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A cracked span length frame element has been proposed for service load analysis of composite frames consisting of steel columns and steel-concrete composite beams. The element comprises a maximum of three regions (cracked or uncracked) with simplified formulation and is applicable for all types of loading. This element has been used in an analytical-numerical procedure for the inelastic analysis of such composite frames, subjected to service loads. Analytical expressions have been derived for flexibility and stiffness coefficients, end displacements, load vector and mid span deflections of the cracked span length frame element. Average values over the cracked regions have been used for the tension stiffening characteristics, so as to retain the analytical nature of the procedure at the element level. The procedure uses an iterative technique for establishing the cracked region lengths and the distribution coefficients (for tension stiffening), and yields the inelastic deflections and redistributed moments. The procedure using the proposed element has been validated by comparing the results with experimental results available in literature and also with the results obtained from finite element analysis. The procedure requires a fraction of the computational effort that is required for the numerical methods available in literature and gives sufficiently accurate results. Therefore, the reduction in the computational efforts in the case of high-rise composite frames would be considerable. [ABSTRACT FROM AUTHOR]

For instantaneous non-linear analysis of steel concrete composite bridges that are subjected to a service load, a cracked span length beam element has been proposed. The element consists of two cracked zones at the ends and one uncracked zone between the cracked zones. The element has been used to develop a hybrid analytical–numerical procedure for composite bridges. The procedure yields crack lengths and deflections as well as redistributed moments. The procedure would lead to a considerable reduction in the computational effort in the case of continuous steel concrete composite bridges that consist of a large number of spans. [ABSTRACT FROM AUTHOR]

Abstract: Simply supported steel-concrete composite beams are widely used in bridge construction. Deflection is the significant parameter for serviceability limit state of bridges. A lot of computational effort is required for finite element analysis of bridges considering flexibility of shear connectors and shear leg effects. Neural network is presented for prediction of deflections, at service load, in simply supported steel-concrete composite bridges incorporating flexibility of shear connectors and shear lag effect. The training, testing and validation data sets for neural network are generated using finite element models. The finite element models have been developed using ABAQUS software. These models have been validated with available experimental results. Closed form solution is also proposed based on the developed neural network. The use of the neural network requires a computational effort almost equal to that required for the simple beam analysis (neglecting flexibility of shear connectors and shear lag effect). The neural network has been validated for number of bridges and the errors are found to be small. The network/closed form solution can be used for rapid prediction of deflection for everyday design. [Copyright &y& Elsevier]

Abstract: Neural networks have been developed for prediction of deflections, at service load, in steel-concrete composite bridges incorporating flexibility of shear connectors, shear lag effect and cracking in concrete slabs. Three neural networks have been presented to cover simply supported bridges, two span continuous bridges and three span continuous bridges. The use of the neural networks requires a computational effort almost equal to that required for the simple beam analysis (neglecting flexibility of shear connectors, shear lag effect and cracking of concrete). The training and testing data for neural networks are generated using finite element software ABAQUS. The neural networks have been validated for number of bridges and the errors are found to be small. Closed form solutions are also proposed based on the developed neural networks. The networks/ closed form solutions can be used for rapid prediction of deflection for everyday design. [Copyright &y& Elsevier]

• Residual properties of rubberized concrete were investigated. • ANN models are proposed to predict the residual performance of rubberized concrete. • The ANN outputs show good agreement with experimental results. Considering scarcity of natural sand, waste rubber tyre can be an alternate ingredient for replacement of conventional fine aggregates in the production of concrete. Use of the waste rubber tyre in building materials is beneficial from sustainable and economical points of view. A systematic and comprehensive experimental study was conducted earlier by the authors for the mechanical and durable properties of rubberised concrete subjected to elevated temperature. However, there is non-availability of a mathematical model for rapid prediction of mechanical properties of the rubberised concrete subjected to elevated temperature. To bridge this gap an attempt has been made for development of explicit expressions through artificial neural network (ANN) approach in this paper. The training, validation, and testing data sets for ANN, are compiled from the recent researches of the authors. The input data sets contain six levels of elevated temperature (T) with three exposure durations (t) for all the specimens having six different fiber content (RF) along with three different water-cement ratio (w / c). On the other hand, the output parameters consist of mechanical properties (compressive strength static modulus of elasticity, dynamic modulus of elasticity and mass loss). Sensitivity analysis has also been carried out to investigate the effect of the input parameters on the output parameters. It is found that the average contribution of w / c , R F , T , t to all the output parameter is 6.67%, 10.10%, 80.01% and 3.22% respectively. The parameter T has highest impact on the all output parameters followed by RF whereas, rest of the input parameters (w / c , t) have relatively lower impact. [ABSTRACT FROM AUTHOR]