**6. Nomenclature**

ܸ௨: Shear strength.

ܸ: The shear resistance of members reinforced with FRP bars as flexural reinforcement.


ܾ௪: Width of the concrete specimen reinforced wih FRP


ߚଵ: Is a function of the concrete compressive strength.

Neural Network and Adaptive Neuro-Fuzzy

d (mm)

��′ (MPa)

b (mm)

ferrocement members

Test No.

Inference System Applied to Civil Engineering Problems 497

�

�� �� %

Vu

(kN) Reference

Mashrei, 2010

�� (MPa)

35 150 25 32.20 380.00 1.00 5.70 13.50 36 150 25 32.20 380.00 2.00 0 0.93 37 150 25 32.20 380.00 2.00 2.85 3.92 38 150 25 32.20 380.00 2.00 3.80 4.95 39 150 25 32.20 380.00 2.00 4.75 5.79 40 150 25 32.20 380.00 2.00 5.70 6.57 41 150 25 32.20 0 3.00 0 0.49 42 150 25 32.20 380.00 3.00 2.85 2.20 43 150 25 32.20 380.00 3.00 3.80 2.55 44 150 25 32.20 380.00 3.00 4.75 2.97 45 150 25 32.20 380.00 3.00 5.70 3.36 46 150 25 32.20 0 4.00 0 0.44 47 150 25 32.20 380.00 4.00 2.85 1.60 48 150 25 32.20 380.00 4.00 3.80 1.99 49 150 25 32.20 380.00 4.00 4.75 2.35 50 150 25 32.20 380.00 4.00 5.70 2.65 51 150 25 32.20 0 5.00 0 0.40 52 150 25 32.20 380.00 5.00 2.85 1.42 53 150 25 32.20 380.00 5.00 3.80 1.86 54 150 25 32.20 380.00 5.00 4.75 2.16 55 150 25 32.20 380.00 5.00 5.70 2.40 56 150 25 32.20 0 6.00 0 0.34 57 150 25 32.20 380.00 6.00 2.85 1.37 58 150 25 32.20 380.00 6.00 3.80 1.84 59 150 25 32.20 380.00 6.00 4.75 2.15 60 150 25 32.20 380.00 6.00 5.70 2.40 61 200 50 33.80 390.00 7.00 0.25 1.16

62 200 50 33.80 390.00 7.00 0.50 1.47 63 200 50 36.90 390.00 7.00 0.99 2.25 64 200 50 40.40 390.00 3.00 0.25 2.94 65 200 50 40.40 390.00 3.00 0.50 3.53 66 200 50 40.40 390.00 3.00 0.99 7.16 67 200 50 41.20 390.00 2.00 0.25 5.40 68 200 50 41.20 390.00 2.00 0.50 7.85 69 200 50 41.20 390.00 2.00 0.99 12.75

Table A. Experimental data used to construct the BPNN and ANFIS for shear strength of

#### **6.1 Appendix**



�� (MPa)

1 100 40 35.20 410.00 1.00 1.80 8.60

2 100 40 35.20 410.00 1.50 1.80 5.40 3 100 40 35.20 410.00 2.00 1.80 3.90 4 100 40 35.20 410.00 2.50 1.80 3.00 5 100 40 35.20 410.00 3.00 1.80 2.50 6 100 40 35.20 410.00 1.00 2.72 10.80 7 100 40 35.20 410.00 1.50 2.72 7.00 8 100 40 35.20 410.00 2.00 2.72 5.70 9 100 40 35.20 410.00 2.50 2.72 4.00 10 100 40 35.20 410.00 3.00 2.72 3.30 11 100 40 36.00 410.00 1.00 3.62 14.00 12 100 40 36.00 410.00 1.50 3.62 9.70 13 100 40 36.00 410.00 2.00 3.62 7.50 14 100 40 36.00 410.00 2.50 3.62 5.90 15 100 40 36.00 410.00 3.00 3.62 4.80 16 100 40 36.00 410.00 1.00 4.52 17.20 17 100 40 36.00 410.00 1.50 4.52 11.60 18 100 40 36.00 410.00 2.00 4.52 8.60 19 100 40 36.00 410.00 2.50 4.52 6.80 20 100 40 36.00 410.00 3.00 4.52 5.60 21 100 40 44.10 410.00 1.00 4.52 19.00

