2017
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1
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98
Advanced ANN Prediction of Moisture Damage in CNT Modified Asphalt Binder
2
2
Moisture penetration causes many direct and indirect distresses in flexible asphalt pavement. Due to damage in asphalt concrete and binder by moisture are the prime concern of failure for flexible pavement worldwide. The causes and prediction are investigated in this study. The asphalt binder was modified with carbon nanotubes (CNT) with very small percentages. The modified binder was simulated with moisture damage with AASHTO T283 methods. In this study, polymer and carbon nanotubes (CNT) have been added to liquid asphalt binder to examine whether the resulting modified binder has improved moisture damage resistance. Using laboratory tested data, an artificial intelligence modeling technique has been utilized to determine the moisture damage behavior of the modified binder. MultiLayer Perceptron (MLP) provides the best prediction for wet and dry samples AFM readings with R2 values respectively 0.6407 and 0.8371.
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11


Md
Arifuzzaman
Assistant Professor, Department of Civil Engineering, University of Bahrain, Bahrain
Assistant Professor, Department of Civil
Bahrain
arafiquzzaman@uob.edu.bh
Fuzzy System
Artificial Neural Network
Atomic force microscopy
Adhesion forces
Functionalized tips
Moisture
Damage model
Comparison Study of Soft Computing Approaches for Estimation of the NonDuctile RC Joint Shear Strength
2
2
Today, retrofitting of the old structures is important. For this purpose, determination of capacities for these buildings, which mostly are nonductile is a very useful tool. In this context, nonductile RC joint in concrete structures, as one of the most important elements in these buildings are considered and the shear capacity, especially for retrofitting goals can be very beneficial. In this paper, three famous soft computing methods including artificial neural networks (ANN), adaptive neurofuzzy inference system (ANFIS) and also group method of data handling (GMDH) were used to estimating the shear capacity for this type of RC joints. A set of experimental data which were a failure in joint are collected and first, the effective parameters were identified. Based on these parameters, predictive models are presented in detail and compare with each other. The results showed that the considered soft computing techniques are very good capabilities to determine the shear capacity.
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28


Masoomeh
Mirrashid
Faculty of Civil Engineering, Semnan University, Semnan, Iran
Faculty of Civil Engineering, Semnan University,
Iran
m.mirrashid@semnan.ac.ir
ANFIS
RC joint
Shear strength
Soft Computing
Neural Networks
NonDuctile
[[1] F. G. McLean and J. S. Pierce, "Comparison of joint shear strengths for conventional and roller compacted concrete," in Roller Compacted Concrete II:, 1988, pp. 151169.##[2] S. Hitoshi, "Analysis of Joint Shear Failure Of HighStrength Reinforced Concrete Interior BeamToColumn Joint," Journal High Strength Concrete. ASCE, pp. 114, 1998.##[3] A. Ghobarah and A. Biddah, "Dynamic analysis of reinforced concrete frames including joint shear deformation," Engineering Structures, vol. 21, pp. 971987, 1999.##[4] P. Bakir and H. Boduroğlu, "A new design equation for predicting the joint shear strength of monotonically loaded exterior beamcolumn joints," Engineering Structures, vol. 24, pp. 11051117, 2002.##[5] A. A. Sayed, "General Analytical Model for Nominal Shear Stress of Type 2 Normaland HighStrength Concrete BeamColumn Joint," ACI Structural Journal, vol. 101, pp. 6575, 2004.##[6] J. Kim and J. M. LaFave, "Key influence parameters for the joint shear behaviour of reinforced concrete (RC) beam–column connections," Engineering Structures, vol. 29, pp. 25232539, 2007.##[7] J. Kim and J. M. LaFave, "Probabilistic joint shear strength models for design of RC beamcolumn connections," ACI Structural Journal, vol. 105, p. 770, 2008.##[8] J. Saravanan and G. Kumaran, "Joint shear strength of FRP reinforced concrete beamcolumn joints," Central European Journal of Engineering, vol. 1, pp. 89102, 2011.##[9] A. Sharma, R. Eligehausen, and G. Reddy, "A new model to simulate joint shear behavior of poorly detailed beam–column connections in RC structures under seismic loads, Part I: Exterior joints," Engineering Structures, vol. 33, pp. 10341051, 2011.##[10] C. Lee, Y. Kim, W. Chin, and E. Choi, "Shear strength of ultrahigh performance fiber reinforced concrete (UHPFRC) precast bridge joint," in High Performance Fiber Reinforced Cement Composites 6, ed: Springer, 2012, pp. 413420.##[11] L. V. Pradeesh, S. Sasmal, K. Devi, and K. Ramanjaneyulu, "Evaluation of Models for Joint Shear Strength of Beam–Column Subassemblages for Seismic Resistance," in Advances in Structural Engineering, ed: Springer, 2015, pp. 885896.##[12] T. M. Elshafiey, A. M. Atta, H. M. Afefy, and M. E. Ellithy, "Structural performance of reinforced concrete exterior beam–column joint subjected to combined shear and torsion," Advances in Structural Engineering, vol. 19, pp. 327340, 2016.##[13] K. Jin, K. Kitayama, S. Song, and K.o. Kanemoto, "Shear Capacity of Precast Prestressed Concrete BeamColumn Joint Assembled by Unbonded Tendon," ACI Structural Journal, vol. 114, p. 51, 2017.##[14] J.S. Jang, "ANFIS: adaptivenetworkbased fuzzy inference system," IEEE transactions on systems, man, and cybernetics, vol. 23, pp. 665685, 1993.