A Fuzzy Inference System in Constructional Engineering Projects to Evaluate the Design Codes for RC Buildings

Reza Kamgar, Sayyed Morteza Hatefi, Noorollah Majidi


Economical design of a building is one of the main aims that should be followed because of its importance in constructional projects. In order to have an economical design, longitudinal reinforcing bars in the reinforced concrete members are among those parts of the structure that can be designed economically. The application of fuzzy inference systems provides an effective tools to handle the uncertainties and subjectivities arising in the designing process of buildings. Therefore, the main purpose of this paper is to propose a fuzzy inference system to evaluate the building design codes from an economical point of view. For this purpose, after designing the mentioned fuzzy inference system, three examples of three-dimensional concrete buildings are analyzed and designed using different codes. For all these codes, the structural properties of concrete buildings, the gravity and the seismic loads are considered to be the same. Finally, it finds that the fuzzy logic theory is an effective and practical tool to compute a value that shows the distance between the designed building and the economically designed building. Also, it concludes that between the studied codes, (EUROCODE 2-1992, Hong Kong CP-04, CSA A23.3-04 and ACI 318-05), the ACI 318-05 and Hong Kong CP04 codes lead to a more economical design for taller buildings. For low-rise buildings, the CSA A23.3-04 and ACI 318-05 codes lead to an economical design. Also, the EUROCODE 2-1992 has a minimum value for the economical design of all the considered buildings.


Fuzzy Logic Theory; Economical Design; Seismic Load; Concrete Building; Design Codes.


Nilson, Arthur, Darwin David, and Dolan Charles. “Design of concrete structures, Thirteenth Edition” McGraw Hill, New York. (2004).

McCormac, Jack C, and Brown, Russell H. “Design of reinforced concrete, Tenth Edition” John Wiley and Sons, USA. Inc. (2016).

Subramanian, N. “Design of reinforced concrete structures” Oxford University Press, USA. (2014).

Hassoun, M. Nadim, and Al-Manaseer Akthem. “Structural concrete, Theory & Design, Sixth Edition” John Wiley & Sons, Inc., Hoboken, New Jersey. (2015).

Punmia, B.C., Kumar Jain, Ashok. and Kumar Jain, Arun. “Reinforced concrete structures” Laxmi Publication (P) LTD, New Delhi. (1995).

Jahankhani, H., Carlile, A., Emm, D., Hosseinian-Far, A., Brown, G., Sexton, G. and Jamal, A. “Global security, safety, and sustainability: The Security Challenges of the Connected World.” 11th International Conference, ICGS3 2017, London, UK, January 18-20, 2017.

Choi, K.K. “Reinforced concrete structure design assistant tool for beginners.” MS. C thesis, University of Southern California, 2002.

Ebrahimpour Komleh, H. and Maghsoudi, A.A. “Prediction of curvature ductility factor for FRP strengthened RHSC beams using ANFIS and regression models.” Computers & Concrete 16 (September 2015): 399-414. doi: 10.12989/cac.2015.16.3.399.

Gu, ZQ. and Oyadiji, S.O. “Application of MR damper in structural control using ANFIS method.” Computers and Structures 86 (February 2008): 427-436. doi: 10.1016/j.compstruc.2007.02.024.

Cevik, A. “Modeling strength enhancement of FRP confined concrete cylinders using soft computing.” Expert Systems with Applications 38 (May 2011): 5662-5673. doi: 10.1016/j.eswa.2010.10.069.

Amini, J. and Moeini, R. “Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network.” Scientia Iranica, Transaction A. 19 (April 2012): 242-248. doi: 10.1016/j.scient.2012.02.009.

Mashrei, M.A., Seracino, R. and Rahman, M.S. “Application of artificial neural networks to predict the bond strength of FRP-to-concrete joints.” Constructional Building Materials 40, (March 2013): 812-821. doi: 10.1016/j.conbuildmat.2012.11.109.

Mohammadhassani, M., Nezamabadi-Pour, H., Jumaat, M., Jameel, M., Hakim, S.J.S. and Zargar, M. “Application of the ANFIS model in deflection prediction of concrete deep beam.” Structural Engineering and Mechanics 45, (February 2013): 319-332. doi: 10.12989/sem.2013.45.3.323.

Buckley, J.J. and Eslami, E. “Advances in soft computing, an introduction to fuzzy logic and fuzzy sets” Heidelberg, New York, Physica-Verlag. (2002).

Moraga, C. “Introduction to fuzzy logic.” Facta Universitatis: Electronics and Energetics 18, (2005): 319-328. doi: 10.2298/FUEE0502319M.