22 100 40 44.10 410.00 1.50 4.52 13.00 23 100 40 44.10 410.00 2.00 4.52 9.50 24 100 40 44.10 410.00 2.50 4.52 7.50 25 100 40 44.10 410.00 3.00 4.52 5.90 26 100 40 26.50 410.00 1.00 4.52 15.50 27 100 40 26.50 410.00 1.50 4.52 9.00 28 100 40 26.50 410.00 2.00 4.52 7.90 29 100 40 26.50 410.00 2.50 4.52 6.20 30 100 40 26.50 410.00 3.00 4.52 5.00 31 150 25 32.20 0 1.00 0 1.84

32 150 25 32.20 380.00 1.00 2.85 8.24 33 150 25 32.20 380.00 1.00 3.80 9.93 34 150 25 32.20 380.00 1.00 4.75 12.00

�

�� �� %

Vu

(kN) Reference

*Mansur Ong*, 1987

*Mansur & Ong, 1987*

*Rao et al.*, 2006

**6.1 Appendix** 

b (mm)

d (mm)

��′ (MPa)

Test No.


Table A. Experimental data used to construct the BPNN and ANFIS for shear strength of ferrocement members

Neural Network and Adaptive Neuro-Fuzzy

d (mm)

��′ (MPa)

Test No.

b (mm)

Inference System Applied to Civil Engineering Problems 499

�� (Gpa)

�

�� Vu

(kN) Reference

Tureyen & Frosch, 2002

Yost et al., 2001

Deitz et al., 1999

Omeman et al.,2008

Al-Sayed, 2006

�� %

38 457 360 39.7 0.96 40.5 3.39 108.1

39 457 360 40.3 0.96 47.1 3.39 114.8 40 457 360 39.9 0.96 37.6 3.39 94.7 41 457 360 42.3 1.92 40.5 3.39 137 42 457 360 42.5 1.92 37.6 3.39 152.6 43 457 360 42.6 1.92 47.1 3.39 177 44 229 225 36.3 1.11 40.3 4.06 38.13

45 229 225 36.3 1.66 40.3 4.06 44.43 46 279 225 36.3 1.81 40.3 4.06 45.27 47 254 224 36.3 2.05 40.3 4.08 45.1 48 229 224 36.3 2.27 40.3 4.08 42.2 49 178 279 24.1 2.3 40 2.69 53.4 50 178 287 24.1 0.77 40 2.61 36.1 51 178 287 24.1 1.34 40 2.61 40.1 52 305 157.5 28.6 0.73 40 4.5 26.8

53 305 157.5 30.1 0.73 40 5.8 28.3 54 305 157.5 28.2 0.73 40 5.8 28.5 55 305 157.5 27 0.73 40 5.8 29.2 56 305 157.5 30.8 0.73 40 5.8 27.6 57 150 150 34.7 1.13 134 1.55 185.2

58 150 150 38.9 1.13 134 1.83 154.9 59 150 150 37.4 1.7 134 1.83 162.3 60 150 150 40.6 1.13 134 2.33 91.5 61 150 150 39.6 2.26 134 1.83 185.5 62 150 250 41.7 1.35 134 1.41 298.1 63 150 350 37.6 1.21 134 1.36 468.2 64 150 150 63.1 1.13 134 1.83 226.9 65 250 326 40 0.78 134 1.69 179.5

66 250 326 40 0.78 40 1.69 164.5 67 250 326 40 1.24 40 1.69 175 68 250 326 40 1.24 134 1.69 195 69 250 326 40 1.71 134 1.69 233.5 70 250 326 40 1.71 40 1.69 196 71 250 326 40 1.24 134 1.3 372 72 250 326 40 1.24 40 1.3 269

73 1000 112 60 0.95 41.3 8.93 42.6 Wegian& 74 1000 162 60 0.77 41.3 6.17 86.1 Abdalla, 2005

Table B. Experimental data used to construct the BPNN and ANFIS for shear strength of

concrete beams reinforced with FRP.