##[15] A. Ivakhnenko, "Polynomial theory of complex systems," IEEE Transactions on Systems, Man, and Cybernetics, pp. 364378, 1971.##[16] U. Akguzel, "Seismic performance of FRP retrofitted exterior RC beamcolumn joints under varying axial and bidirectional loading," 2011.##[17] Y. A. AlSalloum, N. A. Siddiqui, H. M. Elsanadedy, A. A. Abadel, and M. A. Aqel, "Textilereinforced mortar versus FRP as strengthening material for seismically deficient RC beamcolumn joints," Journal of Composites for Construction, vol. 15, pp. 920933, 2011.##[18] C. P. Antonopoulos and T. C. Triantafillou, "Experimental investigation of FRPstrengthened RC beamcolumn joints," Journal of composites for construction, vol. 7, pp. 3949, 2003.##[19] T.H. Chen, "Retrofit strategy of nonseismically designed frame systems based on a metallic haunch system," 2006.##[20] C. Clyde, C. P. Pantelides, and L. D. Reaveley, Performancebased evaluation of exterior reinforced concrete building joints for seismic excitation: Pacific Earthquake Engineering Research Center, College of Engineering, University of California, Berkeley, 2000.##[21] R. De Otiz, "Strutandtie modelling of reinforced concrete: short beams and beamcolumn joints," University of Westminster, 1993.##[22] R. P. Dhakal, T.C. Pan, P. Irawan, K.C. Tsai, K.C. Lin, and C.H. Chen, "Experimental study on the dynamic response of gravitydesigned reinforced concrete connections," Engineering Structures, vol. 27, pp. 7587, 2005.##[23] M. Engindeniz, Repair and strengthening of pre1970 reinforced concrete corner beamcolumn joints using CFRP composites: Georgia Institute of Technology, 2008.##[24] Y. Goto and O. Joh, "An experimental study of shear failure mechanism of RC interior beamcolumn joints," in 11 th World Conference on Earthquake Engineering, 1996.##[25] S. J. Hamil, "Reinforced concrete beamcolumn connection behaviour," Ph.D. Thesis, , Durham University, UK, 2000.##[26] W. M. Hassan, Analytical and experimental assessment of seismic vulnerability of beamcolumn joints without transverse reinforcement in concrete buildings: University of California, Berkeley 2011.##[27] A. Ilki, I. Bedirhanoglu, and N. Kumbasar, "Behavior of FRPretrofitted joints built with plain bars and lowstrength concrete," Journal of Composites for Construction, vol. 15, pp. 312326, 2010.##[28] C. Karayannis, C. Chalioris, and K. Sideris, "Effectiveness of RC beamcolumn connection repair using epoxy resin injections," Journal of Earthquake Engineering, vol. 2, pp. 217240, 1998.##[29] W.T. Lee, Y.J. Chiou, and M. Shih, "Reinforced concrete beam–column joint strengthened with carbon fiber reinforced polymer," Composite Structures, vol. 92, pp. 4860, 2010.##[30] B. Li, Y. Wu, and T.C. Pan, "Seismic behavior of nonseismically detailed interior beamwide column joints—Part II: Theoretical comparisons and analytical studies," ACI Structural Journal, vol. 99, pp. 791802, 2003.##[31] B. Oh, K. Park, H. Hwang, and H. Chung, "An experimental study on shear capacity of reinforced concrete exterior beamcolumn joint with high strength concrete," Proceedings of the Architecture Institute of Korea, vol. 12, pp. 363366, 1992.##[32] Y. Ohwada, "A study on RC beamcolumn connection subjected to lateral load (8)," in (in Japanese), Summaries of Technical Papers of Annual Meeting Architectural Institute of Japan, 1970, pp. 737738.##[33] Y. Ohwada, "A study on RC beamcolumn connection subjected to lateral load (9)," in (in Japanese), Summaries of Technical Papers of Annual Meeting Architectural Institute of Japan, 1973, pp. 12971298.##[34] Y. Ohwada, "A study on effect of lateral beams on RC beamcolumn joints (1) " (in Japanese), Summaries of Technical Papers of Annual Meeting Architectural Institute of Japan, pp. 14551456, 1976.##[35] Y. Ohwada, "A study on effect of lateral beams on RC beamcolumn joints (2) " (in Japanese), Proceedings of the Architectural Institute of Japan, vol. 61, pp. 241244, 1977.##[36] Y. Ohwada, "A study on effect of lateral beams on RC beamcolumn joints (4)," Summaries of Technical Papers of Annual Meeting Architectural Institute of Japan, pp. 15111512, 1980.##[37] C. P. Pantelides, J. Hansen, J. Nadauld, and L. D. Reaveley, "Assessment of reinforced concrete building exterior joints with substandard details," PEER report, vol. 18, 2002.##[38] S. Park and K. M. Mosalam, "Experimental investigation of nonductile RC corner beamcolumn joints with floor slabs," Journal of Structural Engineering, vol. 139, pp. 114, 2012.##[39] D. E. Parker and P. Bullman, "Shear strength within reinforced concrete beamcolumn joints," Structural Engineer, vol. 75, 1997.##[40] A. Pimanmas and P. Chaimahawan, "Shear strength of beam–column joint with enlarged joint area," Engineering structures, vol. 32, pp. 25292545, 2010.##[41] I. B. Salim, "The influence of concrete strengths on the behaviour of external beamcolumn joints ", MS Thesis, Universiti Teknologi Malaysia, 2007.##[42] R. Scott, "The effects of detailing on RC beam/column connection behaviour," Structural Engineer, vol. 70, 1992.##[43] T. Supaviriyakit and A. Pimanmas, "Comparative performance of substandard interior reinforced concrete beam–column connection with various joint reinforcing details," Materials and Structures, vol. 41, pp. 543557, 2008.##[44] H. Taylor, "The behavior of in situ concrete beamcolumn joints," 1974.##[45] A. G. Tsonos, "Cyclic load behavior of reinforced concrete beamcolumn subassemblages of modern structures," ACI Structural journal, vol. 104, p. 468, 2007.##[46] A. G. Tsonos, "Effectiveness of CFRPjackets and RCjackets in postearthquake and preearthquake retrofitting of beam–column subassemblages," Engineering Structures, vol. 30, pp. 777793, 2008.