Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network

In this paper, the Artificial Neural Network (ANN) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to predict the shear strength of Reinforced Concrete (RC) beams, and the models are compared with American Concrete Institute (ACI) and Iranian Concrete Institute (ICI) empirical codes. The ANN model, with Multi-Layer Perceptron (MLP), using a Back-Propagation (BP) algorithm, is used to predict the shear strength of RC beams. Six important parameters are selected as input parameters including: concrete compressive strength, longitudinal reinforcement volume, shear span-to-depth ratio, transverse reinforcement, effective depth of the beam and beam width. The ANFIS model is also applied to a database and results are compared with the ANN model and empirical codes. The first-order Sugeno fuzzy is used because the consequent part of the Fuzzy Inference System (FIS) is linear and the parameters can be estimated by a simple least squares error method. Comparison between the models and the empirical formulas shows that the ANN model with the MLP/BP algorithm provides better prediction for shear strength. In adition, ANN and ANFIS models are more accurate than ICI and ACI empirical codes in prediction of RC beams shear strength.

[1]  Hamed Ketabchi,et al.  PREDICTING DENSITY AND COMPRESSIVE STRENGTH OF CONCRETE CEMENT PASTE CONTAINING SILICA FUME USING AR-TIFICIAL NEURAL NETWORKS , 2009 .

[2]  Jamal A. Abdalla,et al.  Modeling and simulation of shear resistance of R/C beams using artificial neural network , 2007, J. Frankl. Inst..

[3]  Seung-Chang Lee,et al.  Prediction of concrete strength using artificial neural networks , 2003 .

[4]  Yong-seong Kim,et al.  Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model , 2008 .

[5]  J. A. Ware,et al.  Using neural networks to predict workability of concrete incorporating metakaolin and fly ash , 2003 .

[6]  G. N. J. Kani,et al.  The Riddle of Shear Failure and its Solution , 1964 .

[7]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[8]  Murat Dicleli,et al.  Predicting the shear strength of reinforced concrete beams using artificial neural networks , 2004 .

[9]  Maria Q. Feng,et al.  Application of neural networks for estimation of concrete strength , 2002 .

[10]  Seyed Jamshid Mousavi,et al.  APPLICATION OF FUZZY INFERENCE SYSTEM IN THE PREDICTION OF WAVE PARAMETERS , 2005 .

[11]  Theodore Zsutty,et al.  SHEAR STRENGTH PREDICTION FOR SEPERATE CATEGORIES OF SIMPLE BEAM TESTS , 1971 .

[12]  Mahmut Bilgehan,et al.  Comparison of ANFIS and NN models - With a study in critical buckling load estimation , 2011, Appl. Soft Comput..

[13]  T. Zsutty.  Beam Shear Strength Prediction by Analysis of Existing Data , 1968 .

[14]  Leonard Ziemiański,et al.  Neural networks in mechanics of structures and materials – new results and prospects of applications , 2001 .

[15]  W. J. Krefeld,et al.  Contribution of Longitudinal Steel to Shear Resistance of Reinforced Concrete Beams , 1966 .

[16]  E. Bentz.  Sectional analysis of reinforced concrete members , 2000 .

[17]  Hong-Guang Ni,et al.  Prediction of compressive strength of concrete by neural networks , 2000 .

[18]  Hakan Erdem,et al.  Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks , 2010, Adv. Eng. Softw..

[19]  G. N. J. Kani,et al.  HOW SAFE ARE OUR LARGE REINFORCED CONCRETE BEAMS , 1967 .

[20]  Muhammad N. S Hadi.  Neural networks applications in concrete structures , 2003 .

[21]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[22]  W. J. Krefeld,et al.  Studies of the Shear and Diagonal Tension Strength of Simply SupportedReinforced Concrete Beams , 1966 .

[23]  Antoni Cladera Bohigas.  Shear design of reinforced high-strength concrete beams , 2003 .

[24]  Naci Caglar,et al.  Neural networks in 3-dimensional dynamic analysis of reinforced concrete buildings , 2008 .

[25]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[26]  I. Yeh.  Modeling Concrete Strength with Augment-Neuron Networks , 1998 .

[27]  Antoni Cladera,et al.  Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part II: beams with stirrups , 2004 .

[28]  İlker Bekir Topçu,et al.  Prediction of rubberized mortar properties using artificial neural network and fuzzy logic , 2008 .

[29]  Kazuhiko Kawashima,et al.  Neural Network Modeling of Confined Compressive Strength and Strain of Circular Concrete Columns , 2003 .

[30]  A. Roli.  Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[31]  M. Hakan Arslan,et al.  Predicting of torsional strength of RC beams by using different artificial neural network algorithms and building codes , 2010, Adv. Eng. Softw..

[32]  Artur Dubrawski,et al.  HPC Strength Prediction Using Artificial Neural Network , 1995 .

[33]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[34]  Andres Winston C. Oreta,et al.  Simulating size effect on shear strength of RC beams without stirrups using neural networks , 2004 .

[35]  Gregory C. Frantz,et al.  Shear Tests of High- and Low-Strength Concrete Beams Without Stirrups , 1984 .

[36]  James K. Wight,et al.  Towards deformation-based capacity design of RCS beam–column connections , 2003 .