2015
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1
5
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Steel Plate Characteristic Effecting on Composite Coupled Beam at Concrete Shear Wall
2
2
Composite couple beams are the concrete elements consisting of longitudinal bars and steel plate, therefore suitable for shear transferring in couple shear walls with arranged gates in its height. In this paper, after modeling couple beams with and without steel plates with F.E methods and calibration the models with experimental results, effects of parameters such as thickness, height, length and yielding strength of the steel plates located in concrete couple composite beam have been investigated on the ductility, energy dissipation and capacity. The results were illustrated that if the plate thickness would be increased by four times, ductility and energy dissipation capacity were decreased 15.6 and 14.7 percent and also loading capacity was enhanced up to 25 percent, respectively. And also the plate height and length didn’t have influence on above mentioned parameters. Furthermore, by 80 and 280 percent enhancement in yielding plate strength, ductility and energy dissipation capacity were decline 10.8 to 23.9 and 8.9 to 21.7 percent and also 19, 33 percent enhancement in loading capacity was happened.
1

1
13


MohammadKazem
Sharbatdar
Associate Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran
Associate Professor, Faculty of Civil Engineering,
Iran
msharbatdar@semnan.ac.ir


Mohammad Ali
Kafi
Assistant Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran
Assistant Professor, Faculty of Civil Engineering,
Iran
mkafi@semnan.ac.ir


Amin
Behrad
Graduated M.Sc. Student of Structural Eng., Semnan University, Semnan, Iran
Graduated M.Sc. Student of Structural Eng.,
Iran
behrad@yahoo.com
Composite beams
Steel plate
Energy dissipation
Shear walls
F.E. method
Ductility
[[1] Fortney, P.J., Shahrouz, B.M., Rassati, G.A. (2006). “The next generation of coupling beams”. Composite Construction in Steel and Concrete V, ASCE, pp. 619630. ##[2] Gong, B., Harris, K.A., Shahrouz, B.M. (2000). “Behaviors and design of reinforced concrete, steel, and steelconcrete coupling beams”. Earthquake Spectra, Vol. 16, No. 4, pp. 77599. ##[3] Gong, B., Shahrouz, B.M. (2001). “Concretesteel composite coupling beams I: Component testing”. Journal of structural Engineering, ASCE, Vol. 127, No. 6, pp. 62531. ##[4] Harris, K.A., Mitchell, D., Cook, W.D., Redwood, R.G. (1993). “Seismic response of steel beams coupling concrete walls”. Journal of Structural Engineering, ASCE, Vol. 119, No. 12, pp. 361129. ##[5] Riazi, M. (2003). “Modeling concrete couple beams with regular reinforcement at shear walls”, M.S. Thesis, Ferdosi University, Iran. ##[6] Mahzarnia, S.H. (2003). “Investigation of shear walls behavior with steel couple beams”. M.S. Thesis, Semnan University, Iran. ##[7] Subedi, N.K. (1989). “Reinforced concrete beams with plate reinforcement for shear”. Proceeding of Institution of Civil Engineers part 1Design & Construction, Vol. 87, pp. 37799. ##[8] Teng J.G., Chen J.F., Lee Y.C. (1999). “Concretefilled steel tubes as coupling beams for RC shear walls”. Proceedings of the Second International Conference on Advances in Steel Structures, pp. 391399. ##[9] ElTawil, S., Harris, K.A., Fortney, P.J., Shahrooz, B.M., Kurama, Y. (2010). “Seismic design of hybrid coupled wall systems: state of the art”. Journal of structural engineering, Vol. 136, Issue 7, pp. 755769. ##[10] Lu, X., Chen, Y. (2005). “Modeling of coupled shear walls and its experimental verification”. Journal of Structural Engineering, Vol. 131, Issue 1, pp. 7584. ##[11] ANSYS, (2009). “ANSYS. User’s Manual”. 10.0, SAS IP, Inc.##]
Adaptive Neural Fuzzy Inference System Models for Predicting the Shear Strength of Reinforced Concrete Deep Beams
2
2
A reinforced concrete member in which the total span or shear span is especially small in relation to its depth is called a deep beam. In this study, a new approach based on the Adaptive Neural Fuzzy Inference System (ANFIS) is used to predict the shear strength of reinforced concrete (RC) deep beams. A constitutive relationship was obtained correlating the ultimate load with seven mechanical and geometrical parameters. These parameters contain Web width, Effective depth, Shear span to depth ratio, Concrete compressive strength, Main reinforcement ratio, Horizontal shear reinforcement ratio and Vertical shear reinforcement ratio.The ANFIS model is developed based on 214 experimental database obtained from the literature. The data used in the present study, out of the total data, 80% was used for training the model and 20% for checking to validate the model. The results indicated that ANFIS is an effective method for predicting the shear strength of reinforced concrete (RC) deep beams and has better accuracy and simplicity compared to the empirical methods.
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14
23


Atieh
Khajeh
M.S student, Department of Civil Engineering, University of Sistan and Baluchestan, zahedan, Iran
M.S student, Department of Civil Engineering,
Iran
atiehkhajeh@gmail.com


Seyed Roohollah
Mousavi
Assistant Professor, Department of Civil Engineering, University of Sistan and Baluchestan, zahedan, Iran
Assistant Professor, Department of Civil
Iran
s.r.mousavi@eng.usb.ac.ir