##[47] A. G. Tsonos and K. V. Papanikolaou, "Postearthquake repair and strengthening of reinforced concrete beamcolumn connections (theoretical & experimental investigation)," BulletinNew Zealand society for earthquake engineering, vol. 36, pp. 7393, 2003.##[48] Y.C. Wang and K. Hsu, "Shear strength of RC jacketed interior beamcolumn joints without horizontal shear reinforcement," ACI Structural Journal, vol. 106, p. 222, 2009.##[49] Y.C. Wang and M.G. Lee, "Rehabilitation of nonductile beamcolumn joint using concrete jacketing," in A paper presentation at 13th world conference on earthquake engineering, Vancouver (BC, Canada), 2004.##[50] H. F. Wong, "Shear strength and seismic performance of nonseismically designed reinforced concrete beamcolumn joints," Ph.D. Thesis, Department of Civil Engineering, The Hong Kong University of Science and Technology, 2005.##]
NoDeposition Sediment Transport in Sewers Using Gene Expression Programming
2
2
The deposition of flow suspended particles has always been a problematic case in the process of flow transmission through sewers. Deposition of suspended materials decreases transmitting capacity. Therefore, it is necessary to have a method capable of precisely evaluating the flow velocity in order to prevent deposition. In this paper, using GeneExpression Programming, a model is presented which properly predicts sediment transport in sewer. In order to present GeneExpression Programming model, firstly parameters which are effective on velocity are surveyed and considering each and every of them, six different models are presented. Among the presented models the best is being selected. The results show that using verification criteria, the presented model presents the results as Root Mean Squared Error, RMSE=0.12 and Mean Average Percentage Error, MAPE=2.56 for train and RMSE=0.14 and MAPE=2.82 for verification. Also, the model presented in this study was compared with the other existing sediment transport equations which were obtained using nonlinear regression analysis.
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29
53


Isa
Ebtehaj
Ph.D. Candidate, Department of Civil Engineering, Razi University, Kermanshah, Iran
Ph.D. Candidate, Department of Civil Engineering,
Iran
isa.ebtehaj@gmail.com


Hossein
Bonakdari
Professor, Department of Civil Engineering, Razi University, Kermanshah, Iran
Professor, Department of Civil Engineering,
Iran
bonakdari@yahoo.com
Bed load
Sediment transport
Sewer
Nodeposition
GeneExpression Programming
[Ab Ghani A (1993) Sediment transport in sewers. PhD Thesis, University of Newcastle Upon Tyne, UK##Ab Ghani A, Azamathulla HM (2011) Gene Experession Programming for Sediment Transport in Sewer Pipe Systems. J Pipeline Syst Eng Pract 2(3):102106##Ackers P (1991) Sediment aspects of drainage and outfall design. Proc Int Symp Environ Hydraul, Hong Kong.##Ackers JC, Butler D, May RWP (1996) Design of sewers to control sediment problems. Report No. CIRIA 141, Construction Industry Research and Information Association, London, UK##Ackers P, White WR (1973) Sediment transport; new approach and analysis. J Hydraul DivASCE., 99(HY11):20412060##Ahmadianfar I, Adib A, Taghian M (2016) Optimization of multireservoir operation with a new hedging rule: application of fuzzy set theory and NSGAII. Appl Water Sci. doi:10.1007/s132010160434z##AlAbadi AM (2014) Modeling of stage–discharge relationship for Gharraf River, southern Iraq using backpropagation artificial neural networks, M5 decision trees, and Takagi–Sugeno inference system technique: a comparative study. Appl Water Sci. 114. doi:10.1007/s1320101402587##ASCE. (1970) Water pollution control federation: Design and construction of sanitary and storm sewers. American Society of Civil Engineers Manuals and Reports on Engineering Practices, No. 37, Reston, VA##Azamathulla HM, Ab Ghani A (2010) Genetic Programming to Predict River Pipeline Scour. J Pipeline Syst Eng Pract 1(3):127132##Azamathulla HM, Ab Ghani A, Fei SY (2012) ANFIS – based approach for predicting sediment transport in clean sewer. J Appl soft Comput 12(3):12271230##Azamathulla HMd, Ahmad Z (2012) Geneexpression programming for transverse mixing coefficient. J Hydrol 435(20):142148##Azimi H, Bonakdari H, Ebtehaj I, Talesh SHA, Michelson D G, Jamali A (2017) Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition. Fuzzy Set Syst 319:5069.##Banasiak R (2008) Hydraulic performance of sewer pipes with deposited sediments. Water Sci Technol 57(11):17431748##Butler D, Clark RB (1995) Sediment management in urban drainage catchments. CIRIA Report No. 134, Construction Industry Research and Information Association, London, UK.##Butler D, May R, Ackers J (2003) Selfcleansing sewer design based on sediment transport principles. J Hydraul Eng 129(4):276282.##BS80051. (1987) Sewerage Guide to New Sewerage Construction, British Standard Institution, London, UK.##Chang CK, Azamathulla HM, Zakaria NA, Ab Ghani A (2012) Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers. J Earth Syst Sci 121(1):125133##Ebtehaj I, Bonakdari H (2013) Evaluation of Sediment Transport in Sewer using Artificial Neural Network. Eng Appl Comput Fluid Mech 7(3):382392##Ebtehaj I, Bonakdari H (2014) Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers. Water Resour Manage 28(13):4765–4779##Ebtehaj I, Bonakdari H, Sharifi A (2014) Design criteria for sediment transport in sewers based on selfcleansing concept. J Zhejiang UnivSci A 15(11):914924##European Standard EN 7524 (1997) Drain and sewer system outside building: Part 4. Hydraulic design and environmental considerations, Brussels: CEN (European Committee for Standardization)##Gad MI, Khalaf S (2013) Application of sharing genetic algorithm for optimization of groundwater management problems in Wadi ElFarigh, Egypt. Appl Water Sci 3(4):701716##Gorai AK, Hasni SA, Iqbal J (2014) Prediction of ground water quality index to assess suitability for drinking purposes using fuzzy rulebased approach. Appl Water Sci. doi:10.1007/s1320101402413##Maghrebi MF, Givehchi M (2007) Using nondimensional velocity curves for estimation of longitudinal dispersion coefficient. Proceedings of the seventh international symposium river engineering, 1618 October, Ahwaz, Iran.