Localized corrosion induced damage monitoring of large-scale RC piles using acoustic emission technique in the marine environment

被引:35
作者
Zheng, Yonglai [1 ]
Zhou, Yujue [1 ]
Zhou, Yubao [1 ]
Pan, Tanbo [1 ]
Sun, Limin [2 ]
Liu, Don [3 ]
机构
[1] Tongji Univ, Civil Engn Coll, Dept Hydraul Engn, Shanghai, Peoples R China
[2] Tongji Univ, Civil Engn Coll, Dept Bridge Engn, Shanghai, Peoples R China
[3] Louisiana Tech Univ, Dept Engn Mech, Ruston, LA 71270 USA
关键词
Localized corrosion; Piles; Marine environment; Acoustic emission (AE); Machine learning; FIBER-REINFORCED POLYMER; CONCRETE; STEEL; CRACKING; MECHANISMS; CLASSIFICATION; IDENTIFICATION; PERFORMANCE; PREDICTION; BEHAVIOR;
D O I
10.1016/j.conbuildmat.2020.118270
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
An experimental study was conducted to apply the acoustic emission (AE) technique to monitor the corrosion process and cracking behavior in large-scale reinforced concrete (RC) pile specimens. In this study, the underwater and tidal zones of six RC piles were exposed to accelerated, localized corrosion in a simulated marine environment to reach 5%, 10% and 20% steel mass loss. The two piles with 20% steel mass loss together with another reference pile without corrosion were continuously monitored during the test via three attached AE sensors. Tidal action had a significant impact on the AE signals; accordingly, a novel amplitude-duration-peak frequency-based AE signal filter (ADPF) was proposed, which achieved better performance than the previous amplitude-duration-based filters. Additionally, the conjoint analysis of AE signals and the fractal dimension of cover cracks throughout the corrosion period enabled global detection of localized corrosion-induced damage of piles, regardless of sensor location. This study also presents an integrated corrosion-induced damage detection framework and four models based on multi-layer perception (MLP) networks to predict the corrosion level of the underwater and tidal zones of piles in the marine environment. (C) 2020 Elsevier Ltd. All rights reserved.
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收藏
页数:17
相关论文
共 69 条
  • [1] Classification of alkali-silica reaction damage using acoustic emission: A proof-of-concept study
    Abdelrahman, Marwa
    ElBatanouny, Mohamed K.
    Ziehl, Paul
    Fasl, Jeremiah
    Larosche, Carl J.
    Fraczek, John
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2015, 95 : 406 - 413
  • [2] Acoustic emission based damage assessment method for prestressed concrete structures: Modified index of damage
    Abdelrahman, Marwa
    ElBatanouny, Mohamed K.
    Ziehl, Paul H.
    [J]. ENGINEERING STRUCTURES, 2014, 60 : 258 - 264
  • [3] Acoustic Emission Monitoring of Corrosion Damage Propagation in Large-Scale Reinforced Concrete Beams
    Abouhussien, Ahmed A.
    Hassan, Assem A. A.
    [J]. JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES, 2018, 32 (02)
  • [4] Application of acoustic emission monitoring for assessment of bond performance of corroded reinforced concrete beams
    Abouhussien, Ahmed A.
    Hassan, Assem A. A.
    [J]. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2017, 16 (06): : 732 - 744
  • [5] Classification of cracking mode in concrete by acoustic emission parameters
    Aggelis, Dimitrios G.
    [J]. MECHANICS RESEARCH COMMUNICATIONS, 2011, 38 (03) : 153 - 157
  • [6] Andrade C, 2019, MATER STRUCT, V53, DOI 10.1617/s11527-019-1420-3
  • [7] [Anonymous], ACTA GEOTECH
  • [8] [Anonymous], P INT C SMART INFR C
  • [9] [Anonymous], MACHINE LEARNING TUT
  • [10] Non-destructive data assimilation as a tool to diagnose corrosion rate in reinforced concrete structures
    Anterrieu, Olivier
    Giroux, Bernard
    Gloaguen, Erwan
    Carde, Christophe
    [J]. JOURNAL OF BUILDING ENGINEERING, 2019, 23 : 193 - 206