Condition assessment of RC beams using artificial neural networks

Abstract In this study, an imaged-based methodology for condition assessment of Reinforced Concrete (RC) road bridge components is presented. The study is divided into experiments on RC rectangular beams and scaled (1:12) T-beams, validation of numerical models for T-beams and training of corresponding artificial neural networks (ANNs). In the experimental work, Digital Image Correlation (DIC) was used as a virtual sensor for data extraction. Rectangular RC beams of size 1800 mm × 150 mm × 200 mm and scaled (1:12) RC T-beams were tested under four-point flexural loading on a 100-ton dynamic testing machine. The experimental stress-strain curves obtained from the compression test on prism specimens at 28 days were used as input data for material model parameters in finite element model (FEM) software SAP2000. To assess the condition of structural components, a local damage index (LDI) was developed. Validation of FEM results with experimental results enables derivation of moment-curvature backbone curve for full-scale bridge girders, which enables further quantification of damage and residual moment capacity of full-scale bridges designed by Ministry of Road Transport and Highways (MoRTH). The correlation between the experiments, simulation and ANN predictions was found to be very satisfactory.

[1]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[2]  Richard N. White,et al.  Structural Modeling and Experimental Techniques , 1999 .

[3]  Paul J. Werbos,et al.  The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting , 1994 .

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

[5]  Jacques de Villiers,et al.  Backpropagation neural nets with one and two hidden layers , 1993, IEEE Trans. Neural Networks.

[6]  Hubert W. Schreier,et al.  Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts,Theory and Applications , 2009 .

[7]  Justin A. Blaber,et al.  Ncorr: Open-Source 2D Digital Image Correlation Matlab Software , 2015, Experimental Mechanics.

[8]  James H. Garrett,et al.  Use of neural networks in detection of structural damage , 1992 .

[9]  Andreas J. Kappos,et al.  Seismic damage indices for RC buildings: evaluation of concepts and procedures , 1997 .

[10]  Raheleh Jafari,et al.  Recent Advances in Intelligent-Based Structural Health Monitoring of Civil Structures , 2018 .

[11]  Daniel R. Fairbairn,et al.  Neural-network applications in predicting moment-curvature parameters from experimental data , 1996 .

[12]  Mahmud Ashraf,et al.  A new damage index for reinforced concrete structures subjected to seismic loads , 2011 .

[13]  Varinder S. Kanwar,et al.  Damage Detection for Framed RCC Buildings using ANN Modeling , 2007 .

[14]  Tong Guo,et al.  Analysis and assessment of bridge health monitoring mass data—progress in research/development of “Structural Health Monitoring” , 2012 .

[15]  Okan Önal,et al.  Artificial neural network application on microstructure-compressive strength relationship of cement mortar , 2010, Adv. Eng. Softw..

[16]  Oğuzhan Hasançebi,et al.  Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks , 2013 .