##Ferreira C (2001) Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Syst 13(2):87–129##Ferreira C (2006) Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. 2nd Edition, SpringerVerlag, Germany##Gan Z, Yang Z, Li G, Jiang M (2007) Automatic modeling of complex functions with clonal selectionbased gene expression programming. In Natural Computation, 2007. ICNC 2007. Third International Conference on (Vol. 4, pp. 228232). IEEE.##Graf WH, Acaroglu ER (1968) Sediment transport in conveyance systems. Bulletin IAHR, Part 1, 13(2):2039.##Hsu K, Gupta VH, Sorroshian S (1995) Artificial neural network modeling of the rainfallrunoff process. Water Resour Res 31(10):25172530##Isanta Navarro R (2013) Study of a neural networkbased system for stability augmentation of an airplane. Universitat Polite`cnica de Catalunya, Barcelona, pp. 77.##Jain A, Ormsbee LE (2002) Evaluation of shortterm water demand forecast modeling techniques: Conventional methods versus AI. J Am Water Works Ass 94(7):6472##Jain A, Varshney AK, Joshi UC (2001) Shortterm water demand forecast modeling at IIT Kanpur using artificial neural networks. Water Resour Manage 15(5):299321##Khan M., Azamathulla, HM, Tufail M, Ab Ghani A (2012) Bridge pier scour prediction by gene expression programming. P ICEWater Manage 165(9):481493##Khoshbin F, Bonakdari H, Ashraf Talesh SH, Ebtehaj I, Zaji AH, Azimi H (2016) Adaptive neurofuzzy inference system multiobjective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharpcrested side weirs. Eng Optimiz 48(6):933948.##Koza JR (1992) Genetic programming: On the programming of computers by means of natural selection, MIT Press, Cambridge, MA, USA##Legates DR, McCabe JR (1999) Evaluating the use of goodnessoffit measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233241##Lysne DK (1969) Hydraulic design of selfcleaning sewage tunnels. J Sanitary Eng DivASCE 95(SA1):1736##Macke E (1982) About sediment at low concentrations in partly filled pipes. Mitteilungen, Leichtweiss institut fur Wasserbau der technischen Universitat Braunschweig, Heft 71:1151 (In Germany)##May RWP (1982) Sediment transport in sewers. Hydraulic Research Station, Wallingford, England, Report IT 222##May RWP, Ackers JC, Butler D, John S (1996) Development of design methodology for selfcleansing sewers. Water Sci Technol 33(9):195205##May RWP, Brown PM, Hare GR, Jones KD (1989) Selfcleansing condition for sewers carrying sediment. Hydraulic Research Ltd (Wallingford), Report SR 221##Mayerle R (1988) Sediment transport in rigid boundary channels. PhD Thesis, University of Newcastle Upon Tyne, UK##Mayerle R, Nalluri C, Novak P (1991) Sediment transport in rigid bed conveyance. J Hydraul Res 29(4):475495##Mondal SK, Jana S, Majumder M, Roy D (2012) A comparative study for prediction of direct runoff for a river basin using geomorphological approach and artificial neural networks. Appl Water Sci 2(1):113##Nalluri C (1985) Sediment transport in rigid boundary channels. Proceeding Euromech 192: Transport of Suspended Solids in Open channels, Neubiberg, Germany##Nalluri C, Ab Ghani A (1993) Bed load transport without deposition in channel of circular section. Proceeding of the sixth international conference on Urban Storm Drainage, Niagara Falls, Canada##Nalluri C, Ab Ghani A (1996) Design option for selfcleansing storm sewers. Water Sci Technol 33(9):215220##Nalluri C, Ab Ghani A, ElZaemey AK (1994) Sediment transport over deposited beds in sewers. Water Sci Technol 29(12):125133##Novak P, Nalluri C, (1975) Sediment transport in smooth fixed bed channels. J Hydraul DivASCE 101(9):11391154##Ota JJ, Nalluri C (1999) Graded sediment transport at limit deposition in clean pipe channel. 28th Int Assoc HydroEnviron Eng Res, Graz, Austria##Ota JJ, Perrusquía GS (2013).Particle velocity and sediment transport at the limit of deposition in sewers. Water Sci. Technol 67(5):959967.##Pedroli R (1963) Bed load transportation in channels with fixed and smooth inverts. PhD Thesis, Scuola Politecnica Federale, Zurigo, Switzerland##Rajurkar MP, Kothyari UC, Chaube UC (2004) Modeling of the daily rainfallrunoff relationship with artificial neural network. J Hydrol 285(1):96113.##Rezaei H, Rahmati M, Modarress H (2017) Application of ANFIS and MLR models for prediction of methane adsorption on X and Y faujasite zeolites: effect of cations substitution. Neural Comput Appl 28(2):301312.##Safari MJS, Aksoy H, Unal NE, Mohammadi M (2017) Nondeposition selfcleansing design criteria for drainage systems. J Hydroenviron Rese 14:7684.##Singh R, Vishal V, Singh T (2012) Soft computing method for assessment of compressional wave velocity. Sci Iran 19:1018–1024##Singh R, Vishal V, Singh T, Ranjith P (2013) A comparative study of generalized regression neural network approach and adaptive neurofuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23:499–506##Vongvisessomjai N, Tingsanchali T, Babel MS (2010) Nondeposition design criteria for sewers with partfull flow. Urban Water J 7(1):6177##]
