A Prediction Model for Bond Strength Between Concrete and Steel Bars Based on Bayesian Neural Networks

WANG Hai-bo

doi:10.13204/j.gyjzg22031516

Volume 52 Issue 9

Sep. 2022

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Abstract

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WANG Hai-bo. A Prediction Model for Bond Strength Between Concrete and Steel Bars Based on Bayesian Neural Networks[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(9): 87-93. doi: 10.13204/j.gyjzg22031516

Citation:

WANG Hai-bo. A Prediction Model for Bond Strength Between Concrete and Steel Bars Based on Bayesian Neural Networks[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(9): 87-93. doi: 10.13204/j.gyjzg22031516

WANG Hai-bo. A Prediction Model for Bond Strength Between Concrete and Steel Bars Based on Bayesian Neural Networks[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(9): 87-93. doi: 10.13204/j.gyjzg22031516

Citation:

WANG Hai-bo. A Prediction Model for Bond Strength Between Concrete and Steel Bars Based on Bayesian Neural Networks[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(9): 87-93. doi: 10.13204/j.gyjzg22031516

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A Prediction Model for Bond Strength Between Concrete and Steel Bars Based on Bayesian Neural Networks

doi: 10.13204/j.gyjzg22031516

WANG Hai-bo

The Engineering Design Academy of Chang'an University Co., Ltd., Xi'an 710064, China

Received Date: 2022-03-15
Available Online: 2023-02-06

Abstract

Abstract

A new calculation method of bond strength between steel bars and concrete with different aggregate types was proposed. Combined with 748 groups of test data and the introduction to dynamic renewal theories of the neural network, a prediction model with multi parameters for bond strength based on neural network method was established, the significance analysis of influencing factors of bond strength was conducted, the prediction model was simplified by the parameter elimination method of the neural network, and the formula for critical anchorage length was put forward. Important factors that influenced bond performances were tensile strength, anchorage length, diameters of steel bars, and thickness of concrete covers. The proposed model was of higher prediction accuracy and the mean value and coefficient of variation for the ratio of the prediction values to test values were 1.056 and 0.377 respectively. It provided a new method to predict bond strength between steel bars and concrete.
- aggregate type,
- bond strength,
- bayesian ANNs,
- significance analysis for parameter,
- prediction model

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References(42)

References

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