Neural networks for prediction of deflection in composite bridges

Abstract Neural networks have been developed for prediction of deflections, at service load, in steel-concrete composite bridges incorporating flexibility of shear connectors, shear lag effect and cracking in concrete slabs. Three neural networks have been presented to cover simply supported bridges, two span continuous bridges and three span continuous bridges. The use of the neural networks requires a computational effort almost equal to that required for the simple beam analysis (neglecting flexibility of shear connectors, shear lag effect and cracking of concrete). The training and testing data for neural networks are generated using finite element software ABAQUS. The neural networks have been validated for number of bridges and the errors are found to be small. Closed form solutions are also proposed based on the developed neural networks. The networks/ closed form solutions can be used for rapid prediction of deflection for everyday design.

[1]  Naci Caglar,et al.  A parametric study for distortional buckling stress on cold-formed steel using a neural network , 2007 .

[2]  Claudio Amadio,et al.  Effective width evaluation for steel–concrete composite beams , 2002 .

[3]  A. E. Aktan,et al.  DIFFERENT LEVELS OF MODELING FOR THE PURPOSE OF BRIDGE EVALUATION , 1997 .

[4]  Hyo-Gyoung Kwak,et al.  Time-dependent behavior of composite beams with flexible connectors , 2002 .

[5]  Jianguo Nie,et al.  Steel-Concrete Composite Beams Considering Shear Slip Effects , 2003 .

[6]  Hong Hao,et al.  Vibration based damage detection using artificial neural network with consideration of uncertainties , 2007 .

[7]  Goran Turk,et al.  Fire Analysis of Steel Frames with the use of Artificial Neural Networks , 2007 .

[8]  Kassim M. Tarhini,et al.  Wheel Load Distribution in I‐Girder Highway Bridges , 1992 .

[9]  Bulent Akbas.  A neural network model to assess the hysteretic energy demand in steel moment resisting frames , 2006 .

[10]  Nabeel A. Jasim,et al.  Deflections of partially composite continuous beams: A simple approach , 1999 .

[11]  A. K. Nagpal,et al.  Bending Moment Prediction for Continuous Composite Beams by Neural Networks , 2007 .

[12]  M. Bradford,et al.  Strength Analysis of Steel–Concrete Composite Beams in Combined Bending and Shear , 2005 .

[13]  Gerhard Sedlacek,et al.  A simplified method for the determination of the effective width due to shear lag effects , 1993 .

[14]  Ulf Arne Girhammar,et al.  Dynamic analysis of composite members with interlayer slip , 1993 .

[15]  Ibrahim H. Guzelbey,et al.  Prediction of rotation capacity of wide flange beams using neural networks , 2006 .

[16]  J. Y. Fu,et al.  Wind effects on the world’s longest spatial lattice structure: Loading characteristics and numerical prediction , 2007 .

[17]  P Ansourian,et al.  EXPERIMENTS ON CONTINUOUS COMPOSITE BEAMS. , 1982 .

[18]  K. Sumangala,et al.  Damage assessment of prestressed concrete beams using artificial neural network (ANN) approach , 2006 .

[19]  Ashraf Ayoub,et al.  MIXED FORMULATION OF NONLINEAR STEEL-CONCRETE COMPOSITE BEAM ELEMENT , 2000 .

[20]  Yong Wang,et al.  Deflection of steel-concrete composite beams with partial shear interaction , 1998 .

[21]  Ibrahim H. Guzelbey,et al.  Prediction of web crippling strength of cold-formed steel sheetings using neural networks , 2006 .

[22]  E. Nigro,et al.  Steel and concrete composite beams with flexible shear connection: “exact” analytical expression of the stiffness matrix and applications , 2002 .

[23]  Naci Caglar,et al.  Neural network based approach for determining the shear strength of circular reinforced concrete columns , 2009 .

[24]  Luigino Dezi,et al.  SIMPLIFIED CREEP ANALYSIS OF COMPOSITE BEAMS WITH FLEXIBLE CONNECTORS. CLOSURE , 1993 .

[25]  Mohamed A. Shahin,et al.  Neural networks for modelling ultimate pure bending of steel circular tubes , 2008 .

[26]  E. Arcaklioğlu,et al.  Use of artificial neural networks for mapping of solar potential in Turkey , 2004 .

[27]  A. K. Nagpal,et al.  Hybrid Procedure for Cracking and Time-Dependent Effects in Composite Frames at Service Load , 2007 .

[28]  P. B. Deolalikar,et al.  Neural Networks for Estimation of Scour Downstream of a Ski-Jump Bucket , 2005 .

[29]  Naci Caglar,et al.  A new approach to determine the base shear of steel frame structures , 2009 .

[30]  Sung-Pil Chang,et al.  Crack control of a steel and concrete composite plate girder with prefabricated slabs under hogging moments , 2005 .

[31]  A. K. Nagpal,et al.  Neural network for bending moment in continuous composite beams considering cracking and time effects in concrete , 2007 .

[32]  Nabeel A. Jasim,et al.  Deflections of partially composite beams with linear connector density , 1999 .

[33]  R P Johnson,et al.  PARTIAL SHEAR CONNECTION IN COMPOSITE BEAMS FOR BUILDINGS. , 1991 .

[34]  Mounir Mabsout,et al.  Effect of Multilanes on Wheel Load Distribution in Steel Girder Bridges , 1999 .

[35]  Shao-Fei Jiang,et al.  Structural Damage Detection by Integrating Data Fusion and Probabilistic Neural Network , 2006 .

[36]  Ulf Arne Girhammar,et al.  Composite Beam-Columns with Interlayer Slip : Exact Analysis , 1993 .

[37]  Il-Sang Ahn,et al.  Effective Flange Width Definition for Steel-Concrete Composite Bridge Girder , 2004 .