Using of Backpropagation Neural Network in Estimation of Compressive Strength of Waste Concrete
2
2
Waste concrete is one of the most usable and economic kind of concrete which is used in many civil projects all around the world, and its importance is undeniable. Also, the explanation of constructional process and destruction of them cause the extensive growth of irreversible waste to the industry cycle, which can be as one of the main damaging factors to the economy. In this investigation, with using of constructional waste included concrete waste, brick, ceramic and tile and stone new aggregate was made, also it was used with different weight ratios of cement in mix design. The results of laboratory studies showed that, the using of ratio of sand to cement 1 and waste aggregate with 20% weight ratio (W20), replacing of normal aggregate, increased the 28 days compressive strength to the maximum stage 45.23 MPa. In the next stage, in order to develop the experimental results backpropagation neural network was used. This network with about 91% regression, 0.24 error, and 1.41 seconds, is a proper method in estimating of results.
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Ali
Heidari
Associate Professor, Department of Civil Engineering, Shahrekord University, Shahrekord, Iran
Associate Professor, Department of Civil
Iran
heidari@eng.sku.ac.ir


Masoumeh
Hashempour
M.Sc. Student, Department of Civil Engineering, Shahrekord University, Shahrekord, Iran
M.Sc. Student, Department of Civil Engineering,
Iran
ms.hashempour@gmail.com


Davoud
Tavakoli
Ph.D., Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
Ph.D., Department of Civil Engineering, Shahid
Iran
tavakoli.d@gmail.com
Waste materials
Concrete
Compressive strength
Backpropagation neural network
[Tavakoli, D., Heidari, A., & karimian, M. (2013). Properties of concretes produced with waste ceramic tile aggregate. Asian journal of civil engineering (BHRC), 14(3), 369382.##Ay, N., & Unal, M. (2000). The use of waste ground ceramic in cement production. Cement and Concrete Research, 30(3), 497499.##Heidari, A., & Tavakoli, D. (2013). A study of the mechanical properties of ground ceramic powder concrete incorporating nanoSiO2 particles. Construction and Building Materials, 38, 255264. doi:https://doi.org/10.1016/j.conbuildmat.2012.07.110##Akhtaruzzaman, A. A., & Hasnat, A. (1983). Properties of concrete using crushed brick as aggregates. Concr. Int, 5(2), 5863.##Tavakoli, D., Heidari, A., & Hayati Pilehrood, S. (2014). Properties of Concrete made with Waste Clay Brick as Sand Incorporating Nano SiO2. Indian Journal of Science and Technology, 7(12), 18991905.##Merlet, J., & Pimienta, P. (1993). Mechanical and physical–chemical properties of concrete produced with coarse and fine recycled aggregates. Paper presented at the Demolition and reuse of concrete and masonry.##Zega, C., & di Maio, A. (2011). Use of recycled fine aggregate in concrete with durable requirements. Waste Manage, 31, 23362340.##Jeong, G. (2011). Processing and properties of recycled aggregate concrete. Paper presented at the MSc dissertation in Civil Engineering.##Kalman Šipoš, T., Miličević, I., & Siddique, R. (2017). Model for mix design of brick aggregate concrete based on neural network modelling. Construction and Building Materials, 148, 757769. doi:https://doi.org/10.1016/j.conbuildmat.2017.05.111##Khademi, F., Jamal, S. M., Deshpande, N., & Londhe, S. (2016). Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive NeuroFuzzy Inference System and Multiple Linear Regression. International Journal of Sustainable Built Environment, 5(2), 355369. doi:https://doi.org/10.1016/j.ijsbe.2016.09.003##Zhou, Q., Wang, F., & Zhu, F. (2016). Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neurofuzzy inference systems. Construction and Building Materials, 125, 417426. doi:https://doi.org/10.1016/j.conbuildmat.2016.08.064##Chandwani, V., Agrawal, V., & Nagar, R. (2015). Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks. Expert Systems with Applications, 42(2), 885893. doi:https://doi.org/10.1016/j.eswa.2014.08.048##Heidari, A., Tavakoli, D., & Fakharian, P. (1392). Approximation of special sheet values using artificial neural network. Quarterly Journal of Engineering Modeling, 11(35).##Doğan, E. (2009). Reference evapotranspiration estimation using adaptive neurofuzzy inference systems. Irrigation and Drainage, 58(5), 617628. doi:10.1002/ird.445##]
A Method for Constructing NonIsosceles Triangular Fuzzy Numbers using Frequency Histogram and Statistical Parameters
2
2
The philosophy of fuzzy logic was formed by introducing the membership degree of a linguistic value or variable instead of divalent membership of 0 or 1. Membership degree is obtained by mapping the variable on the graphical shape of fuzzy numbers. Because of simplicity and convenience, triangular membership numbers (TFN) are widely used in different kinds of fuzzy analysis problems. This paper suggests a simple method using statistical data and frequency chart for constructing nonisosceles TFN, when we are using direct rating for evaluating a variable in a predefined scale. In this method the relevancy between assessment uncertainties and statistical parameters such as mean value and standard deviation is established in a way that presents an exclusive form of triangle number for each set of data. The proposed method with regard to the graphical shape of the frequency chart distributes the standard deviation around the mean value and forms the TFN with the membership degree of 1 for mean value. In the last section of the paper modification of the proposed method is presented through a practical case study.