Melin, P. and Castillo, O. “Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory.” Applied Soft Computing 3, (December 2003): 353-362. doi: 10.1016/j.asoc.2003.05.006.

Das, A., Maiti, J. and Banerjee, R.N. “Process control strategies for a steel making furnace using ANN with bayesian regularization and ANFIS.” Expert Systems with Applications 37, (March 2010): 1075-1085. doi: 10.1016/j.eswa.2009.06.056.

Najafzadeh, M. and Sattar, A.M.A. “Neuro-fuzzy GMDH approach to predict longitudinal dispersion in water networks.” Water Resources Management 29, (May 2015): 2205-2219. doi: 10.1007/s11269-015-0936-8.

Najafzadeh, M. and Lim S.Y. “Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates.” Earth Science Informatics 8, (March 2015): 187-196. doi: 10.1007/s12145-014-0144-8.

Najafzadeh, M. and Bonakdari, H. “Application of a neuro-fuzzy GMDH model for predicting the velocity at limit of deposition in storm sewers.” Journal of Pipeline Systems Engineering and Practice 8, (February 2017): 1-8. doi: 10.1061/(ASCE)PS.1949-1204.0000249.

Behnood, A., Olek, J. and Glinicki, M.A. “Predicting modulus elasticity of recycled aggregate concrete using M 5′ model tree algorithm.” Construction and Building Materials 94, (September 2015): 137–147. doi: 10.1016/j.conbuildmat.2015.06.055.

Mansouri, Iman, Aliakbar Gholampour, Ozgur Kisi, and Togay Ozbakkaloglu. “Evaluation of Peak and Residual Conditions of Actively Confined Concrete Using Neuro-Fuzzy and Neural Computing Techniques.” Neural Computing and Applications 29, no. 3 (July 25, 2016): 873–888. doi:10.1007/s00521-016-2492-4.

Naderpour, H. and Poursaeidi, M.A. “Shear resistance prediction of concrete beams reinforced by FRP bars using artificial neural networks.” Measurement 126, (October 2018): 299-308. doi: 10.1016/j.measurement.2018.05.051.

Stamopoulos, A.G., Tserpes, K.I. and Dentsoras, A.J. “Quality assessment of porous CFRP specimens using X-ray Computed Tomography data and Artificial Neural Networks.” Composites Structures 192, (May 2018): 327-335. doi: 10.1016/j.compstruct.2018.02.096.

ACI 318-05. “Building code requirements for structural concrete and commentary, ACI Committee 318.” Structural Building Code, American Concrete Institute, Farmington Hills, MI, USA, (2005).

CSA. “Design of concrete structures. CSA standard A23.3-04.” Canadian Standards Association (CSA), Mississauga, Ont, (2004).

Hong Kong CP-04. “Design of concrete structures.” China, (2004).

Eurocode 2. “Design of concrete structures: 1-1: General rules and rules for buildings.” ENV 1992-1-1.22, (1992).

Wang, L.X. “Adaptive fuzzy systems and control: Design and stability analysis” Prentice Hall, New York. (1994).

Elsayed, T. “Fuzzy inference system for the risk assessment of liquefied natural gas carriers during loading/offloading at terminals.” Applied Ocean Research 31, (July 2009): 179–185. doi: 10.1016/j.apor.2009.08.004.

Ghasemi, E. and Ataei, M. “Application of fuzzy logic for predicting roof fall rate in coal mines.” Neural Computing and Applications 22, (May 2013): 311–321. doi: 10.1007/s00521-012-0819-3.

Jamshidi, A., Yazdani-Chamzini, A., Yakhchali, S.H. and Khaleghi, S. “Developing a new fuzzy inference system for pipeline risk assessment.” Journal of Loss Prevention in the Process Industries 26, (January 2013): 197–208. doi: 10.1016/j.jlp.2012.10.010.

Wong, B.K. and Monaco, J.A. “A bibliography of expert system applications for business (1984–1992).” European Journal of Operational Research 85, (September 1995): 416–432. doi: 10.1016/0377-2217(95)00047-t.

BHRC. “Standard No. 2800 Iranian code of practice for seismic resistant design of buildings” Building and Housing Research Center, Iran. (2014).

Etabs V.16.2.1, “Computers and Structures.” Berkeley, California, USA, (2016).

Full Text: PDF

DOI: 10.28991/cej-03091147


  • There are currently no refbacks.

Copyright (c) 2018 Reza Kamgar, Sayyed Morteza Hatefi, Noorollah Majidi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.