Mehrollah
Rakhshani Mehr
Assistant Professor, Department of Civil Engineering, University of Alzahra, Tehran, Iran
Assistant Professor, Department of Civil
Iran
rakhsh_77@yahoo.com
Shear strength
RC deep beams
Adaptive Neural Fuzzy Inference System (ANFIS)
[[1] BarraBirrcher, D.. (2009). "Design of reinforced concrete deep beams for strength and serviceability". Ph. D Thesis, The University of Texas at Austin, USA, PP. 370. ##[2] Collins,MP., Kuchma, D.. (1999). "How safe are our large, lightly reinforced concrete beams, slab and footing". ACI Struct J, Vol. 96, pp. 482490. ##[3] Kani, GNJ.. (1967)."How safe are our large RC beams" ACI journal Proceedings, Vol. 64,.pp. 12841. ##[4] Yang,KH., Ashour, AF.. (2011). " Strutandtie model based on crack band theory for deep beams". J Struct Eng, Vol. 137,pp. 1030–1038. ##[5] Pal, M., Deswal,S.. (2011)."Support vector regression based shear strength modeling of deep beams". Comput Struct, Vol. 89, pp. 1430–1439. ##[6] Ashour,AF., Alvarez, LF., Toropov, VV.. (2003). "Empirical modeling of shear strength of RC deep beams by genetic programming". ComputStruct, Vol. 81, pp. 331–338. ##[7] Zararis,PD..(2003)."Shear compression failure in reinforced concrete deep beams" J StructEng, Vol. 129, pp. 544–553. ##[8] ACI318.(1995)."Building Code Requirements for Reinforced Concrete (ACI 318 M95) and Commentary".ACI 318RM95, American Concrete Institute. ##[9] ACI318,31805/318 R05.(2005)."Building Code Requirements for Structural Concrete and Commentary". American Concrete Institute, pp. 432. ##[10] ACI318, 31808.(2008)."Building Code Requirements for Structural Concrete and Commentary, American Concrete Institute". ##[11] ACI318, 31811.(2011)."Building Code Requirements for Structural Concrete and Commentary, American Concrete Institute". ##[12] CSA. (1994). "Design of Concrete Structures: A National Standard of Canada". CANA23.394,Toronto. ##[13] Matamoros, AB., Wong KH..(2003). Design of simply supported deep beams using strutandtie models". ACI Struct J, Vol. 100, pp. 704–712. ##[14] Park,JW., Kuchma, D.. (2007). " Strutandtie model analysis for strength prediction of deep beams". ACI StructJ, Vol. 104, pp. 657–666. ##[15] Goh, ATC..(1995)."Prediction of ultimate shear strength of deep beams using neural networks".ACI Struct J, Vol. 92, pp. 2832. ##[16] Yeh,IC..(1998)."concrete strength with augmentneuron networks".J. Mater. Civ. Eng., Vol. 10, pp. 263–268. ##[17] Atici, U..(2011)."Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network".Expert Systems with Applications, Vol. 38, pp. 9609–9618. ##[18] Oreta,AWC., Kawashima, K..(2003). Neural network modeling of concrete compressive strength and strain of circular concrete columns".J. Struct. Eng., Vol. 129, pp. 554561. ##[19] Sanad, A.,Saka, M..(2001)."Prediction of ultimate shear strength of reinforced concrete deep beams using neural networks". J. Struct. Eng., Vol. 127, pp. 818–828. ##[20] Lee,SC..(2003)."Prediction of concrete strength using artificial neural networks". Eng. Struct, Vol. 25, pp. 849–857. ##[21] Kim,JI., Kim, DK., Feng, MQ. And Yazdani F..(2004)."Application of neural networks for estimation of concrete strength".J. Mater. Civ. Eng., Vol. 16, pp. 257–264. ##[22] Mansour, MY.,Dicleli, M., Lee, JY. and Zhang J.. (2004)."Predicting the shear strength of reinforced concrete beams using artificial neural networks". Eng. Struct, Vol. 26, pp. 781–799. ##[23] Cladera, A., Mari,AR..(2004)."Shear design procedure for reinforced normal and high strength concrete beams using artificial neural networks. PartII: beams with stirrups".Eng. Struct, Vol. 26, pp. 927–936. ##[24] Tang, CW.. (2006)."Using radial basis function neural networks to model torsional strength of reinforced concrete beams".Comput Concr, Vol. 3, pp. 335–355. ##[25] Abdalla,JA.,Elsanosi, A. and Abdelwahab A..(2007)."Modeling and simulation of shear resistance of RC beams using artificial neural network".J. Franklin Inst, Vol. 344, pp. 741–756. ##[26] Caglar, N., Elmas, M., Yaman, ZD. andSaribiyik M..(2008)."Neural network in 3dimensional dynamic analysis of reinforced concrete buildings". Constr. Build. Mater, Vol. 22, pp. 788–800. ##[27] Arslan, MH.. (2010)."Predicting of tensional strength of RC beams by using different artificial neural network algorithms and building codes". Adv. Eng. Softw,Vol. 41, pp. 946–955. ##[28] Ashour,AF., Alvarez, LF., Toropov, VV.. (2003)."Empirical modeling of shear strength of RC deep beams by genetic programming".Comput Struct, Vol. 81, pp. 331–338. ##[29] Gandomi,AH.,Alavi, AH., Yun, GJ.. (2011). "Nonlinear modeling of shear strength of SFRC beams using linear genetic programming".Struct. Eng. Mech, Vol. 38, pp, 1–25. ##[30] Gandomi,AH., Yong, GJ., Alavi, AH..(2013)."An evolutionary approach for modeling of shear strength of RC deep beams". Materials and Structures, Vol. 46, pp. 21092119. ##[31] Jang,S..(1993). "Adaptive networkbased Fuzzy Inference System". IEEE Journal,Vol. 23, pp. 665685. ##[32] Abudlkudir, A., Ahmet, T. and Murat Y.. (2006)."Prediction of Concrete Elastic Modulus Using Adaptive NeuroFuzzy Inference System" Journal of Civil Engineering and Environmental Systems, Vol. 23, pp. 295–309. ##[33] Tesfamariam, S.,Najjaran, H..(2007). "Adaptive NetworkFuzzy Inferencing to Estimate Concrete Strength Using Mix Design". Journal of Materials in Civil Engineering, Vol. 19, pp. 550560. ##[34] Fonseca, E., Vellasco, S. and Andrade,S..(2008). "A NeuroFuzzy Evaluation of Steel Beams Patch Load Behaviour". Journal of Advances in Engineering Software, Vol. 39, pp. 535555. ##[35] Mohammadhassani, M., NezamabadiPour, H., Jumaat,MZ.,Jameel, M., Hakim SJS.andZargar M..(2013)."Application of the ANFIS model in deflection prediction of concrete deep beam".Structural Engineering and Mechanics, Vol. 45, pp. 319332. ##[36] Smith, KN., Vantsiotis, AS..(1982). "Shearstrength of deep beams". J. Am. Concr. Inst, Vol. 79, pp. 201–213. ##[37] Kong,FK., Robins, PJ., Cole, DF..(1970). "Web reinforcement effects on deep beams".ACI Journal Proceeding, Vol. 67, pp. 1010–1017. ##[38] Clark, AP..(1951)."Diagonal tension in reinforced concrete beams". ACI Journal Proceeding, Vol. 48, pp. 145–156. ##[39] Oh,JK., Shin, SW..(2001). "Shear strength of reinforced highstrength concrete deep beams". ACI Struct J, Vol. 98, pp. 164–173. ##[40] Aguilar, G., Matamoros, AB., ParraMontesinos,GJ., Ramirez, JA.and Wight JK..(2002)."Experimental evaluation of design procedures for shear strength of deep reinforced concrete beams". ACI Struct J, Vol. 99, pp. 539–548. ##[41] QuinteroFebres,CG., ParraMontesinos, G., Wight, JK.. (2006)."Strength of struts in deep concrete members designed using strutandtie method". ACI Struct J, Vol. 103, pp. 577–586. ##[42] Tan,KH., Kong, FK., Teng, S. and Guan L.. (1995). "Highstrength concrete deep beams with effective span and shear span variations".ACI Struct J, Vol. 92, pp. 395–405. ##[43] Anderson,NS., Ramirez, JA.. (1989). "Detailing of stirrup reinforcement". ACI Struct J, Vol. 86, pp. 507–515. ##[44] Sugeno, M..(1985). "Industrial Applications of Fuzzy Control", Elsevier, Amsterdam. ##[45] Golbraikh, A. and Tropsha, A.. (2002). "Beware of q2!".J. Mol. Graphics Modell, Vol. 20, pp. 269276. ##[46] Roy, PP., Roy,K..(2008)."On some aspects of variable selection for partial least squares regression models".QSAR Comb.Sci, Vol. 27, pp. 302313.##]
Steel Buildings Damage Classification by damage spectrum and Decision Tree Algorithm
2
2
Results of damage prediction in buildings can be used as a useful tool for managing and decreasing seismic risk of earthquakes. In this study, damage spectrum and C4.5 decision tree algorithm were utilized for damage prediction in steel buildings during earthquakes. In order to prepare the damage spectrum, steel buildings were modeled as a singledegreeoffreedom (SDOF) system and timehistory nonlinear analysis was carried out to develop a set of SDOF structures. Then, damage index was used to prepare the damage spectrum. Data parameters required for training and evaluating the C4.5 decision tree algorithm were obtained from the results of damage spectra for steel structures and using Krawinkler damage index Also, two decision trees were trained based on quantitative indices. The first decision tree determined whether damage occurred in buildings or not and the second predicted severity of damage as repairable, beyond repair, or collapse. decision tree classification algorithm was used to predict damage to steel structures.
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24
42


