Automatic assessment of CFRP-steel interfacial performance under adhesive curing using PZT-based EMI-integrated deep learning technique

被引:0
作者
Deng, Jun [1 ]
Wu, Xingpei [1 ]
Li, Xiaoda [1 ,2 ]
Qin, Yang [1 ]
Zhong, Kaijin [1 ]
机构
[1] Guangzhou Univ, Sch Civil Engn, Guangzhou 510006, Peoples R China
[2] Guangzhou Univ, Huangpu Res Inst, Guangzhou 510006, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
CFRP-strengthened notched steel beam; Adhesive curing; EMI technique; Automatic assessment; Deep learning;
D O I
10.1016/j.tws.2024.112894
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Although externally bonding FRP with adhesive has been widely adopted for structural strengthening, the rapid and accurate prediction of early-age interfacial performance in CFRP-strengthened steel structures remained challenging. This study investigated the interfacial performance of CFRP-strengthened notched steel beams over curing periods ranging from 3 to 168 h, utilizing two types of adhesives with conventional curing (CC) and rapid curing (RC) rates. Furthermore, an electromechanical impedance (EMI)-integrated deep learning (DL) approach, based on a convolutional neural network-long short-term memory-sparrow search algorithm (CNN-LSTM-SSA) model, was developed to automatically predict bond-slip characteristic parameters at various curing stages using raw EMI responses. The results revealed that the interfacial performance varied significantly and generally improved with increasing curing time. The maximum shear stress was peaked at 72 h and 48 h for the CC and RC series specimens, respectively, with the maximum improvement reaching 238.32 % during the curing period. Moreover, the proposed model accurately predicted early-stage interfacial performance, achieving R2 values of 0.98, 0.94, and 0.97 for initial stiffness, fracture energy, and maximum shear stress, respectively. Additionally, the proposed network outperformed traditional machine learning and deep learning methods in terms of prediction accuracy, strong noise resistance (5dB), and robustness. These findings highlight the significant potential of the proposed method for the rapid and accurate estimation of early-age interfacial performance in FRPstrengthened structures.
引用
收藏
页数:18
相关论文
共 71 条
  • [1] Observations of the Cabibbo-Suppressed decays Λc+ → nπ+ π0, nπ+ π- π+ and the Cabibbo-Favored decay Λc+ → nK- π+ π+*
    Ablikim, M.
    Achasov, M. N.
    Adlarson, P.
    Albrecht, M.
    Aliberti, R.
    Amoroso, A.
    An, M. R.
    An, Q.
    Bai, Y.
    Bakina, O.
    Ferroli, R. Baldini
    Balossino, I
    Ban, Y.
    Batozskaya, V
    Becker, D.
    Begzsuren, K.
    Berger, N.
    Bertani, M.
    Bettoni, D.
    Bianchi, F.
    Bianco, E.
    Bloms, J.
    Bortone, A.
    Boyko, I
    Briere, R. A.
    Brueggemann, A.
    Cai, H.
    Cai, X.
    Calcaterra, A.
    Cao, G. F.
    Cao, N.
    Cetin, S. A.
    Chang, J. F.
    Chang, W. L.
    Che, G. R.
    Chelkov, G.
    Chen, C.
    Chen, Chao
    Chen, G.
    Chen, H. S.
    Chen, M. L.
    Chen, S. J.
    Chen, S. M.
    Chen, T.
    Chen, X. R.
    Chen, X. T.
    Chen, Y. B.
    Chen, Z. J.
    Cheng, W. S.
    Choi, S. K.
    [J]. CHINESE PHYSICS C, 2023, 47 (02)
  • [2] Deep learning of electromechanical admittance data augmented by generative adversarial networks for flexural performance evaluation of RC beam structure
    Ai, Demi
    Zhang, Rui
    [J]. ENGINEERING STRUCTURES, 2023, 296
  • [3] Deep learning of electromechanical impedance for concrete structural damage identification using 1-D convolutional neural networks
    Ai, Demi
    Mo, Fang
    Cheng, Jiabao
    Du, Lixun
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2023, 385
  • [4] A deep learning approach for electromechanical impedance based concrete structural damage quantification using two-dimensional convolutional neural network
    Ai, Demi
    Cheng, Jiabao
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 183
  • [5] [Anonymous], 2021, GB/T 2567
  • [6] [Anonymous], 2011, Technical Code for Safety Appraisal of Engineering Structural Strengthening Materials
  • [7] Fiber-Reinforced Polymer Strengthening of Steel Beams under Static and Fatigue Loadings
    Bagale, Bibek Regmi
    Parvin, Azadeh
    [J]. PRACTICE PERIODICAL ON STRUCTURAL DESIGN AND CONSTRUCTION, 2021, 26 (01)
  • [8] Observation of the decay ψ(2S) → K0SK0L -: art. no. 052001
    Bai, JZ
    Ban, Y
    Bian, JG
    Cai, X
    Chang, JF
    Chen, HF
    Chen, HS
    Chen, HX
    Chen, J
    Chen, JC
    Chen, J
    Chen, ML
    Chen, YB
    Chi, SP
    Chu, YP
    Cui, XZ
    Dai, HL
    Dai, YS
    Deng, ZY
    Dong, LY
    Du, SX
    Du, ZZ
    Fang, J
    Fang, SS
    Fu, CD
    Fu, HY
    Fu, LP
    Gao, CS
    Gao, ML
    Gao, YN
    Gong, MY
    Gong, WX
    Gu, SD
    Guo, YN
    Guo, YQ
    Guo, ZJ
    Han, SW
    Harris, FA
    He, J
    He, KL
    He, M
    He, X
    Heng, YK
    Hu, HM
    Hu, T
    Huang, GS
    Huang, L
    Huang, XP
    Ji, XB
    Jia, QY
    [J]. PHYSICAL REVIEW LETTERS, 2004, 92 (05) : 5
  • [9] Analytical solution of the full-range behavior of adhesively bonded FRP-steel joints made with toughened adhesives
    Calabrese, Angelo Savio
    Colombi, Pierluigi
    D'Antino, Tommaso
    [J]. ENGINEERING FRACTURE MECHANICS, 2023, 292
  • [10] Diagnostic imaging of debonding in FRP-strengthened reinforced concrete structures using combinational harmonics generated by Rayleigh waves
    Cao, Yuqiao
    Hu, Xianwen
    Ng, Ching Tai
    Smith, Scott T.
    [J]. ENGINEERING STRUCTURES, 2024, 314