纤维增强复材约束矩形混凝土轴压强度和极限压应变计算方法研究

黄新良; 陈文广; 谭佩; 徐金俊; 钱四江

doi:10.13204/j.gyjzG21021601

纤维增强复材约束矩形混凝土轴压强度和极限压应变计算方法研究

doi: 10.13204/j.gyjzG21021601

1. 浙江汉林建筑设计有限公司, 杭州 310000;
2. 南京工业大学土木工程学院, 南京 211816;
3. 北京市古代建筑设计研究所有限公司, 北京 100009

基金项目:

国家自然科学基金项目（51708289）；国家级大学生创新创业训练计划项目（202110291040Z，202110291096Z）；江苏省大学生创新创业训练计划项目（202110291227Y）

详细信息

作者简介:
黄新良,男,1979年出生,硕士,高级工程师

通讯作者:
徐金俊,男,1986年出生,博士,副教授,jjxu_concrete@njtech.edu.cn。

计量
- 文章访问数: 245
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- 被引次数: 0
出版历程
- 收稿日期: 2021-02-16
- 网络出版日期: 2022-02-21

RESEARCH ON CALCULATION METHOD FOR AXIAL COMPRESSIVE STRENGTH AND ULTIMATE COMPRESSIVE STRAIN OF RECTANGULAR CONCRETE CONFINED WITH FRP

1. Zhejiang Hanlin Architectural Design Co., Ltd., Hangzhou 310000, China;
2. College of Civil Engineering, Nanjing Tech University, Nanjing 211816, China;
3. Beijing Ancient Architectural Design and Research Institute Co., Ltd., Beijing 100009, China

摘要

摘要: 轴压荷载下纤维增强复材（FRP）约束混凝土应力-应变本构关系可分为强化型和软化型两类约束模型，其在极限状态时所固有的轴压应力及应变构成了各自本构的参数基础，准确计算这些本构参数可为FRP约束混凝土结构的性能评估提供判别依据。通过对现有FRP约束矩形混凝土抗压强度和极限压应变经验模型的性能进行综合评价，表明已有经验模型普遍存在通用性差、预测准确性低以及离散性大等问题。针对传统预测模型的局限性，基于反向传播神经网络，分别建立了强化型和软化型FRP约束矩形混凝土抗压强度和极限压应变预测模型。研究结果表明：神经网络模型不仅能反映各类控制参数对轴压应力及应变的影响，且相比于已有经验模型，基于神经网络模型的计算值和试验值吻合更好，偏差和随机性都显著减小，保证了预测结果的准确性和稳定性。
- FRP约束混凝土 /
- 抗压强度 /
- 极限压应变 /
- 矩形柱 /
- 神经网络
Abstract: The stress-strain constitutive relation of FRP-confined concrete can be divided into two categories of constraint models under axial load:work hardening and softening. The intrinsic axial compressive stress and strain in the ultimate state constitute the parameter basis of their constitutions. Calculating these constitutive parameters accurately can provide a discrimination criteria for evaluating the performance of FRP-confined concrete structures. The performance of the existing empirical models about compressive strength and ultimate compressive strain of rectangular concrete confined with FRP was evaluated comprehensively, and the results showed that the existing empirical models generally showed poor universality, low prediction accuracy and high dispersion. Aiming at the limitation of traditional prediction models, the prediction models of compressive strength and ultimate compressive strain for both hardening and softening types of rectangular concrete confined with FRP were constructed respectively based on back-propagation artificial neural network. The research results indicated that the neural network models could not only reflect the influence of various control parameters on the axial compressive stress and strain, but also the calculated values based on the neural network models were in better agreement with the experimental values when compared with the existing empirical models; moreover, the deviation and randomness were significantly decreased,so as to ensure the accuracy and stability of prediction results.
- FRP-confined concrete /
- compressive strength /
- ultimate compressive strain /
- rectangular column /
- neural network

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