Bending Moment Prediction for Continuous Composite Beams by Neural Networks

Chaudhary, Sandeep; Pendharkar, Umesh; Nagpal, A. K.

doi:10.1260/136943307783239390

Cited by 16 publications

(6 citation statements)

References 25 publications

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“…Chaudhary et al proposed a method to predict the inelastic moment of continuous composite beams from elastic moment using neural network. The proposed neural network model predicts the inelastic moment ratio at the span support [ 12 ]. Kumar et al proposed a closed form expression to quickly predict the long-term deflection of simply supported steel-concrete composite bridges under service load.…”

Section: Related Workmentioning

confidence: 99%

Numerical Analysis of Flexural Behavior of Prestressed Steel-Concrete Continuous Composite Beams Based on BP Neural Network

Pan

Shen³

et al. 2022

Computational Intelligence and Neuroscience

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Prestressed steel-concrete continuous composite beam (PCCB) is a kind of beam, which makes reinforced concrete slab and steel beam bear load and coordinate deformation through connectors such as studs. Prestressed steel-concrete continuous composite beam is a kind of transverse load-bearing composite member formed by prestressed technology on the basis of ordinary composite beam. Aiming at the flexural behavior of prestressed steel-concrete continuous composite beams, a three-dimensional finite element numerical analysis model is established, and the whole process of the test is simulated based on BP neural network. The calculated results are in good agreement with the test. Using this model, the mechanical deformation performance of prestressed steel-concrete continuous composite beam is further analyzed, and the effects of some parameters (steel beam strength grade, concrete strength grade, concrete slab thickness, and transverse reinforcement ratio) on the flexural performance of prestressed steel-concrete continuous composite beam are discussed, which provides a reference basis for engineering design.

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Section: Related Workmentioning

confidence: 99%

Numerical Analysis of Flexural Behavior of Prestressed Steel-Concrete Continuous Composite Beams Based on BP Neural Network

Pan

Shen³

et al. 2022

Computational Intelligence and Neuroscience

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“…Some of the applications of neural networks in literature, in the field of structural engineering, include prediction of various structural quantities [11], [12]. Papers [13], [14] present the application of ANNs to predict bending moment in continuous composite beams. There are some papers that present the application of artificial neural networks to predict the deflection of structural elements.…”

Section: Artificial Neural Network As a Tool For Predictionmentioning

confidence: 99%

Application of artificial neural networks to predict the deflections of reinforced concrete beams

Kaczmarek

Szymańska

2016

Studia Geotechnica Et Mechanica

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Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies.Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

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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%

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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Bending Moment Prediction for Continuous Composite Beams by Neural Networks

Cited by 16 publications

References 25 publications

Numerical Analysis of Flexural Behavior of Prestressed Steel-Concrete Continuous Composite Beams Based on BP Neural Network

Numerical Analysis of Flexural Behavior of Prestressed Steel-Concrete Continuous Composite Beams Based on BP Neural Network

Application of artificial neural networks to predict the deflections of reinforced concrete beams

Explicit expression for effective moment of inertia of RC beams

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