Application of artificial neural networks to predict the bond strength of FRP-to-concrete joints

Abstract A Back-Propagation Neural Network (BPNN) model for predicting the bond strength of FRP-to-concrete joints is proposed. Published single-lap shear test specimens were used to predict the bond strength of externally bonded FRP systems adhered to concrete prisms. A database of one hundred and fifty experimental data points from several sources was used for training and testing the BPNN. The data used in the BPNN are arranged in a format of six input parameters including: width of concrete prism; concrete cylinder compressive strength; FRP thickness; bond length; bond width (i.e. FRP width); and FRP modulus of elasticity. The one corresponding output parameter is the bond strength. A parametric study was carried out using BPNN to study the influence of each parameter on the bond strength and to compare results with common existing analytical models. The results of this study indicate that the BPNN provides an efficient alternative method for predicting the bond strength of FRP-to-concrete joints when compared to experimental results and those from existing analytical models.

[1]  M. Erki,et al.  FRP Reinforcement for Concrete Structures , 1993 .

[2]  M. S. Mohamed Ali,et al.  Plate–concrete interfacial bond strength of FRP and metallic plated concrete specimens , 2006 .

[3]  Jin-Guang Teng,et al.  Bond-slip models for FRP sheets/plates bonded to concrete , 2005 .

[4]  Qingda Yang,et al.  On shear bond strength of FRP-concrete structures , 2010 .

[5]  Michael J. Chajes,et al.  Bond and Force Transfer of Composite-Material Plates Bonded to Concrete , 1996 .

[6]  Ahmed Senouci,et al.  A pavement condition-rating model using backpropagation neural networks , 1995 .

[7]  Matthew B. Blaschko,et al.  Externally bonded FRP reinforcement for RC structures , 2001 .

[8]  Turki Y. Abdalla,et al.  Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members , 2010 .

[9]  Jian fei Chen,et al.  Anchorage strength models for FRP and steel plates bonded to concrete , 2001 .

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

[11]  Tamon Ueda,et al.  Development of the Nonlinear Bond Stress-Slip Model of Fiber Reinforced Plastics Sheet-Concrete Interfaces with a Simple Method , 2005 .

[12]  Stuart S. Chen,et al.  Neural Networks in Dynamic Analysis of Bridges , 1992 .

[13]  Houssam Toutanji,et al.  Prediction of Interfacial Bond Failure of FRP–Concrete Surface , 2007 .

[14]  M. Q. Feng,et al.  Identification of a Dynamic System Using Ambient Vibration Measurements , 1998 .

[15]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[16]  Zhishen Wu,et al.  Analytical/Experimental Study on Composite Behavior in Strengthening Structures with Bonded Carbon Fiber Sheets , 1999 .

[17]  Yassine Djebbar,et al.  NEURAL NETWORK MODEL FOR PREFORMED-FOAM CELLULAR CONCRETE , 2001 .

[18]  Stephanie L. Walkup,et al.  Guide for the Design and Construction of Externally Bonded FRP Systems for Strengthening Concrete Structures (ACI 440.2R-02) , 2005 .

[19]  Guido Bugmann,et al.  NEURAL NETWORK DESIGN FOR ENGINEERING APPLICATIONS , 2001 .

[20]  Abhijit Mukherjee,et al.  Prediction of Buckling Load of Columns Using Artificial Neural Networks , 1996 .

[21]  A. Alan B. Pritsker,et al.  Principles of modeling , 1991, 1991 Winter Simulation Conference Proceedings..

[22]  Sang-Kyun Woo,et al.  Experimental study on interfacial behavior of CFRP-bonded concrete , 2010 .

[23]  Jian fei Chen,et al.  Experimental study on FRP-to-concrete bonded joints , 2005 .

[24]  U. Neubauer,et al.  DESIGN ASPECTS OF CONCRETE STRUCTURES STRENGTHENED WITH EXTERNALLY BONDED CFRP-PLATES , 1997 .

[25]  Scott T. Smith,et al.  FRP: Strengthened RC Structures , 2001 .

[26]  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 .

[27]  Prabhat Hajela,et al.  Neurobiological computational models in structural analysis and design , 1991 .

[28]  Maria Q. Feng,et al.  DAMAGE ASSESSMENT OF JACKETED RC COLUMNS USING VIBRATION TESTS , 1999 .

[29]  JongKoo Jeon.  Fuzzy and Neural Network Models for Analyses of Piles , 2007 .

[30]  G. David Garson,et al.  Interpreting neural-network connection weights , 1991 .

[31]  P Holzenkaempfer.  Ingenieurmodelle des Verbunds geklebter Bewehrung fuer Betonbauteile , 1997 .