Seyed Amir Hossein
Hashemi
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Civil Engineering, Science
Iran
a.hashemi@srbiau.ac.ir


Gholamreza
Ghodrati Amiri
Professor, School of Civil Engineering Iran University of Science & Technology
Professor, School of Civil Engineering Iran
Iran
ghodrati@iust.ac.ir


Farzaneh
Hamedi
Assistant Professor, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran
Assistant Professor, Faculty of Engineering,
Iran
hamedi@ikiu.ac.ir
Damage prediction
Damage index
Steel buildings
Decision tree algorithm
[[1] Riddell, R.Garcia, JE.Garces, E.( 2002)."Inelastic deformation response of SDOF systems subjected to earthquakes". Earthquake Eng Struct Dyn, 515,pp.31–38. ##[2] Karbassi, A. Mohebi, B. Rezaee, S. Lestuzzi, P. (2014). "Damage prediction for regular reinforced concrete buildings using the decision tree algorithm". Computers and Structures ,130, 46–56. ##[3] Amziane, S., Dube, JF.(2008)." Global RC structural damage index based on the assessment of local material damage". J Adv Concr Technol,6,459–68. ##[4] Wahalthantri, BL.Thambiratnam, DP. Chan, THT. Fawzia, S.(2012)."An improved method to detect damage using modal strain energy based damage index". Adv Struct Eng,15,727–42. ##[5] BenaventCliment,. A.( 2011)."A seismic index method for vulnerability assessment of existing frames: application to RC structures with wide beams in Spain". Bull Earthquake Eng,9,491–517. ##[6] Bozorgnia,Y. Bertero,V.(2003)."Damage Spectra: Characteristics and Applications to SeismicRisk Reduction". Journal of Structural Engineering, 129, no. 10,13301340. ##[7] Elenas. A. Meskouris, K. (2001)." Correlation study between seismic acceleration parameters and damage indices of structures". Eng Struct,23,698–704. ##[8] Ghobarah, A. and Osman, A.( 1995)." Seismic Damage Assesment in LowRise Steel Moment Resisting Frames". 10th European Conference on Earthquake Engineering. ##[9] DiPasquale, E. Cakmak, AS. (1987)."Detection and assessment of seismic structural damage".State University of New York at Buffalo, National Center for Earthquake Engineering Research. ##[10] McCabe, SL.Hall ,WJ. (1989)."Assessment of seismic structural damage". J Struct Eng ASCE 115,2166–2183. ##[11] Krawinkler, H. Zohrei, M. (1983)."Cumulative Damage in Steel Structures Subjected to Earthquake Ground Motions. Computers and Structures".,16, 531541. ##[12] Building and Housing Research Center (BHRC), http://www.bhrc.ir/. ##[13] Hashemi, S. A. H. Ghodrati Amiri ,G. Mohebi, B. Hamedi, F. (2014)."Developing the attenuation relation for damage spectrum in Xbraced steel structures with neural network. JVE. Internationalltd".Journal of Vibroengineering , 16, 8. 38793900 ##[14] Witten, IH. Frank, E. Hall, MA. (2011).,"Data mining: practical machine learning tools andtechniques". 3rd ed. Burlington: Morgan Kaufmann. ##[15] Quinlan JR. C4.5: programs for machine learning. Morgan KaufmannPublishers; 1993. ##[16] Hall ,M. Frank, E. Holmes, G. Pfahringer, B. Reutemann, P. Witten, IH. The WEKA data mining software: an update. SIGKDD Explor 2009;11(1). ##[17] Matlab (7.6.0.324(R2008a)).[The Language of Technical Computing].USA:Math Works, Inc,U.S. patents. ##[18] Opensees (2.4)[Open system for earthquake engineering simulation]. University of California, Berkeley :Pacifc Earthquake Engineering Research Center.##]
Achievement of Minimum Seismic Damage for Zipper Braced Frames Based on Uniform Deformations Theory
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2
When structures are subjected to strong ground motion excitations, structural elements may be prone to yielding, and consequently experience significant levels of inelastic behavior. The effects of inelastic behavior on the distribution of peak floor loads are not explicitly accounted for in current seismic code procedures. During recent years, many studies have been conducted to develop new design procedures for different types of buildings through proposing improved design lateral load patterns. One of the most important parameters of structural damage in performancebased seismic design is to limit the extent of structural damages (maximum interstory ductility ratio) in the system and distribute them uniformly along the height of the structures. In this paper, a practical method is developed for optimum seismic design of zipperbraced frames (ZBF) subjected to seismic excitations. More efficient seismic design is obtained by redistributing material from strong to weak parts of a structure until a state of uniform ductility ratio (damage) prevails. By applying the proposed design algorithm on 5, 10 and 15‐storey zipperbraced frames subjected to 10 synthetic seismic excitations, the efficiency of the proposed method is investigated for specific synthetic seismic excitations. The results indicate that, for a constant structural weight, the structures designed according to the proposed optimization algorithm experience up to 50% less global ductility ratio (damage) compared with codebased design structures.
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43
60


