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碳纤维增强复合材料(CFRP)加固钢结构承载能力的分析与评估

Analysis and Evaluation of Load-Carrying Capacity of CFRP-Reinforced Steel Structures.

作者信息

Zhao Jian, Huang Yongxing, Gong Kun, Wen Zhiguo, Liu Sinan, Hou Yanyan, Hong Xuewu, Tong Xuecheng, Shi Kai, Qu Ziyi

机构信息

School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, China.

Tianjin Puret Purification Technology Co., Ltd., Tianjin 301814, China.

出版信息

Polymers (Basel). 2024 Sep 23;16(18):2678. doi: 10.3390/polym16182678.

DOI:10.3390/polym16182678
PMID:39339140
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11435699/
Abstract

Carbon Fiber Reinforced Polymer (CFRP) can be used to reinforce steel structures depending on its high strength and lightweight resistance. To analyze and evaluate the load-carrying capacity of CFRP-reinforced steel structures. This study uses the Finite Element Analysis (FEA) and the experimental tests combined to investigate the influence that the reinforcement patterns and the relevant parameters have on the load-carrying capacity. We made specimens with different reinforcement patterns. Take the steel beam specimen with full reinforcement as an example. Compared with the load-carrying capacity of the steel beam reinforced by two-layer CFRP cloth, that respectively increases by 5.16% and 11.1% when the number of the CFRP cloth increases to four and six, respectively. Based on a specimen set consisting of CFRP-reinforced steel structures under different reinforcement patterns, the random forest algorithm is used to develop an evaluation model for the load carrying. The performance test results show that the MAE (Mean Absolute Error) of the evaluation model can reach 0.12 and the RMSE (Root Mean Square Error) is 0.25, presenting a good prediction accuracy, which lays a solid foundation for the research on the CFRP-based reinforcement technology and process.

摘要

碳纤维增强聚合物(CFRP)因其高强度和轻质特性可用于加固钢结构。为了分析和评估CFRP加固钢结构的承载能力。本研究采用有限元分析(FEA)和实验测试相结合的方法,研究加固方式和相关参数对承载能力的影响。我们制作了具有不同加固方式的试件。以全加固钢梁试件为例。与两层CFRP布加固的钢梁承载能力相比,当CFRP布层数分别增加到四层和六层时,承载能力分别提高了5.16%和11.1%。基于由不同加固方式的CFRP加固钢结构组成的试件集,使用随机森林算法建立了承载能力评估模型。性能测试结果表明,评估模型的平均绝对误差(MAE)可达0.12,均方根误差(RMSE)为0.25,具有良好的预测精度,为基于CFRP的加固技术和工艺研究奠定了坚实基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/c385d1af6cbd/polymers-16-02678-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/c385d1af6cbd/polymers-16-02678-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/6e1c1e1467f4/polymers-16-02678-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/f5034ee3142b/polymers-16-02678-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/89ec4c5fa839/polymers-16-02678-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/c64963c5b080/polymers-16-02678-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/514a0f629825/polymers-16-02678-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/925a56182d35/polymers-16-02678-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/30053758ac2f/polymers-16-02678-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/1df506f02221/polymers-16-02678-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/e796d43c37bb/polymers-16-02678-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f817/11435699/c385d1af6cbd/polymers-16-02678-g012.jpg

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本文引用的文献

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Mechanistic Study of Failure in CFRP Hybrid Bonded-Bolted Interference Connection Structures under Tensile Loading.CFRP 混合粘结-螺栓干涉连接结构拉伸载荷下失效的机理研究
Materials (Basel). 2024 Apr 30;17(9):2117. doi: 10.3390/ma17092117.
3
Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models.
使用人工神经网络和随机森林预测模型估算纤维增强塑料(FRP)加固梁的抗弯强度
Polymers (Basel). 2022 Jun 2;14(11):2270. doi: 10.3390/polym14112270.
4
An approach for predicting the compressive strength of cement-based materials exposed to sulfate attack.一种预测遭受硫酸盐侵蚀的水泥基材料抗压强度的方法。
PLoS One. 2018 Jan 18;13(1):e0191370. doi: 10.1371/journal.pone.0191370. eCollection 2018.