Closed Form Solution for Deflection of Flexible Composite Bridges

Gupta, Raghvendra; Patel, K. A.; Chaudhary, Sandeep; Nagpal, A. K.

doi:10.1016/j.proeng.2013.01.013

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…Sigmoid function (logsig) is used as an activation function and the Levenberg-Marquardt back propagation learning algorithm (trainlm) is used for training. The back propagation algorithm has been used successfully for many structural engineering applications (Maru and Nagpal, 2004;Kanwar et al, 2007;Gupta et al, 2007;Pendharkar et al, 2007;2010;Chaudhary et al, 2007;Sarkar and Gupta, 2009;Gupta and Sarkar, 2009;Min et al, 2012;Tadesse et al, 2012;Mohammadhassani et al, 2013a;Gupta et al, 2013) and is considered as one of the efficient algorithms in engineering applications (Hsu et al, 1993). One hidden layer is chosen and the number of neurons in the layer is decided in the learning process by trial and error.…”

Section: Training Of Neural Networkmentioning

confidence: 99%

“…The explicit expression requires the values of inputs, weights of the links between the neurons in different layers, and biases of output neurons (Tadesse et al, 2012;Gupta et al, 2013). …”

Section: Explicit Expression For Prediction Of Effective Moment Of Inmentioning

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%

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Explicit expression for effective moment of inertia of RC beams

Patel

Bhardwaj

Chaudhary

et al. 2015

Lat. Am. j. solids struct.

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Deflection is an important design parameter for structures subjected to service load. This paper provides an explicit expression for effective moment of inertia considering cracking, for uniformly distributed loaded reinforced concrete (RC) beams. The proposed explicit expression can be used for rapid prediction of short-term deflection at service load. The explicit expression has been obtained from the trained neural network considering concrete cracking, tension stiffening and entire practical range of reinforcement. Three significant structural parameters have been identified that govern the change in effective moment of inertia and therefore deflection. These three parameters are chosen as inputs to train neural network. The training data sets for neural network are generated using finite element software ABAQUS. The explicit expression has been validated for a number of simply supported and continuous beams and it is shown that the predicted deflections have reasonable accuracy for practical purpose. A sensitivity analysis has been performed, which indicates substantial dependence of effective moment of inertia on the selected input parameters.

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Section: Training Of Neural Networkmentioning

confidence: 99%

“…The explicit expression requires the values of inputs, weights of the links between the neurons in different layers, and biases of output neurons (Tadesse et al, 2012;Gupta et al, 2013). …”

Section: Explicit Expression For Prediction Of Effective Moment Of Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Explicit expression for effective moment of inertia of RC beams

Patel

Bhardwaj

Chaudhary

et al. 2015

Lat. Am. j. solids struct.

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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%

“…6 A typical neural network model recognition, adaptive learning, self-organization and real time operation. The back propagation algorithm has been used successfully for many structural engineering applications (Gupta et al 2007, 2013, Kanwar et al 2007, Mohammadhassani et al 2013a, Chaudhary et al 2007, Pendharkar et al 2007, Tadesse et al 2012 and is considered as one of the efficient algorithms in engineering applications (Hsu et al 1993). One hidden layer is chosen and the number of neurons in the layer are decided in the learning process by trial and error.…”

Section: Configuration Of Neural Network Modelsmentioning

confidence: 99%

Rapid prediction of long-term deflections in composite frames

Pendharkar¹,

Patel²,

Chaudhary³

et al. 2015

Steel Compos. Struct.

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Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

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