Javad
Vaseghi Amiri
Associate Professor, Faculty of Civil Engineering, Babol University of Technology, Babol, Iran
Associate Professor, Faculty of Civil Engineering,
Iran
vaseghi@nit.ac.ir


Mojtaba
Esmaeilnia Amiri
Ph.D. Student, Faculty of Civil Engineering, Babol University of Technology, Babol, Iran
Ph.D. Student, Faculty of Civil Engineering,
Iran
m.e.amiri.ac@gmail.com


Behnoud
Ganjavi
Assistant Professor, Department of Civil Engineering, University of Mazandaran, Babolsar, Iran
Assistant Professor, Department of Civil
Iran
b.ganjavi@umz.ac.ir
Zipperbraced Frame structures
maximum damage
uniform ductility distribution
inelastic behavior
strong ground motion excitation
[[1] Green, N.B. (1981). “Earthquake Resistant Building Design and Construction”. Second Edition, Van Nostrand Reinhold Company, New York. ##[2] Hart, G.C. (2000). “Earthquake forces for the lateral force code”. The structural Designof Tall Buildings, Vol. 9, pp. 4964. ##[3] Chopra, A.K. (2001). “Dynamics of structures, Theory and Applications to EarthquakeEngineering”. 2nd Edition, Prentice Hall Inc., London. ##[4] Mahin, S.A. (1998). “Lessons from damage to steel buildings during the Northridgeearthquake”. Engineering Structures, Vol. 20, No. 4, pp. 261270. ##[5] Building Seismic Safety Council (BSSC). “National Earthquake Hazard Reduction Program (NEHRP)”. Recommended Provisions for Seismic Regulations for 348 New Buildings and Other StructuresPart 2: Commentary (FEMA450–2), Federal Emergency Management Agency, Washington, D. C, 2003. ##[6] IBC (2012). “International Building Code. International Code Council”. Country Club Hills, USA. ##[7] UBC (1997). “Structural engineering design provisions. In: Uniform building code”. International conference of building officials, Vol.2. ##[8] Park, K., Medina, RA. (2007). “Conceptual seismic design of regular frames based on the concept of uniform damage”. Journal of Structural Engineering (ASCE) 133(7): 945955. ##[9] Chopra, A.K. (2012), “Dynamics of structures, Theory and Applications to EarthquakeEngineering”. 4th Edition, Prentice Hall Inc., London. ##[10] Leelataviwat, S., Goel, SC. and Stojadinovic´ B. (1999). “Toward performancebased seismic design of structures”. Earthquake Spectra, 15; 435461. ##[11] Lee, SS, and Goel, SC. (2001). “PerformanceBased Design of Steel Moment Frames Using Target Drift and Yield Mechanism”. Report No. UMCEE 0117, Department of Civil and nvironmental Engineering, University of Michigan, Ann Arbor. ##[12] Mohammadi, RK., ElNaggar, MH., Moghaddam, H. (2004). “Optimum Strength Distribution for Seismic Resistant Shear Buildings”. International Journal of Solids and Structures, 41: 6597–6612. ##[13] Ganjavi, B., Vaseghi Amiri, J., Ghodrati Amiri, G., Yahyazadeh Ahmadi, Q. (2008). “Distribution of drift, hysteretic energy and damage in reinforced concrete buildings with uniform strength ratio”. The 14th World Conference on Earthquake Engineering, Beijing, China. ##[14] Hajirasouliha, I. and Moghaddam, H. (2009). “New lateral force distribution for seismic design of structures”. Journal of Structural Engineering (ASCE) 135(8): 906–915. ##[15] Goel, SC., Liao, WC., Bayat, MR., and Chao, SH. (2010). “PerformanceBased Plastic Design (PBPD) Method for EarthquakeResistant Structures: An Overview”. Structural Design of Tall Special Buildings, 19: 115137. ##[16] Mohammadi, RK., Ghasemof, A. (2015). “PerformanceBased Design Optimization Using Uniform Deformation Theory: A Comparison Study”. Latin American of Solids and Structures, Vol. 12. ##[17] Ganjavi, B. and Hao, H., (2013). “Optimum lateral load pattern for elastic seismic design of buildings incorporating soil structure interaction effects”. Earthquake Engineering and Structural Dynamics, 42(6):913933. ##[18] Ganjavi, B., (2015). “Optimal Structural Weight for FlexibleBase Buildings under Strong Ground Motion Excitations”. Asian Journal of Civil Engineering, (In press) ##[19] Khatib, IF., Mahin, SA., Pister, KS. (1988). “SeismicBehavior of Concentrically Braced Steel Frames, Earthquake Engineering Research Center”. Report no. UCB/EERC88/01 University of California, Berkeley. ##[20] Tremblay, R., Tirca, L. (2003). “Behavior and Design of MultiStory Zipper Concentrically Braced Steel Frames for the Mitigation of SoftStory Response”. in: Proceedings of the Conference on Behavior of Steel Structures in Seismic Areas, pp. 471–7. ##[21] Yang, C., Leon, R., DesRoches, R. (2008). “Design and Behavior of ZipperBraced Frames”. Engineering Structures, 30, pp. 10921100. ##[22] Sabelli, R. (2001). “Research on Improving the Design and Analysis of Earthquake Resistant SteelBraced Frames”. Earthquake Engineering Research Institute, NEHRP Fellowship Report No. PF20009. Oakland, California. ##[23] ASCE710 (2010). “Minimum Design Loads for Buildings and Other Structures”. American Society of Civil Engineers: Reston, VA. ##[24] American Institute of Steel Construction (2005). “Seismic Provisions for Structural Steel Buildings”. AISC Seismic, Chicago. ##[25] Mazzoni, S., McKenna, F., Scott, MH., Fenves, GL. (2014). “OpenSEES Command Language Manual, Pacific Earthquake Engineering Research Center”. http://opensees.berkeley.edu. ##[26] Uriz, P., Mahin, S. (2004). “Summary of test results for UC Berkeley special concentric braced frame specimen No. 1 (SCBF1)”. ##[27] SeismoMatch (2014). “A computer program for adjusting earthquake records to match a specific target response spectrum”. Available from: http://www.seismosoft.com. ##[28] Chandler, A.M., Lam, T.K. (2001). “Performancebased design in earthquake engineering: a multidisciplinary review”. Engineering Structures, No. 23, pp.15251543.##]
Determining the Relative Importance of Parameters Affecting Concrete Pavement Thickness
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2
Spending costs in construction of road pavements has turned this subject into one of the significant points in transportation infrastructure of countries. Concrete slabs consider as a paving method with ability of reducing the rehabilitation needs. Therefore, to manage costs and optimize the thickness of concrete pavements, recognizing the amount of determinative factors’ influence will be required. A study with the aim of determining the influence of traffic parameters, type of subgrade soil and the base layer thickness on the concrete pavement slab thickness can provide the choice of best concrete pavement design. For this purpose, the PCASE software has been used in this paper to construct sufficient number of numerical examples, 288 specimens, with taking into account the number of equivalent single axle, the subgrade modulus of concrete pavement construction place and the base layer thickness. These samples are considered as the basis of training and testing an artificial neural network and the level of pavement design parameters importance is relatively determined on the results of optimal neural network. The method used in this paper for calculating the relative importance of each parameter involved in the concrete pavement thickness indicates that the parameters of base layer thickness and the number of equivalent single axle have the lowest and highest level of influence, with the values of about 21 and 42 percent, respectively. The obtained results are also compatible with concepts and structural features of concrete pavements.
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61
73