�� (Gpa)

�

�� Vu

(kN) Reference

El-Sayed et al., 2005b

El-Sayed et al., 2006a, 2006b

Razaqpur et al., 2004

Gross et al.,

Tariq & Newhook, 2003

Gross et al., 2003

�� %

1 1000 165.3 40 0.39 114 6.05 140

2 1000 159 40 1.7 40 6.29 142 3 1000 165.3 40 0.78 114 6.05 167 4 1000 160.5 40 1.18 114 6.23 190 5 1000 162.1 40 0.86 40 6.16 113 6 1000 162.1 40 1.71 40 6.16 163 7 1000 159 40 2.44 40 6.29 163 8 1000 154.1 40 2.63 40 6.49 168 9 250 326 44.6 1.22 42 3.07 60

10 250 326 50 0.87 128 3.07 77.5 11 250 326 50 0.87 39 3.07 70.5 12 250 326 44.6 1.24 134 3.07 104 13 250 326 43.6 1.72 134 3.07 124.5 14 250 326 43.6 1.71 42 3.07 77.5 15 250 326 63 1.71 135 3.07 130 16 250 326 63 2.2 135 3.07 174 17 250 326 63 1.71 42 3.07 87 18 250 326 63 2.2 42 3.07 115.5 19 200 225 40.5 0.25 145 2.67 36.1

20 200 225 49 0.5 145 2.67 47 21 200 225 40.5 0.63 145 2.67 47.2 22 200 225 40.5 0.88 145 2.67 42.7 23 200 225 40.5 0.5 145 3.56 49.7 24 200 225 40.5 0.5 145 4.22 38.5 25 127 143 60.3 0.33 139 6.36 14

27 121 141 81.4 0.76 139 6.45 15.4 28 160 346 37.3 0.72 42 2.75 59.1 29 160 346 43.2 1.1 42 3.32 44.1

30 160 325 34.1 1.54 42 3.54 46.8 31 130 310 37.3 0.72 120 3.06 47.5 32 130 310 43.2 1.1 120 3.71 50.15 33 130 310 34.1 1.54 120 3.71 57.1 34 203 225 79.6 1.25 40.3 4.06 38

35 152 225 79.6 1.66 40.3 4.06 32.53 36 165 224 79.6 2.1 40.3 4.08 35.77 37 203 224 79.6 2.56 40.3 4.08 46.4

2004 26 159 141 61.8 0.58 139 6.45 20

Test No.

b (mm)

d (mm)

��′ (MPa)


Table B. Experimental data used to construct the BPNN and ANFIS for shear strength of concrete beams reinforced with FRP.

Neural Network and Adaptive Neuro-Fuzzy

No.2, pp.235-243, ISSN 0889-3241

pp.383-389, ISSN 0889-3241

pp.535-555, ISSN 0965-9978

pp. 47-51, ISSN 0888-3785

Bridges, ASCE, 426-437.

Fuzzy Logic Toolbox User's Guide for Use with MATLAB 2009.

03), Reported by ACI Committee 440, 2003

Calgary, Alberta, Canada, July 20-23, 8p.

