Closed-form Expressions for Long-term Deflections in High-rise Composite Frames

Umesh Pendharkar,K. A. Patel,Sandeep Chaudhary,A.K. Nagpal 한국강구조학회 2017 International Journal of Steel Structures Vol.17 No.1

This paper presents closed-form expressions for rapid prediction of long-term deflections in high-rise steel concrete composite frames subjected to service load. The closed-form expressions 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). The expressions also take into account the sagging moments developed in beams due to the substantial differential shortening of adjacent columns in high-rise frames. The expressions can be used for frames with any number of bays and storeys. The expressions have been obtained from trained neural networks. The training, validating, and testing data sets for the neural networks are generated using a hybrid analyticalnumerical procedure of analysis. The proposed expressions are verified for example frames of different number of spans and storeys and the errors are shown to be small. The expressions can be used in every day design as they enable a rapid prediction of inelastic deflections with reasonable accuracy for practical purposes without detailed complex analysis and require computational effort that is a fraction of that required for the available methods.

Prediction of moments in composite frames considering cracking and time effects using neural network models

Pendharkar, Umesh,Chaudhary, Sandeep,Nagpal, A.K. Techno-Press 2011 Structural Engineering and Mechanics, An Int'l Jou Vol.39 No.2

There can be a significant amount of moment redistribution in composite frames consisting of steel columns and composite beams, due to cracking, creep and shrinkage of concrete. Considerable amount of computational effort is required for taking into account these effects for large composite frames. A methodology has been presented in this paper for taking into account these effects. In the methodology that has been demonstrated for moderately high frames, neural network models are developed for rapid prediction of the inelastic moments (typically for 20 years, considering instantaneous cracking, and time effects, i.e., creep and shrinkage, in concrete) at a joint in a frame from the elastic moments (neglecting instantaneous cracking and time effects). The proposed models predict the inelastic moment ratios (ratio of elastic moment to inelastic moment) using eleven input parameters for interior joints and seven input parameters for exterior joints. The training and testing data sets are generated using a hybrid procedure developed by the authors. The neural network models have been validated for frames of different number of spans and storeys. The models drastically reduce the computational effort and predict the inelastic moments, with reasonable accuracy for practical purposes, from the elastic moments, that can be obtained from any of the readily available software.

Rapid prediction of long-term deflections in composite frames

Umesh Pendharkar,Sandeep Chaudhary,K. A. Patel,A.K. Nagpal 국제구조공학회 2015 Steel and Composite Structures, An International J Vol.18 No.3

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.

Neural networks for inelastic mid-span deflections in continuous composite beams

Umesh Pendharkar,Sandeep Chaudhary,A.K. Nagpal 국제구조공학회 2010 Structural Engineering and Mechanics, An Int'l Jou Vol.36 No.2

Maximum deflection in a beam is a design criteria and occurs generally at or close to the mid-span. Neural networks have been developed for the continuous composite beams to predict the inelastic mid-span deflections (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 instantaneous cracking and time effects). The training and testing data for the neural networks is generated using a hybrid analytical-numerical procedure of analysis. The neural networks have been validated for four example beams and the errors are shown to be small. This methodology, of using networks enables a rapid estimation of inelastic mid-span deflections and requires a computational effort almost equal to that required for the simple elastic analysis. The neural networks can be extended for the composite building frames that would result in huge saving in computational time.

Prediction of moments in composite frames considering cracking and time effects using neural network models

Umesh Pendharkar,Sandeep Chaudhary,A.K. Nagpal 국제구조공학회 2011 Structural Engineering and Mechanics, An Int'l Jou Vol.39 No.2

There can be a significant amount of moment redistribution in composite frames consisting of steel columns and composite beams, due to cracking, creep and shrinkage of concrete. Considerable amount of computational effort is required for taking into account these effects for large composite frames. A methodology has been presented in this paper for taking into account these effects. In the methodology that has been demonstrated for moderately high frames, neural network models are developed for rapid prediction of the inelastic moments (typically for 20 years, considering instantaneous cracking, and time effects, i.e., creep and shrinkage, in concrete) at a joint in a frame from the elastic moments (neglecting instantaneous cracking and time effects). The proposed models predict the inelastic moment ratios (ratio of elastic moment to inelastic moment) using eleven input parameters for interior joints and seven input parameters for exterior joints. The training and testing data sets are generated using a hybrid procedure developed by the authors. The neural network models have been validated for frames of different number of spans and storeys. The models drastically reduce the computational effort and predict the inelastic moments, with reasonable accuracy for practical purposes, from the elastic moments, that can be obtained from any of the readily available software.

Neural networks for inelastic mid-span deflections in continuous composite beams

Pendharkar, Umesh,Chaudhary, Sandeep,Nagpal, A.K. Techno-Press 2010 Structural Engineering and Mechanics, An Int'l Jou Vol.36 No.2

Maximum deflection in a beam is a design criteria and occurs generally at or close to the mid-span. Neural networks have been developed for the continuous composite beams to predict the inelastic mid-span deflections (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 instantaneous cracking and time effects). The training and testing data for the neural networks is generated using a hybrid analytical-numerical procedure of analysis. The neural networks have been validated for four example beams and the errors are shown to be small. This methodology, of using networks enables a rapid estimation of inelastic mid-span deflections and requires a computational effort almost equal to that required for the simple elastic analysis. The neural networks can be extended for the composite building frames that would result in huge saving in computational time.

An analytical-numerical procedure for cracking and time-dependent effects in continuous composite beams under service load

A.K. Nagpal,Sandeep Chaudhary,Umesh Pendharkar 국제구조공학회 2007 Steel and Composite Structures, An International J Vol.7 No.3

An analytical-numerical procedure has been presented in this paper to take into account the nonlinear effects of concrete cracking and time-dependent effects of creep and shrinkage in the concrete portion of the continuous composite beams under service load. The procedure is analytical at the element level and numerical at the structural level. The cracked span length beam element consisting of uncracked zone in middle and cracked zones near the ends has been proposed to reduce the computational effort. The progressive nature of cracking of concrete has been taken into account by division of the time into a number of time intervals. Closed form expressions for stiffness matrix, load vector, crack lengths and mid-span deflection of the beam element have been presented in order to reduce the computational effort and bookkeeping. The procedure has been validated by comparison with the experimental and analytical results reported elsewhere and with FEM. The procedure can be readily extended for the analysis of composite building frames where saving in computational effort would be very considerable.