Rapid prediction of moments in highrise composite frames considering cracking and time-effects

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

doi:10.3311/ppci.8210

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…Jasim and Al-Jabbri discussed the application of artificial neural network in the design of continuous composite beams by the plastic method [ 14 ]. Pendharkar et al used the neural network model to quickly predict the inelastic moment in the high-rise composite frame [ 15 ]. Kalibhat and Upadhyay proposed two different cross section improvers and the proposed simplified numerical program that has advantages in flexibility, versatility, and ease of use [ 16 ].…”

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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“…nd floor of a 72-storey high-rise building for automatic and real-time measurements of the shortening of vertical members to achieve more precise construction. Alternatively, Pendharkar et al [21] developed a neural network model for rapid prediction of the inelastic behavior. Indeed it was reported that the methodology presented in their study could be extended for further rapid prediction of inelastic deflections in high-rise frames in which the effect of axial shortening of the columns is significant.…”

mentioning

confidence: 99%

A Practical Compensation Method for Differential Column Shortenings in High-rise Reinforced Concrete Buildings

Seçer

Zamani

İşler

2020

Period. Polytech. Civil Eng.

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High-rise reinforced concrete buildings have technical, economic and environmental advantages for high density development and they have become a distinctive feature for densely populated urban areas around the world. For this purpose, structural design of high-rise reinforced concrete buildings have become forward and particularly serviceability requirements gained more interest. Differential shortening of vertical members is one of the serviceability requirements; however, only a limited number of studies exist. In this study, a practical compensation method was proposed for the differential shortening of columns and shear walls in high-rise reinforced concrete buildings. In the proposed compensation method, vertical members were grouped and the total error was aimed to be minimized by penalizing the higher shortening differences in the groups to simplify the process of building construction. In order to validate the proposed method, a 32-storey high-rise building that was built in Izmir Turkey was investigated considering both the construction sequence and time-dependent effects as shrinkage and creep. Vertical shortening of columns and shear walls in the tower part of the building were calculated. Uniform-grouped compensation method and the proposed penalized errors compensation method with using L1-norm and L2-norm were applied for differential shortenings of columns and shear walls with considering different numbers of member groups. The magnitude of errors for each compensation method was presented and evaluated. Results of the numerical study reveal that the proposed penalized errors compensation method was capable of determining the compensation errors by minimizing the maximum errors efficiently.

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Rapid prediction of moments in highrise composite frames considering cracking and time-effects

Abstract: Abstract

Cited by 2 publications

References 29 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

A Practical Compensation Method for Differential Column Shortenings in High-rise Reinforced Concrete Buildings

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