Rapid Prediction of Long-term Deflections in Steel-Concrete Composite Bridges Through a Neural Network Model

Kumar, Sushil; Patel, K. A.; Chaudhary, Sandeep; Nagpal, A. K.

doi:10.1007/s13296-021-00458-1

Cited by 10 publications

(1 citation statement)

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“…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. The expression considers the flexibility, shear lag effect, and time effect of shear connectors in concrete [ 13 ]. Jasim and Al-Jabbri discussed the application of artificial neural network in the design of continuous composite beams by the plastic method [ 14 ].…”

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%