8936

3801

Inference System Applied to Civil Engineering Problems 501

El-Sayed, A.; El-Salakawy, E. & Benmokrane, B. (2005b). Shear Strength of One-way

El-Sayed, A.; El-Salakawy, E. & Benmokrane, B. (2006a). Shear Strength of FRP Reinforced

El-Sayed, A.; El-Salakawy, E. & Benmokrane, B. (2006b). Shear Capacity of High-Strength

El-Sayed, A. (2006c),Concrete Contribution to the Shear Resistance of FPR- Reinforced

Feng, M. & Kim, J. (1998). Identification of a Dynamic System Using Ambient Vibration

Feng, M. & Bahng, E. (1999). Damage Assessment of Jacketed RC Columns Using Vibration Tests. *Journal of Structure Engineering*, Vol.125, No.3, pp. 265–271, ISSN 0733-9445 Fonseca, E.; Vellasco, S. & Andrade S. (2008). A Neuro-Fuzzy Evaluation of Steel Beams

Gershenson C. Artificial Neural Networks for Beginners. Cognitive and Computing Sciences, University of Sussex, Available from, http:// cgershen@vub.ac.be Garson, G. (1991). Interpreting Neural-Network Connection Weights. *AI Expert*, Vol.6, No.7,

Gagarin, N.; Flood, I., & Albrecht, P. (1994). Computing Truck Attributes with Artificial

Guide for the Design and Construction of Concrete Reinforced with FRP Bars (ACI 440.1R-

Gross, S.; Dinehart, D.; Yost, J. & Theisz, P. (2004). Experimental Tests of High-Strength

Gross, S. P.; Yost, J.; Dinehart, D. W.; Svensen, E. & Liu, N. (2003). Shear Strength of Normal

Hajela, P. & Berke, L. (1991). Neurobiological Computational Models in Structural Analysis and Design. *Computers and Structures*, Vol.41, No.4, pp. 657-667, ISSN 0045-7949 Hamidian D. & Seyedpoor M. (2010). Shape Optimal Design of Arch Dams Using an

Neural Networks. *Journal of Computing in Civil Engineering*, Vol.8, No.2, ISSN 0887-

Concrete Beams Reinforced with CFRP Bars, *Proceedings of the 4th International Conference on Advanced Composite Materials in Bridges and Structures (ACMBS-4)*,

and High Strength Concrete Beams Reinforced with GFRP Reinforcing Bars, Proceedings of the International Conference on High Performance Materials in

Adaptive Neuro-Fuzzy Inference System and Improved Particle Swarm Optimization. *Jornal of Applied Mathematical Modelling*. Vol.34, No.6. pp.1574-1585. Jang, S.; Sun T. & Mizutani E. (1997). *Neuro-Fuzzy and Soft Computing A Computational Approach to Learning and Machine intelligence*, Prentice Hall, Inc. ISBN 0132610663

*Construction*, ASCE, Vol.9, No.2, pp.147-157, ISSN 1090-0268

Concrete beams, PhD Thesis, Sherbrook University, Canada.

Concrete Slabs Reinforced with FRP Composite Bars. *Journal of Composites for* 

Concrete Beams without Transverse Reinforcement. *ACI Structural Journal*, Vol.103,

Concrete Beams Reinforced with FRP Bars. *ACI Structural Journal,*Vol.103, No.3,

Measurements. *Journal of Applied Mech*anic, Vo.65, No.2, pp. 1010– 1023, ISSN 0021-

Patch Load Behaviour. *Journal of Advances in Engineering Software*, Vol.39, No.7,

#### **7. References**


Adeli, H. & Park, H. (1995). A Neural Dynamic Model for Structural Optimization-Theory. *Journal of Computer and Structure*, Vol.57, No.3, pp. 383–390, ISSN 0045-7949 Abudlkudir, A.; Ahmet, T.& Murat, Y. (2006). Prediction of Concrete Elastic Modulus Using

Akbuluta, S.; Samet, H. & Pamuk S. (2004). Data Generation for Shear Modulus and

Alkhrdaji, T.; Wideman, M.; Belarbi, A. & Nanni, A. (2001). Shear Strength of GFRP RC

Bank .L. (2006). *Composites for Construction: Structural Design with FRP Materials*, John Wiley,

Baughman, D. & Liu, Y. (2005). *Neural Network in Bioprocessing and Chemical Engineering*.