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85


Amin
Amini
Ph.D. Student, Faculty of Science and Engineering, Curtin University, Kent St, Bentley WA 6102, Australia
Ph.D. Student, Faculty of Science and Engineering,
Australia
amin.amini@postgrad.curtin.edu.au


Navid
Nikraz
Senior lecturer, Faculty of Science and Engineering, Curtin University, Kent St, Bentley WA 6102, Australia
Senior lecturer, Faculty of Science and Engineerin
Australia
navid.nikraz@curtin.edu.au
Triangular fuzzy number
Nonisosceles
Membership function construction
Direct rating
statistical
[[1] Timothy, J. R., Parkinson, W.J., Fuzzy Logic and Probability Applications, 2002, Chapter 2, pp. 2943.##[2] Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning, Information Sciences 8 (1975), pp. 43–80.##[3] Zadeh, L.A., Fuzzy logic and approximate reasoning, Synthese, 30 (1975), pp. 407–428.##[4] Zadeh, L.A., A new direction in AI: toward a computational theory of perceptions, AI Magazine, 22 (1) (2001), pp. 73–84.##[5] Zadeh, L.A., Fuzzy sets Information and Control, 8 (1965), pp. 338–353.##[6] Castillo, O., Melin, P., Type2 Fuzzy Logic Theory and Applications, SpringerVerlag, Berlin Heidelberg (2008), Chapter 2, p. 6.##[7] Dubois, D., Prade, H., Possibility Theory: An Approach to Computerized Processing of Uncertainty, Plenum Press, New York, 1988.##[8] Barnabas, B., Mathematics of Fuzzy Sets and Fuzzy Logic, SpringerVerlag, Berlin Heidelberg (2013), Chapter 4, pp. 5164.##[9] Dubois, D., Prade, H., Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York, 1980.##[10] Stefanin, L., Sorini, L., Fuzzy arithmetic with parametric LR fuzzy numbers, IFSAEUSFLAT, 2009, pp. 600605.##[11] Castillo, O., Melin, P., Modelling, Simulation and Control of Nonlinear Dynamical System, Taylor and Francis, 2002, Chapter 2, pp. 1314.##[12] Bilgiç, T., Turksen, IB., Elicitation of Membership Functions: How far can theory take us?, Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, Vol. 3 (1997), pp. 13211325.##[13] Bilgiç, T., Turksen, IB., Measurement of membership functions: Theoretical and experimental work. To appear in the D. Dubois and H. Prade (eds) International Handbook of Fuzzy Systems, Vol. 1: Foundations, forthcoming.##[14] Krantz, D.H, Luce, R.D., Suppes, P., Tversky, A., Foundations of Measurement, Vol. 1. Academic Press, San Diego, 1971.##[15] Wierman, M.J., An introduction to the mathematics of uncertainty, Creighton University, 2010, pp. 149150.##[16] Saaty, T.L., The Analytic Hierarchy Process, McGraw Hill, New York, 1980.##[17] Zadeh, L.A., Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, Vol.1 (1978), pp. 328.##[18] Dubois, D., Prade, H., Unfair coins and necessity measures: towards a possibilistic interpretation of histograms, Fuzzy Sets and Systems, Vol. 10 (1983), pp. 1520.##[19] Klir G., A principle of uncertainty and information invariance, International Journal of General Systems, Vol. 17 (1990), pp. 249275.##[20] Civanlar, M.R., Trussell, H.J., Constructing membership functions using statistical data, Fuzzy Sets and Systems, Vol. 18 (1986), pp. 113.##[21] Valliappan, S., Pham, T.D., Constructing the membership function of a fuzzy set with objective and subjective information, Microcomputers in Civil Engineering, Vol.8 (1993), pp. 7582.##[22] Chen, J.E., Otto, K.N., Constructing membership functions using interpolation and measurement theory, Fuzzy Sets and Systems, Vol. 73 (1995), pp. 313327.##[23] Pedrycz, W., Why triangular membership functions?, Fuzzy Sets and Systems, Vol. 64 (1994), pp. 2130.##[24] Medasani, S., Kim, J., Krishnapuram, R., An overview of membership function generation techniques for pattern recognition, International Journal of Approximate Reasoning, Vol. 19 (1998), pp. 391417.##[25] SanchoRoyo, A., Verdegay, J.L., Methods for the construction of membership functions, International Journal of Intelligent Systems, Vol. 14 (1999), pp. 12131230.##[26] Sivanandam, S.N., Sumathi, S., Deepa, S.N., Introduction to Fuzzy Logic using MATLAB, SpringerVerlag, Berlin Heidelberg, 2007, pp. 7694.##[27] Azar, A., Faraji, H., Fuzzy Management Science, Management and Productivity Study Center of Iran (affiliated with the Tarbiat Modares University), 2008.##[28] Asgharpour, M.J, Multi Criteria Decision Makings, Tehran University publications, 2009, Chapter 4, pp. 332346.##[29] Norwich, A.M., Turksen, IB., A model for the measurement of membership and the consequences of its empirical implementation, Fuzzy Sets and Systems, Vol.12 (1984), pp. 125.