2011
DOI: 10.12989/sem.2011.39.2.267
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Prediction of moments in composite frames considering cracking and time effects using neural network models

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Cited by 9 publications
(6 citation statements)
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“…Since cracking and creep and shrinkage effects, in the type of frames being considered, are confined to beams only, it may be postulated based on the studies on the composite frames [26], that in order to establish redistribution of moment at a joint j with sufficient accuracy, cracking at the joint and adjacent joints (joint j and joint j + 1) only needs to be considered. Keeping this in view, the following input parameters for an internal joint j of a frame are identified as: …”
Section: Probable Structural Parametersmentioning
confidence: 99%
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“…Since cracking and creep and shrinkage effects, in the type of frames being considered, are confined to beams only, it may be postulated based on the studies on the composite frames [26], that in order to establish redistribution of moment at a joint j with sufficient accuracy, cracking at the joint and adjacent joints (joint j and joint j + 1) only needs to be considered. Keeping this in view, the following input parameters for an internal joint j of a frame are identified as: …”
Section: Probable Structural Parametersmentioning
confidence: 99%
“…The output parameter for other values of relative humidity can be estimated in a manner similar to that explained by Pendharkar et al [26].…”
Section: Inelastic Momentmentioning
confidence: 99%
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“…Further, neural networks have been applied to predict the various design quantities in steel-concrete composite structures including bending moments and deflections in continuous composite beams considering concrete cracking (Chaudhary et al 2007a(Chaudhary et al , 2014, bending moments and deflections in continuous composite beams considering cracking and time effects in concrete (Pendharkar et al 2007(Pendharkar et al , 2010, deflections in composite bridges considering flexibility of shear connectors, concrete cracking and shear lag effect (Tadesse et al 2012, Gupta et al 2013, and moments in composite frames considering cracking and time effects in concrete (Pendharkar et al 2011).…”
Section: Steel Sectionmentioning
confidence: 99%
“…Some of the recent applications of neural networks in the field of structural engineering include Latin A m erican Journal of Solids and Structures 12 (2015) 542-560 prediction of time effects in RC frames (Maru and Nagpal, 2004), prediction of damage detection in RC framed buildings after earthquake (Kanwar et al, 2007), structural health monitoring (Min et al, 2012;Kaloop and Kim, 2014), bending moment and deflection prediction in composite structures Pendharkar et al, 2007Pendharkar et al, , 2010Pendharkar et al, , 2011Tadesse et al, 2012;Gupta et al, 2013), predicting the creep response of a rotating composite disc operating at elevated temperature (Gupta et al, 2007), optimum design of RC beams subjected to cost , static model identification (Kim et al, 2009), response prediction of offshore floating structure (Uddin et al, 2012), prediction of deflection in high strength selfcompacting concrete deep beams (Mohammadhassani et al, 2013a;2013b) and prediction of energy absorption capability and mechanical properties of fiber reinforced self-compacting concrete containing nano-Silica particles (Tavakoli et al, 2014a;2014b). These studies reveal the strength of neural networks in predicting the solutions of different structural engineering problems.…”
Section: Introductionmentioning
confidence: 99%