Carpenter, W.& Barthelemy, J. (1994). Common Misconceptions about Neural Networks as Approximators. *Journal of Computing in Civil Engineering*, Vol.8, No.3, pp. 345-358 Chen, S. & Shah, K. (1992). Neural Networks in Dynamic Analysis of Bridges. Proceedings,

Chiu S. (1994). Fuzzy Model Identification Based on Cluster Estimation. *Journal of Intelligent* 

Cladera, A. & Mar A. (2004). Shear Design Procedure for Reinforced Normal and High-

Eberhart, R. & Dobbins, R. (1990). *Neural Network PC Tools A Practical Guide*. Academic Press,

Eldin, N. & Senouci, A.(1995), A Pavement Condition-Rating Model Using Back Propagation Neural Networks. *Microcomputers in Civil Engineering,* Vol.10, No.6, pp. 433–441 El-Sayed, A.; El-Salakawy, E. & Benmokrane, B. (2004). Evaluation of Concrete Shear

*Environmental Systems*, Vol.23, No.4, pp.295–309, ISSN 1028-6608

Adaptive Neuro-Fuzzy Inference System. *Journal of Civil Engineering and* 

Damping Ratio in Reinforced Sands Using Adaptive Neuro-Fuzzy Inference System. *Journal of Soil Dynamics and Earthquake Engineering*, Vol. 24, No.11, pp. 805–

Beams and Slabs, *Proceedings of the International Conference, Civil Construction International Conference (CCC) 2001, Composites in Construction*, pp. 409-414,

8th Confernce. *Computing in Civil Engineering and Geographic Information System* 

Strength Concrete beams Using Artificial Neural Networks. Part I: Beams Without Stirrups. *Journal Engineering Structure*, Vol.26, No.7 pp. 917-926, ISSN 0141-0296 Cladera, A. & Mar A. (2004). Shear Design Procedure for Reinforced Normal and High-

Strength Concrete beams Using Artificial Neural Networks. Part II: Beams With Stirrups*. Journal Engineering Structure,* Vol. 26, No.7pp. 927-936, ISSN 0141-0296 Deitz, D.; Harik, I. & Gesund, H. (1999). One-Way Slabs Reinforced with Glass Fiber

Reinforced Polymer Reinforcing Bars, *Proceedings of the 4th International Symposium, Fiber Reinforced Polymer Reinforcement for Reinforced Concrete Structures*, pp. 279-286,

Strength for Beams Reinforced with FRP Bars, *5th Structural Specialty Conference of the Canadian Society for Civil Engineering, CSCE*, Saskatoon, Saskatchewan, Canada El-Sayed, A.; El-Salakawy, E. & Benmokrane, B. (2005a). Shear Strength of Concrete Beams Reinforced with FRP Bars: Design Method, ACI- SP-230—54, pp.955-974

**7. References** 

814, ISSN 0267-7261

Porto/Portugal

Maryland, USA

ISBN 0471681261, New Jersey

Academic Press,. ISBN 0120830302. San Diego, CA

*Symposium* ASCE, PP.1058–1065, New York, USA.

*and Fuzzy System*, Vol.2, No.3, pp.267-278.

ISBN0-12-228640-5, San Diego, CA


Neural Network and Adaptive Neuro-Fuzzy

262.

pp. 818-828, ISSN 0733-9445

ISBN0444878297, NY, USA

No.7, pp. 550-560, ISSN 0899-1561

University of Newfoundland.

ISSN 0263-8223

pp.35–49, ISSN 0376-9429

32, ISSN 0018-9235

Vol.5, No.4, pp. 268-275, ISSN 1090-0268

the ACM, Vol.3, No.3, pp.77-84.

*Science*, Vol.41, No.3, pp.305-311, ISSN0927-0256

*(CSCE)Anuual Conference*, Moncton, NB, Canada, 10p.

