Shear Capacity Prediction of Reactive Powder Concrete Beams Based on Neural Network

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Shear Capacity Prediction of Reactive Powder Concrete Beams Based on Neural Network

Journal Title: 河南科技大学学报（自然科学版） - Year 2017, Vol 38, Issue 2

Abstract

In order to explore the effectiveness of back-porpagation( BP) neural network shear capacity prediction of simply supported reactive powder concrete( RPC) beams,the shearing failure test results of the 14high-strength reinforced RPC beams was used to analyze the 4 main factors that affected the shear capacity of RPC simply supported beams. The BP neural network prediction model of RPC beams was established and its reliability was verified. The effects of various parameters on the shear capacity of high-strength reinforced RPC beams were analyzed by using the model. The results show that if the shear span ratio is more than 3,the effects of shear span ratio on shear capacity of RPC beams become gentle. The shear capacity of RPC beams increases with the improvement of longitudinal reinforcement ratio. The stirrup ratio improve more shear capacity of the RPC beams with large shear span ratio than that with small shear span ratio.

Authors and Affiliations

Xia CAO, Huayang WANG, Yi ZHANG, Jindan ZHANG

Keywords

reactive powder concrete; high strength steel; BP neural network; shear capacity;

EP ID EP477835
DOI -
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