Golamali
Shafabakhsh
Associate Professor, Department of Civil Engineering, Semnan University, Semnan, Iran
Associate Professor, Department of Civil
Iran
shafabakhsh@semnan.ac.ir


Hossein
Naderpour
Assistant Professor, Department of Civil Engineering, Semnan University, Semnan, Iran
Assistant Professor, Department of Civil
Iran
naderpour@semnan.ac.ir


Reza
Noroozi
Ph.D. student, Department of Civil Engineering, Semnan University, Semnan, Iran
Ph.D. student, Department of Civil Engineering,
Iran
rno470@gmail.com
Concrete pavement
pavement design
PCASE
neural network
relative importance
[[1] Fogg, J. A., Baus, R. L., Ray, R. P. (1991). “AASHTO rigid pavement design equation study”, J. Transp. Eng., vol. 117, no. 1, pp. 124–131. ##[2] Guclu, A., Ceylan, H. (2005). “Sensitivity Analysis of Rigid Pavement Systems Using MechanisticEmpirical Pavement Design Guide”, in Proceedings of the 2005 MidContinent Transportation Research Symposium, Ames, Iowa. ##[3] Southgate, H. F. (1988). “Comparison of rigid pavement thickness design systems”, Research Report UKTRP8814, Lexington, Kentuchy. ##[4] Jersey, S. R., Bell, H. P. (2011). “Analyses of Structural Capacity of Rigid Airfield Pavement Using Portable Seismic Technology”, Int. J. Pavement Res. Technol., vol. 4, no. 3, pp. 147–153. ##[5] Taghavi Esfandani, M., Mansourian, A., Babaei, A. (2013). “Investigation of Runway Pavement Design Software and Determination of Optimization Software”, J. Basic Appl. Sci. Res., vol. 3, no. 4, pp. 143–150. ##[6] Kannekanti, V., Harvey, J. (2006). “Sensitivity analysis of 2002 design guide distress prediction models for jointed plain concrete pavement”, Transp. Res. Rec. J. Transp. Res. Board, vol. 1947, no. 1, pp. 91–100. ##[7] Hall, K. D., Beam, S. (2005). “Estimating the sensitivity of design input variables for rigid pavement analysis with a mechanisticempirical design guide”, Transp. Res. Rec. J. Transp. Res. Board, vol. 1919, no. 1, pp. 65–73. ##[8] Khazanovich, L., Darter, M. I., Yu, H. T. (2004). “Mechanisticempirical model to predict transverse joint faulting”, Transp. Res. Rec. J. Transp. Res. Board, vol. 1896, no. 1, pp. 34–45. ##[9] Mallela, J., Abbas, A., Harman, T., Rao, C., Liu, R., Darter, M. I. (2005). “Measurement and significance of the coefficient of thermal expansion of concrete in rigid pavement design”, Transp. Res. Rec. J. Transp. Res. Board, vol. 1919, no. 1, pp. 38–46. ##[10] AttohOkine, N. O., Cooger, K., Mensah, S. (2009). “Multivariate adaptive regression (MARS) and hinged hyperplanes (HHP) for doweled pavement performance modeling”, Constr. Build. Mater., vol. 23, no. 9, pp. 3020–3023. ##[11] Bayrak, M. B., Ceylan, H. (2008). “Neural networkbased approach for analysis of rigid pavement systems using deflection data”, Transp. Res. Rec. J. Transp. Res. Board, vol. 2068, no. 1, pp. 61–70. ##[12] Ceylan, H., Gopalakrishnan, K., Lytton, R. L. (2010). “Neural networks modeling of stress growth in asphalt overlays due to load and thermal effects during reflection cracking”, J. Mater. Civ. Eng., vol. 23, no. 3, pp. 221–229. ##[13] Ceylan, H., Gopalakrishnan, K. (2007). “Neural Networks Based Models for MechanisticEmpirical Design of Rubblized Concrete Pavements”, Geotech. Spec. Publ. No. 169, Soil Mater. Inputs Mech. Pavement Des. ASCE, pp. 1–10. ##[14] Gopalakrishnan, K. (2010). “Effect of training algorithms on neural networks aided pavement diagnosis”, Int. J. Eng. Sci. Technol., vol. 2, no. 2, pp. 83–92. ##[15] Sharma, S., Das, A. (2008). “Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network”, Can. J. Civ. Eng., vol. 35, no. 1, pp. 57–66. ##[16] Kisi, O. (2005). “Daily river flow forecasting using artificial neural networks and autoregressive models”, Turkish J. Eng. Environ. Sci., vol. 29, no. 1, pp. 9–20. ##[17] Olden, J., Jackson, D. (2002). “Illuminating the ‘black box’: a randomization approach for understanding variable contributions in artificial neural networks”, Ecol. Modell., vol. 154, no. 1–2, pp. 135–150. ##[18] Dimopoulos, Y., Bourret, P., Lek, S. (1995). “Use of some sensitivity criteria for choosing networks with good generalization ability”, Neural Process. Lett., vol. 2, no. 6, pp. 1–4. ##[19] Gevrey, M., Dimopoulos, I., Lek, S. (2003). “Review and comparison of methods to study the contribution of variables in artificial neural network models”, Ecol. Modell., vol. 160, no. 3, pp. 249–264. ##[20] Scardi, M., Harding Jr, L. W. (1999). “Developing an empirical model of phytoplankton primary production: a neural network case study”, Ecol. Modell., vol. 120, no. 2, pp. 213–223. ##[21] Garson, G. D. (1991). “Interpreting neuralnetwork connection