##[30] Amini, A, Evaluating the effective parameters in prioritizing urban roadway bridges for maintenance operation using fuzzy logic, M.Sc. Thesis, Science and research branch of Tehran Azad University, 2010.##[31] Friedman, M., The use of ranks to avoid the assumption of normality implicit in the analysis of variance, Journal of the American Statistical Association (American Statistical Association), Vol. 32 (1937), pp. 675–701.##[32] Mabuchi, S., An approach to the comparison of fuzzy subsets with a αcut dependent index, IEEE Trans. Systems Man. Cybernet. SMC18, 1988, pp. 264272.##[33] Fathizadeh, A., Evaluating the effective parameters in urban roadway bridges prioritization, M.Sc. Thesis, Science and research branch of Tehran Azad University, 2010.##]
GMDHNetwork to Estimate the Punching Capacity of FRPRC Slabs
2
2
Determination of the punching shear capacity of FRPreinforced concrete slabs was studied in this paper. A database including 81 pairs of data was collected and used. The method was considered in the paper, was group method of data handling (GMDH) which is one of the most structures which is used by researchers. The section area of column, effective flexural depth of slab, compressive strength of concrete, Young’s modulus of the FRP slab and reinforcement ratio were used as input variables. The target of the model was also determination of the ultimate punching capacity of the FRPreinforced concrete flat slab (Target). Based on this dataset, ten polynomials specified and its coefficients was presented. All of these ten polynomials used for the considered prediction. The proposed GMDH structure also validate by several experimental data. The results indicated that group method of data handling (GMDH) is very useful for the prediction of the punching shear capacity of slabs.
1

86
92


Alla
Azimi
Dept. of Civil Engineering, University of Birmingham, United Kingdom
Dept. of Civil Engineering, University of
United Kingdom
alla.azimi@gmail.com
GMDH
FRP
shear capacity
RCSlabs
[[1] A. Ivakhnenko, "Polynomial theory of complex systems," IEEE Transactions on Systems, Man, and Cybernetics, pp. 364378, 1971.##[2] M. Mirrashid, "Earthquake magnitude prediction by adaptive neurofuzzy inference system (ANFIS) based on fuzzy Cmeans algorithm," Natural hazards, vol. 74, pp. 15771593, 2014.##[3] M. Mirrashid, M. Givehchi, M. Miri, and R. Madandoust, "Performance investigation of neurofuzzy system for earthquake prediction " Asian journal of civil enginering (BHRC), vol. 17, pp. 213223, 2016.##[4] H. Naderpour and M. Mirrashid, "Compressive Strength of Mortars Admixed with Wollastonite and Microsilica," in Materials Science Forum, 2017, pp. 415418.##[5] H. Naderpour and M. Mirrashid, "Application of Soft Computing to Reinforced Concrete Beams Strengthened with Fibre Reinforced Polymers: A StateoftheArt Review," in Computational techniques for civil and structural engineering ed Stirlingshire, UK: SaxeCoburg Publications, 2015, pp. 305323.##[6] M. A. W. Hassan, "Punching Shear Behaviour of Concrete Twoway Slabs Reinforced with Glass Fiberreinforced Polymer (GFRP) Bars," Université de Sherbrooke, 2013.##[7] I. M. Metwally, "Prediction of punching shear capacities of twoway concrete slabs reinforced with FRP bars," HBRC Journal, vol. 9, pp. 125133, 2013.##[8] L. NguyenMinh and M. Rovňák, "Punching shear resistance of interior GFRP reinforced slabcolumn connections," Journal of Composites for Construction, vol. 17, pp. 213, 2012.##]
Capacity Prediction of RC Beams Strengthened with FRP by Artificial Neural Networks Based on Genetic Algorithm
2
2
In this paper, the ability of the artificial neural network which was trained based on Genetic algorithm, used to prediction the shear capacity of the reinforced concrete beams strengthened with sidebonded fibre reinforced polymer (FRP). A database of experimental data including 95 data which were published in literatures was collected and used to the network. Seven inputs including width of the beam, effective depth, FRP thickness, Young modulus, tensile strength of FRP and also FRP ratio were used to predict the shear capacity of the reinforced concrete beams strengthened with sidebonded fibre reinforced polymer. The best values of the weights and the biases was obtained by the Genetic algorithm. For increasing the ability of the model to predict the considered target, it was suggested that the predicted values considered smaller. The results indicated that the proposed neural network based on genetic algorithm was able to predict the shear capacity of the considered elements.