Prentice-Hall, Englewood Cliffs, NJ, ISBN: 0130894656.

*Structural Journal*. Vol.100, No.5, pp. 609-615.

*of Structures and Materials*, Udine, Italy, pp. 19-23.

Inference System Applied to Civil Engineering Problems 503

Sanad, A. & Saka, M. (2001). Prediction of Ultimate Shear Strength of Reinforced Concrete

Shah, S. (1974). New Reinforcing Materials in Concrete. *Journal of ACI*, Vol.71, No.5, pp. 257-

Sugeno, M.(1985). *Industrial applications of fuzzy control*, Elsevier Science Pub,

Topcu, I. & Sardemir M. (2007). Prediction of Compressive Strength of Concrete Containing

Tariq, M. & Newhook, J. (2003). Shear Testing of FRP reinforced Concrete without

Tesfamariam, S. & Najjaran, H. (2007). Adaptive Network-Fuzzy Inferencing to Estimate

Tully, S. (1997). Neural Network Approach for Predicting the Structural Behaviour of

Turban, E. & Aronson, J. (2000). *Decision Support Systems and Intelligent Systems*, 6th edition,

Tureyen, A. & Frosch, R. (2002). Shear Tests of FRP-Reinforced Concrete Beams without Stirrups. *ACI Structural Journal*, Vol.99, No.4, pp.427-434, ISSN 0889-3241 Tureyen, A. & Frosch, R. (2003). Concrete Shear Strength: Another Perspective. *ACI* 

Waszczyszyn, Z.; Pabisek, E. & Mucha, G., (1998). Hybrid Neural Network/Computational

Wegian, F.& Abdalla, H. (2005). Shear Capacity of Concrete Beams Reinforced with Fiber

Wu, Z. & Bailey, C. (2005). Fracture Resistance of a Cracked Concrete Beam Post

Yost, J.; Gross, S. & Dinehart, D. (2001). Shear Strength of Normal Strength Concrete Beams

Zadih, L. (1993). Fuzzy Logic, Neural Networks and Soft Computing. *Microprocessing and* 

Zadeh, L. (1994), Fuzzy Logic, Neural Networks and Soft Computing. Communication of

Zadeh L. (1965). Fuzzy sets. *Journal of Information and Control*, Vol.8, No.3, pp. 338-353. Zadeh, L. Making Computers Think Like People. *IEEE Spectrum*. 1984; Vol.21, No.8 pp.26-

*Microprogramming,* Vol.38, No.1,pp.13, ISSN 0165-6074

Program to the Analysis of Elastic- Plastic Structures, *Neural networks in Mechanics* 

Reinforced Polymers. *Journal of Composite Structures*, Vol.71, No.1, pp. 130–138,

Strengthened with FRP Sheets. *International Journal of Fracture*, Vol.135, No.(1-4),

Reinforced with Deformed GFRP Bars. *Journal of Composites for Construction*, ASCE,

Deep Beams using neural Networks. *Journal of Structural Engineering*, Vol.127, No.7,

Fly Ash Using Artificial Neural Networks and Fuzzy Logic. *Computational Materials* 

Transverse Reinforcement, *Proceedings of Canadian Society for Civil Engineering* 

Concrete Strength Using Mix Design. *Journal of Materials in Civil Engineering*, Vol.19,

Concrete Slabs. M.Sc Thesis, College of Engineering and Applied Science,


Jang S. (1993). Adaptive network-based Fuzzy Inference System. *IEEE Journal*, Vol.23, No.3,

Jeon J. (2007). Fuzzy and Neural Network Models for Analyses of Piles. PhD thesis, Civil

Lin. C. & Lee. C. (1996). *Neural Fuzzy Systems-A Neuro Fuzzy Synergism to Intelligent Systems*.

