Study of the Influence of Cracks on the Bearing Capacity of Reinforced Concrete Plates Using the Prins Calculation Complex

Aydemirov, K. R.; Агапов, В. П.

doi:10.21822/2073-6185-2019-46-4-143-154

Cited by 1 publication

(1 citation statement)

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“…Due to the particularity of architectural problems, intelligent algorithms such as neural networks have also begun to attract the attention of architectural engineers 1,2 . Usually in construction, the repetitive work of selecting the structure, analyzing the structure and drawing good drawings not only enhances the work of analysis and calculation, but also requires the structural engineer to have the ability to judge and innovate the past, and to fully implement it; some old buildings are going through After some disasters (such as fires and earthquakes), they are often damaged in various forms, so their safety, serviceability and durability structures are getting worse and worse 3,4 .…”

Section: Introductionmentioning

confidence: 99%

Automatic testing system for ceramsite concrete bearing capacity based on genetic algorithm

Liu

Xue

Zhao

et al. 2023

International Conference on Computer Application and Information Security (ICCAIS 2022)

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At present, the research work on recycled concrete is still relatively slow and backward, and many problems are not studied in detail enough. It only stays at the material level, and there is no in-depth research on the performance of recycled concrete structures. The purpose of this paper is to study the automatic testing system of ceramsite concrete bearing capacity based on genetic algorithm. A prediction model for the flexural and shear resistance of RC beams after high temperature, and the bearing capacity of RC columns after high temperature, based on BP neural network and GA-BP neural network, was established to prove the effectiveness and efficiency of the automatic test system based on ceramsite concrete bearing capacity accuracy. The overall structure of the system is analyzed, and the following conclusions are obtained: Compared with the BP neural network prediction model, the flexural and shear bearing capacity of the RC beam based on the GA-BP neural network after high temperature and the axial compression of the RC column after high temperature are analyzed. The relative error, absolute error, average error and root mean square of the predicted data obtained by the bias bearing capacity prediction model and the theoretical calculation value are smaller.

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