weights”, AI Expert, vol. 6, no. 4, pp. 46–51. ##[22] Flood, I., Kartam, N. (1994). “Neural networks in civil engineering. I: Principles and understanding”, J. Comput. Civ. Eng., vol. 8, no. 2, pp. 131–148, 1994. ##[23] Adeli, H. (2001). “Neural Networks in Civil Engineering: 19892000”, Comput. Civ. Infrastruct. Eng., vol. 16, no. 2, pp. 126–142, Mar. 2001. ##[24] McCulloch, W. S., Pitts, W. (1943). “A logical calculus of the ideas immanent in nervous activity”, Bull. Math. Biophys., vol. 5, no. 4, pp. 115–133, 1943. ##[25] Chen, D. G., Ware, D. M. (1999). “A neural network model for forecasting fish stock recruitment”, Can. J. Fish. Aquat. Sci., vol. 56, no. 12, pp. 2385–2396. ##[26] Manel, S. S. , Dias, J.M., Ormerod, S. J. (1999). “Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird”, Ecol. Modell., vol. 120, no. 2, pp. 337–347. ##[27] Özesmi, S. L., Özesmi, U. (1999). “An artificial neural network approach to spatial habitat modelling with interspecific interaction”, Ecol. Modell., vol. 116, no. 1, pp. 15–31. ##[28] Paruelo, J., Tomasel, F. (1997). “Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models”, Ecol. Modell., vol. 98, no. 2, pp. 173–186. ##[29] Spitz, F., Lek, S. (1999). “Environmental impact prediction using neural network modelling. An example in wildlife damage”, J. Appl. Ecol., vol. 36, no. 2, pp. 317–326. ##[30] Mural, R. V., Puri, A. B., Prabhakaran, G. (2010). “Artificial neural networks based predictive model for worker assignment into virtual cells”, Int. J. Eng. Sci. Technol., vol. 2, no. 1, pp. 163–174. ##[31] Haykin, S. (1999). “Neural networks: a comprehensive foundation 2nd edition”, Up. Saddle River, NJ, US Prentice Hall. ##[32] Melesse, A. M., Hanley, R. S. (2005). “Artificial neural network application for multiecosystem carbon flux simulation”, Ecol. Modell., vol. 189, no. 3, pp. 305–314. ##[33] Bilgili, M., Sahin, B., Yasar, A. (2007). “Application of artificial neural networks for the wind speed prediction of target station using reference stations data”, Renew. Energy, vol. 32, no. 14, pp. 2350–2360. ##[34] Bishop, C. M. (1995). "Neural networks for pattern recognition", vol. 92, no. 440. Clarendon press Oxford, p. 498. ##[35] Dimopoulos, I., Chronopoulos, J., ChronopoulouSereli, A., Lek, S. (1999). “Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece)”, Ecol. Modell., vol. 120, no. 2, pp. 157–165. ##[36] Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., Aulagnier, S. (1996). “Application of neural networks to modelling nonlinear relationships in ecology”, Ecol. Modell., vol. 90, no. 1, pp. 39–52. ##[37] Abrahart, R. J., See, L., Kneale, P. E. (2001). “Investigating the role of saliency analysis with a neural network rainfallrunoff model”, Comput. Geosci., vol. 27, no. 8, pp. 921–928. ##[38] Makarynskyy, O., Makarynska, D., Kuhn, M., Featherstone, W. E. (2004). “Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia”, Estuar. Coast. Shelf Sci., vol. 61, no. 2, pp. 351–360. ##[39] Montano, J., Palmer, A. (2003). “Numeric sensitivity analysis applied to feedforward neural networks”, Neural Comput. Appl., vol. 12, no. 2, pp. 119–125. ##[40] Elmolla, E. S., Chaudhuri, M., Eltoukhy, M. M. (2010). “The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process”, J. Hazard. Mater., vol. 179, no. 1, pp. 127–134.##]
Fatigue Behavior Analysis of Asphalt Mixes Containing Electric Arc Furnace (EAF) Steel Slag
2
2
This research was conducted in order to evaluate fatigue behavior of asphalt mixes containing Electric Arc Furnace (EAF) steel slag. After initial evaluation of the properties of EAF steel slag using Xray Diffraction (XRD) and Scanning Electric Microscope (SEM), six sets of laboratory mixtures were prepared. Each set were treated replacing various portions of limestone aggregates of the mix with EAF steel slag. Four point bending beam fatigue tests were performed in both controlled strain and stress mode of loading at various strain and stress levels to characterize the fatigue behavior of asphalt mixes containing different percentages of EAF slag. Different approaches based on stiffness and dissipated energy were used to analyze the fatigue tests data. The results show that the inclusion of EAF in mixes improved the fatigue life considerably under both stress and strain control mode of loading. In the stress control mode, very good correlations were observed between responses and fatigue life of mixes. However, correlation coefficients in the strain control mode were relatively lower than those in the stress control mode (particularly in the tests that were based on 50% reduction of initial stiffness).
1

