基于贝叶斯神经网络的钢筋与混凝土黏结强度预测模型研究

王海波

doi:10.13204/j.gyjzg22031516

基于贝叶斯神经网络的钢筋与混凝土黏结强度预测模型研究

doi: 10.13204/j.gyjzg22031516

王海波

西安长安大学工程设计研究院有限公司, 西安 710064

详细信息

作者简介:
王海波,男,1972年出生,工程师。电子信箱:642560953@qq.com

计量
- 文章访问数: 88
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- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2022-03-15
- 网络出版日期: 2023-02-06

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

WANG Hai-bo

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

摘要

摘要: 提出一种新的钢筋与不同种类骨料混凝土间黏结强度的计算方法。结合748组试验数据,引入神经网络动态更新理论,建立了基于神经网络方法的黏结强度多参数预测模型,通过对黏结强度影响因素显著性的分析,利用神经网络参数剔除法简化了预测模型,并提出了临界锚固长度计算式。结果表明:钢筋与混凝土黏结强度主要受混凝土抗拉强度、锚固长度、钢筋直径和混凝土保护层厚度等因素的影响;建议模型具有较高预测精度,模型预测值与试验值比值的平均值和变异系数分别为1.052和0.337,为预测钢筋与混凝土黏结强度提供了新思路。
- 骨料种类 /
- 黏结强度 /
- 贝叶斯神经网络 /
- 参数显著性分析 /
- 预测模型
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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