bilim Derg 2021;11:675691. [CrossRef]
  • [32] Öztekin E. Fuzzy inverse logic: part-2. validation and evaluation of the method. Gümüşhane Üniv Fen Bilim Derg 2021;11:768791. [CrossRef]
  • [33] Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 1975;7:113. [CrossRef]
  • [34] Mamdani EH. Advances in the linguistic synthesis of fuzzy controllers. Int J Man-Mach Stud 1976;8:669678. [CrossRef]
  • [35] Zadeh LA. Information and control. Fuzzy Sets 1965;8:338353. [CrossRef]
  • [36] Zadeh LA. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 1973:2844. [CrossRef]
  • [37] Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning-III. Inf Sci 1975;9:4380. [CrossRef]
  • [38] Dong WM, Wong FS. Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets Syst 1987;21:183199. [CrossRef]
  • [39] EN 1992-1-2. Eurocode 2: Design of Concrete Structures - Part 1-2. 1st ed. Brussels: BSi; 2004.
  • [40] TS 500-2000. Requirements for design and construction of reinforced concrete structures. Ankara, Türkiye: Turkish Standards Institute; 2000.
  • [41] TBEC. Turkish Building Earthquake Code. Ankara, Turkey: T.C. Resmi Gazete; 2018.
  • [42] Visual Studio: Yazılım Geliştiricileri ve Ekipleri için IDE ve Kod Düzenleyicisi [Internet]. Microsoft. Available at: https://visualstudio.microsoft.com/tr/ Accessed on Apr 28, 2024.
There are 42 citations in total.

Details

Primary Language English
Subjects Civil Construction Engineering
Journal Section Research Articles
Authors

Ertekin Öztekin

Publication Date April 30, 2024
Submission Date July 2, 2022
Published in Issue Year 2024 Volume: 42 Issue: 2

Cite

Vancouver Öztekin E. 1D fuzzy inverse logic method and its use in the design of thick reinforced concrete columns. SIGMA. 2024;42(2):459-74.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/