Lin, J.; Hwang, M.; Becker, J. (2003). A Fuzzy Neural Network for Assessing the Risk of

Mamdani, E. & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic

Mansur, M. & Ong, K. (1987). Shear Strength of Ferrocement Beams. *ACI Structural Journal*,

Mansour, M.; Dicleli, M.; Lee, J. & Zhang, J. (2004). Predicting the Shear Strength of

Malhotra, R. & Malhotra, D. (1999). Fuzzy Systems and Neuro-Computing in Credit Approval. *Journal of Lending & Credit Risk Management*, Vol.81, No.11, pp. 24-37. Mashrei, M. (2010). Flexure and Shear Behavior of Ferrocement Members: Experimental and Theoretical Study, PhD thesis, Civil Engineering, Basrah University, Iraq. McCulloch and Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity. *Bulletin of Mathematical Biophysics*, Vol.(5), pp. 115-133, ISSN 0092-8240 Mukherjee, A.; Deshpande, J. & Anmada, J. (1996). Prediction of Buckling Load of Columns

Naaman, A. *(2000. Ferrocement and Laminated Cementitious Composites*. Ann Arbort,

Omeman, Z.; Nehdi, M. & El-Chabib, H. (2008). Experimental Study on Shear Behavior of

Reinforcement. *Canadian Journal of Civil Engineering*, Vol.35, No.1, pp.1-10. Rafiq, M.; Bugmann, G. & Easterbrook, D. (2001). Neural Network Design for Engineering Applications, *Journal of Computers and Structures*, Vol.79, No. 17, pp.1541-1552. Rao, C.; Rao, G. & Rao, R. (2006). An Appraisal of the Shear Resistance of Ferrocement

Razaqpur, A.; Isgor, B.; Greenaway, S. & Selley, A. (2004). Concrete Contribution to the

Michigan, Techno Press 3000, USA 2000. ISBN 0967493900

Neural Network Toolbox User's Guide for Use with MATLAB, 2009

Prentice Hall P T R. Upper Saddle River, N.J., ISBN 0-13-235169-2

for Automatic Determination of Rules to Control Locomotion. *Journal of IEEE*,

Fraudulent Financial Reporting. *Managerial Auditing Journal*, Vol.18, No.8, pp. 657-

controller. *International Journal of Man Machine Studies*, Vol.7, No.1, pp. 1-13, ISSN

Reinforced Concrete Beams Using Artificial Neural Networks. *Journal of Engineering* 

Using Artificial Neural Networks. *Journal of . Structural Engineering,* Vol.122, No.11,

Carbon-Fiber-Reinforced Polymer Reinforced Concrete Short Beams without Web

Elements. *ASIAN Journal of Civil Engineering (Building and Housing)*, Vol.7, No.6, pp.

Shear Resistance of Fiber Reinforced Polymer Reinforced Concrete Members. *Journal of Composites for Construction*, ASCE, Vol.8, No.5, pp. 452-460,ISSN 1090-0268

, D. (1999). Three Machine Learning Techniques

, V. & Popovic'

*Structures*, Vol.26, No.6, pp.781–799, ISSN 0141-0296

PP.665-685, ISSN 0018-9472

Engineering, NCSU, USA.

665, ISSN 0268-6902

Vol.84, No.1, pp. 10-17.

pp. 1385–1387, ISSN 0733-9445

00207373

591-602.

, T.; Gajic'

Vol.46, No.3, pp.300-310, ISSN 0018-9294.

Jonic'

, S.; Jankovic'


Zaho, J. & Bose, B. (2002). Evaluation of membership Functions for Fuzzy Logic Controlled Induction Motor Drive. *IEEE Journal*, Vo.1, No.pp.229-234, ISBN 0-7803-7474-6. S

Zaho, J. & Bose, B. (2002). Evaluation of membership Functions for Fuzzy Logic Controlled Induction Motor Drive. *IEEE Journal*, Vo.1, No.pp.229-234, ISBN 0-7803-7474-6. S
