Automated serviceability prediction of NSM strengthened structure using a fuzzy logic expert system

Darain, K.M.U. and Jumaat, M.Z. and Hossain, M.A. and Hosen, M.A. and Obaydullah, M. and Huda, M.N. and Hossain, I. (2015) Automated serviceability prediction of NSM strengthened structure using a fuzzy logic expert system. Expert Systems with Applications, 42 (1). pp. 376-389. ISSN 0957-4174

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Abstract

This paper presents a simplified model using a fuzzy logic approach for predicting the serviceability of reinforced concrete (RC) beams strengthened with near surface mounted (NSM) reinforcement. Existing analytical models lack proper formulations for the prediction of deflection and crack width in NSM strengthened beams. These existing models are based on the externally bonded reinforcement (EBR) technique with fiber reinforced polymer (FRP) laminates, which presents certain limitations for application in predicting the behavior of NSM strengthened beams. In this study seven NSM strengthened RC beams were statically tested under four point bending load. The test variables were strengthening material (steel or CFRP) and bond length (1600, 1800 or 1900 mm). For fuzzification, load and bonded length were used as input parameters and the output parameters were deflection and crack width for steel bar and CFRP bar. Experimentally NSM steel strengthened beams showed better performance in terms of crack width and stiffness, although NSM FRP strengthened beams exhibited enhanced strength increment. For all parameters, the relative error of the predicted values was found to be within the acceptable limit (5) and the goodness of fit of the predicted values was found to be close to 1.0. Hence, the developed prediction system can be said to have performed satisfactorily. (C) 2014 Elsevier Ltd. All rights reserved.

Item Type: Article
Additional Information: As1im Times Cited:0 Cited References Count:43
Uncontrolled Keywords: Steel, cfrp, deflection, crack width, prediction model, error analysis, reinforced-concrete beams, shear-strength, rc beams, neural-network, frp, deflection, behavior, bars,
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 23 Jul 2015 01:58
Last Modified: 04 Oct 2017 08:03
URI: http://eprints.um.edu.my/id/eprint/13776

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