1

93
98


Ghazal
Hosseini
Dept. of Civil Engineering, University of New South Wales, Sydney, Australia
Dept. of Civil Engineering, University of
Australia
ghazal.1792@yahoo.com.au
Artificial Neural Networks
FRP
Shear strength
Genetic Algorithm
[[1] H. Naderpour and M. Mirrashid, "Application of Soft Computing to Reinforced Concrete Beams Strengthened with Fibre Reinforced Polymers: A StateoftheArt Review," in Computational techniques for civil and structural engineering ed Stirlingshire, UK: SaxeCoburg Publications, 2015, pp. 305323.##[2] M. Mirrashid, "Earthquake magnitude prediction by adaptive neurofuzzy inference system (ANFIS) based on fuzzy Cmeans algorithm," Natural hazards, vol. 74, pp. 15771593, 2014.##[3] M. Mirrashid, M. Givehchi, M. Miri, and R. Madandoust, "Performance investigation of neurofuzzy system for earthquake prediction " Asian journal of civil enginering (BHRC), vol. 17, pp. 213223, 2016.##[4] H. Naderpour and M. Mirrashid, "Compressive Strength of Mortars Admixed with Wollastonite and Microsilica," in Materials Science Forum, 2017, pp. 415418.##[5] A. Beber and A. Campos Filho, "CFRP composites on the shear strengthening of reinforced concrete beams," Rev IBRACON Estruturas, vol. 1, 2005.##[6] A. Carolin, B. Täljsten, and A. Hejll, "Concrete beams exposed to live loading during carbon fiber reinforced polymer strengthening," Journal of Composites for Construction, vol. 9, pp. 178186, 2005.##[7] O. Chaallal, M.J. Nollet, and D. Perraton, "Strengthening of reinforced concrete beams with externally bonded fiberreinforcedplastic plates: design guidelines for shear and flexure," Canadian Journal of Civil Engineering, vol. 25, pp. 692704, 1998.##[8] J. Chen and J. Teng, "Shear capacity of FRPstrengthened RC beams: FRP debonding," Construction and Building Materials, vol. 17, pp. 2741, 2003.##[9] C. Deniaud and J. Roger Cheng, "Reinforced concrete Tbeams strengthened in shear with fiber reinforced polymer sheets," Journal of Composites for Construction, vol. 7, pp. 302310, 2003.##[10] P. Fanning and O. Kelly, "Shear strengthening of reinforced concrete beams: an experimental study using CFRP plates," Structural Faults+ Repair, vol. 99, 1999.##[11] J. Jayaprakash, A. A. A. Samad, A. A. Abbasovich, and A. A. A. Ali, "Shear capacity of precracked and nonprecracked reinforced concrete shear beams with externally bonded bidirectional CFRP strips," Construction and Building Materials, vol. 22, pp. 11481165, 2008.##[12] D. Kachlakev and W. Barnes, "Flexural and shear performance of concrete beams strengthened with fiber reinforced polymer laminates," Special Publication, vol. 188, pp. 959972, 1999.##[13] T. Kage, M. Abe, H. Lee, and F. Tomosawa, "Effect of CFRP sheets on shear strengthening of RC beams damaged by corrosion of stirrup," in Proceedings of the Third International Symposium: NonMetallic (FRP) Reinforcement for Concrete Structures, Japan Concrete Institute, Sapporo, Japan, 1997, pp. 443450.##[14] A. Khalifa and A. Nanni, "Improving shear capacity of existing RC Tsection beams using CFRP composites," Cement and Concrete Composites, vol. 22, pp. 165174, 2000.##[15] G. Kim, J. Sim, and H. Oh, "Shear strength of strengthened RC beams with FRPs in shear," Construction and Building Materials, vol. 22, pp. 12611270, 2008.##[16] H. Leung, "Strengthening of RC beams: some experimental findings," Structural Survey, vol. 20, pp. 173181, 2002.##[17] Y. Mitsui, K. Murakami, K. Takeda, and H. Sakai, "A study on shear reinforcement of reinforced concrete beams externally bonded with carbon fiber sheets," Composite Interfaces, vol. 5, pp. 285295, 1997.##[18] G. Monti, "Tests and design equations for FRPstrengthening in shear," Construction and Building Materials, vol. 21, pp. 799809, 2007.##[19] C. Pellegrino and C. Modena, "Fiber reinforced polymer shear strengthening of reinforced concrete beams with transverse steel reinforcement," Journal of Composites for Construction, vol. 6, pp. 104111, 2002.##[20] Y. Sato, T. Ueda, Y. Kakuta, and T. Tanaka, "Shear reinforcing effect of carbon fiber sheet attached to side of reinforced concrete beams," in Proceedings of the 2nd International conference on advanced composite materials in bridges and structures, Montreal, 1996, 1996.##[21] J. Sim, G. Kim, C. Park, and M. Ju, "Shear strengthening effects with varying types of FRP materials and strengthening methods," in 7th International Symposium on FiberReinforced Polymer (FRP) Reinforcement for Concrete Structures, 2005, pp. 16651680.##[22] T. C. Triantafillou, "Shear strengthening of reinforced concrete beams using epoxybonded FRP composites," Structural Journal, vol. 95, pp. 107115, 1998.##[23] K. Uji, "Improving shear capacity of existing reinforced concrete members by applying carbon fiber sheets," Transactions of the Japan Concrete Institute, vol. 14, 1992.##[24] H. Tanarslan, "Predicting the Capacity of RC Beams Strengthened in Shear with SideBonded FRP Reinforcements Using Artificial Neural Networks," Composite Interfaces, vol. 18, pp. 587614, 2011.##]