74
86


Amir
Kavussi
Associate Professor, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
Associate Professor, Faculty of Civil and
Iran
kavussia@modares.ac.ir


Morteza
Jalili Qazizadeh
Assistant Professor, Faculty of Engineering, Quchan University of Advanced Technology, Quchan, Iran
Assistant Professor, Faculty of Engineering,
Iran
m.jalili@qiet.ac.ir


Abolfazl
Hassani
Professor, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
Professor, Faculty of Civil and Environmental
Iran
hassani@modares.ac.ir
EAF slag
Fatigue
Strain and stress control
Fatigue model
[[1] Yongjie, X., Shaopeng, W., Haobo, H., Jin, Z. (2006). “Experimental investigation of basic oxygen furnace slag used as aggregate in asphalt mixture”. Journal of Hazardous Materials, Vol. 138, pp. 261–268. ##[2] Shaopeng, W., Yongjie, X., Qunshan, Y. (2007). “Utilization of steel slag as aggregates for stone mastic asphalt (SMA) mixtures”. Building and Environment, Vol. 42, pp. 2580–2585. ##[3] Sofilić, T., RastovčanMioč, A., Ćosić, M., Merle, V., Mioč, B., Sofilić, U. (2010). “EAF steel slag application posibilities in croatian asphalt mixture production”. Chemical Engineering Transactions, Vol. 19, 17. ##[4] Waligora, J., Bulteel, D., Degrugilliers, P., Damidot, D. (2010). “Chemical and mineralogical characterizations of LD converter steel slags: A multianalytical technique approach”. Materials Characterization, Vol. 61, pp. 39 – 48. ##[5] Ziari, H., Khabiri, MM. (2007). “Preventive Maintenance of Flexible Pavement and Mechanical Properties of Steel Slag Asphalt”. Journal of Environmental Engineering and Landscape Management, Vol. 15, pp. 188–192. ##[6] Kavussi, A., Modarres, A. (2010). “Laboratory fatigue models for recycled mixes with bitumen emulsion and cement”. Construction and Building Materials, Vol. 24, pp. 1920–1927. ##[7] You, Z., Buttlar, W. (2004). “Discrete Element Modeling to Predict the Modulus of Asphalt Concrete Mixtures”. Journal of Materials in Civil Engineering, Vol. 16, Special Issue: Micromechanical Characterization And Constitutive Modeling Of Asphalt Mixes, pp. 140–146. ##[8] Bazin, P., Saunier, J.B. (1967). “Deformability, Fatigue and Healing Properties of Asphalt Mixes”. Second International Conference on The Structural Design of Asphalt Pavements Proc. Ann Arbor, Michigan. ##[9] Moreno, F., Rubio, M.C. (2013). “Effect of aggregate nature on the fatiguecracking behavior of asphalt mixes”. Materials and Design, Vol. 47, pp 61–67. ##[10] Bagamapadde, U., Wahhab, Aiban, S.A. (1998). “Optimization of Steel Slag Aggregate for Bituminous Mixes in Saudi Arab”, Journal of Materials in Civil Engineering, Vol. 11, pp. 3035. ##[11] Airey, G.D., Collop, A.C., Thom, N.H. (2004). “Mechanical Performance of Asphalt Mixtures Incorporating Slag and Glass Secondary Aggregates”. Proceedings of the 8th Conference on Asphalt Pavements for Southern Africa (CAPSA'04) Sun City, South Africa. ##[12] Asi, I.M., Qasrawi, H.Y., Shalabi, F.I. (2007). “Use of Steel Slag Aggregate In Asphalt ConcreteMixes”. Canadian Journal of Civil Engineering, Vol. 34, pp. 902–911. ##[13] Pasetto, M., Baldo, N. (2010). “Experimental evaluation of high performance base course and road base asphalt concrete with electric arc furnace steel slags”. Journal of Hazardous Materials, Vol. 181, pp. 938–948. ##[14] Pasetto, M., Baldo, N. (2011). “Mix design and performance analysis of asphalt concretes with electric arc furnace slag”. Construction and Building Materials, Vol. 25, pp. 3458–3468. ##[15] Arabani, M., Azarhoosh, A.R. (2012). “The Effect of Recycled Concrete Aggregate and Steel Slag on the Dynamic Properties of Asphalt Mixtures”. Construction and Building Materials, Vol. 35, pp. 1–7. ##[16] Mamlouk, M., Souliman, M., Zeiada, W. (2012). “Optimum Testing Conditions To Measure HMA Fatigue And Healing Using Flexural Bending Test”. TRB annual meeting. ## [17] Rowe, GM., Bouldin, MG. (2000). “Improved Techniques to Evaluate the Fatigue Resistance of Asphaltic Mixtures”. Proceedings of 2nd Euroasphalt and Eurobitumen Congress, Barcelona, Spain. ##[18] Pronk, AC., Hopman, PC. (1990). “Energy Dissipation: The Leading Factor of Fatigue”. Highway Research: Sharing the Benefits, Proceedings of the Conference the United States Strategic Highway Research Program. London. ##[19] Ghuzlan, KA., Carpenter, SH. (2006). “Fatigue damage analysis in asphalt concrete mixtures using the dissipated energy approach”. Canadian Journal of Civil Engineering, Vol. 33, pp. 890–901. ##[20] Ghuzlan, KA., Carpenter, SH. (2000). “Energyderived, damagebased failure criterion for fatigue testing”. Transportation Research Records, vol. 1723, pp. 141–149.##]
StrutandTie Method for Prediction of Ultimate Shear Capacity of ShearStrengthened RC deep beams with FRP
2
2
The main objective of this study is to propose the StrutandTie method (STM) to predict the shear capacity of simply supported RC deep beams shearstrengthened with carbon fiber reinforced polymers (CFRP). It is assumed that, the total carried shear force by shearstrengthened RC deep beam provided by three independent resistance, namely diagonal concrete strut due to Strutandtie mechanism, and the equivalent resisting force resulted by with web reinforcement and FRP layer. The STM approach is regressioned with 104 specimens shearstrengthened with different scheme which are modelled and analyzed through the Non Linear finite elements method and analyzed according under Push over load. For verifying of the accuracy of proposed method, it was used to determine the shear capacity of specimens which have been tested by other researchers. Obtained results were compared with experimental data, that this comparison indicate the proposed method is capable to predict the shear strength of strengthened deep beams with externally bonded (EB) CFRP with acceptable accuracy.
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87
104


