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Closed-form expressions for long-term deflections in high-rise composite frames
Abstract 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 analytical-numerical 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.
Closed-form expressions for long-term deflections in high-rise composite frames
Abstract 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 analytical-numerical 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.
Closed-form expressions for long-term deflections in high-rise composite frames
Pendharkar, Umesh (author) / Patel, K. A. (author) / Chaudhary, Sandeep (author) / Nagpal, A. K. (author)
2017
Article (Journal)
English
Closed-form expressions for long-term deflections in high-rise composite frames
| Springer Verlag | 2017
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