Masumeh
Bahrami
M.Sc, Structural Engineering, Razi University, Kermanshah, Iran
M.Sc, Structural Engineering, Razi University,
Iran
masumehbahrami@yahoo.com


Reza
Aghayari
Assistant Professor, Structural Engineering, Razi University, Kermanshah, Iran
Assistant Professor, Structural Engineering,
Iran
reza_agh@razi.ac.ir
Shear strengthening CFRP
Strutandtie
deep beams
[[1] ACI 31802. (2002) “Building Code Requirements for Structural Concrete and Commentary”, American Concrete Institute ##[2] ACI 31814. (2014) “Building Code Requirements for Structural Concrete and Commentary”, American Concrete Institute ##[3] ACI 440.2R08. (2008), “Guide for the Design and Construction of Externally Bonded FRP Systems for Strengthening Concrete Structures”, American Concrete Institute ##[4] Adhikary, B.B., Mutsuyoshi, H. and Ashraf, M. (2004), “Shear Strengthening of Reinforced Concrete Beams Using FiberReinforced Polymer Sheets with Bonded Anchorage”, ACI STRUCTURAL JOURNAL, V101, 660668 ##[5] Arabzadeh, A., Rahaie, A. R., Aghayari, R. (2009), “A Simple StrutandTie Model for Prediction of Ultimate Shear Strength of RC Deep Beams”, International Journal of Civil Engineering, Vol. 7, No. 3, 141153 ##[6] Arabzadeh, A., Mirzaei, M. (2008), “Evaluation of shear strength of deep beams that strength and repair with CFRP sheets”, Eighth International Congress of Civil Engineering, Shiraz university, (Persian). ##[7] Brown, M., and Bayrak, O. (2007), “Minimume transvers reinforcement for bottle – shaped strut”, ACI STRUCTURAL JOURNAL, Vol.103, No.6, 813821. ##[8] Chaallal, O., Shahawy, M. and Hassan, M. (2006), “Behavior of Reinforced Concrete TBeams Strengthened in Shear with Carbon FiberReinforced Polymer— An Experimental Study”, ACI STRUCTURAL JOURNAL, V103, 339347. ##[9] Eom, T.S., Park, H.G. (2010), “Secant Stiffness Method for Inelastic Design of StrutandTie Model”, ACI STRUCTURAL JOURNAL, V107, 689698. ## [10] Foster, S.J, and Gilbert, R.I. (1998), “Experimental Studies on High –Strength Concrete Deep beam”, ACI STRUCTURAL JOURNAL, V. 95, No. 4,382390. ##[11] Foster, S.J., and Malik, A.R., 2002, Evaluation of Efficiency Factor Models used in StrutandTie Modeling of Nonflexural Members, ASCE Journal of Structural Engineering, Vol. 128, No. 5, 569577. ##[12] Gaetano, R., Raffaele, V., and Margherita, P. (2005), “Reinforced Concrete Deep Beams—Shear Strength Model and Design Formula”, ACI STRUCTURAL JOURNAL, V. 102, No. 3, 429437. ##[13] Godat, A. and Chaallal, O. (2013), “Strut and Tie Method for externally FRP Shear Strengthened Large scale RC Beams”, Composite Structural, 99, 327338. ##[14] Hwang, S., Lu, W., and Lee, H. (2000), “Shear Strength Prediction for Deep Beams”, ACI STRUCTURAL JOURNAL, Vol. 97, No. 3, 367376 ##[15] Kachlakev, D.I. and Barnes, W.A. (1999), “Flexural and Shear Performance of Concrete Beams Strengthened with Fiber Reinforced Polymer Laminates”, ACI STRUCTURAL JOURNAL, V188, 959972. ##[16] Khalifa, A., Tumialan, G., Nanni, A. and Belarbi, A. (1999), “Shear Strengthening of Continuous Reinforced Concrete Beams Using Externally Bonded Carbon Fiber Reinforced Polymer Sheets”, ACI STRUCTURAL JOURNAL, SP188, 9951008. ##[17] Lu, W., Lin, I. and Yu, Hsin. (2013), “Shear Strength of Reinforced Concrete Deep Beams”, ACI STRUCTURAL JOURNAL, V110, 671680. ##[18] Maaddavwy, T.EI. and Sherif, S. (2009), “FRP composites for shear strengthening of reinforced concrete deep beams with opening”, Compos Struct J, 89, 6069. ##[19] Park, J.W. and Kuchma, D. (2007) “StrutandTie Model Analysis for Strength Prediction of Deep Beams”, ACI STRUCTURAL JOURNAL, Vol.105, No. 6, 657666. ##[20] Shin, S., Lee, K., Moon, J., and Ghosh, S. K. (1999) “Shear Strength of Reinforced HighStrength Concrete Beams with Shear SpantoDepth Ratios between 1/5 and 2/5”, ACI Structural Journal, V.96, NO.4, 549556. ##[21] Sherif H. AlTersawy. (2013), “Effect of fiber parameters and concrete strength on shear behavior of strengthened RC beams”, Construction and Building Material, V44, 1524. ##[22] Sim, J., Kim, G., Park, C., Ju, M. (2005), “Shear Strengthening Effects with Varying Types of FRP Materials and Strengthening Methods”, ACI STRUCTURAL JOURNAL, V230, 16651680. ##[23] Simon, C., Ianniruberto, U. and Rinaldi, Z. (2010), “Redistribution of Bending Moment in Continuous Reinforced Concrete Beams Strengthened with FiberReinforced Polymer”, ACI STRUCTURAL JOURNAL, V105, 318326. ##[24] Yungon, K., Kevin, Q., Wassim M. G. and James O. J. (2011), “Shear Strengthening RC Tbeams Using CFRP Laminates and Anchors”, ACI STRUCTURAL JOURNAL, V111, 10271036.##]