Chongqing Jiaotong University, State Key Laboratory of Mountainous Area Bridges and Tunnels,Province-Ministry Jointly Affiliated, Chongqing 400074, China
China possesses a vast scale of in-service tunnels. With their increasing service life, ensuring the operational safety of lining structures has become a critical industry concern. The frequent occurrence of lining spalling incidents in recent years has resulted in substantial economic losses. This issue is compounded by a lack of effective detection methods and the limitations of conventional techniques for assessing existing tunnel lining defects, making the early identification of loosened lining segments an urgent demand for practitioners. A non-contact detection method was proposed for tunnel linings that integrates a directional acoustic wave generation device with laser Doppler vibrometry technology. Model tests were conducted to obtain the vibration characteristics of tunnel linings containing loosened segments. Findings indicate that the presence of loosened lining segments significantly reduces the overall structural stiffness while simultaneously weakening the local stiffness at the section containing the crack. These characteristics manifest in the monitoring data as: a sharp drop in the dominant vibration frequency at local measurement points. Significant fluctuations in the rate of change of the spectral kurtosis area of vibration signals between the loosened segment zone and the intact zone.These features that are sensitive to potential spalling locations can be effectively used for the accurate localization of potential spalling blocks, thereby further promoting the development of non-contact identification technologies for potential tunnel lining spalling.
FENG Wan-lin, LIN Zhi, GONG Huai-zhang, YANG Hong-yun, GOU Xiao-ying, GUO Xian-de.
Vibration Characteristics of Tunnel Lining with Spalling Risk and Identification Methods of Potential Spalling Block[J].
Science Technology and Engineering, 2026, 26(7): 3052-3059 DOI:10.12404/j.issn.1671-1815.2504292
LiuJiguo, CuiQinglong, LiDanni, et al. Statistics and analysis of super-long highway tunnels over 10 km in China by the end of 2023[J]. Tunnel Construction, 2024, 44(1): 189-198.
LuChen, WangKui, ShiYichao, et al. Tunnel eross-section deformation detection based on 3D laser seanning technology[J]. Seience Technology and Engineering, 2025, 25(24): 10459-10467.
[5]
YanY, MaoX, WangX, et al. Design and implementation of a structural health monitoring system for a large sea-crossing project with bridges and tunnel[J]. Shock and Vibration, 2019, 2019(1): 2832089.
[6]
JiangY, WangL, ZhangB, et al. Tunnel lining detection and retrofitting[J]. Automation in Construction, 2023, 152: 104881.
[7]
ElbazK, ShenJ S, ArulrajahA, et al. Geohazards induced by anthropic activities of geoconstruction: a review of recent failure cases[J]. Arabian Journal of Geosciences, 2016, 9(18): 708.
[8]
TsunoK, KojimaY, KishidaK. Estimation method of falling concrete piece from tunnel lining[J]. Quarterly Report of RTRI, 2013, 54(1): 12-17.
[9]
XuG, HeC, ChenZ, et al. Mechanical behavior of secondary tunnel lining with longitudinal crack[J]. Engineering Failure Analysis, 2020, 113: 104543.
[10]
ZhangC, MinB, WangH, et al. Mechanical properties of the tunnel structures with cracks around voids behind linings[J]. Thin-Walled Structures, 2022, 181: 110117.
[11]
WangF, HuangH, ZhangD, et al. Cracking feature and mechanical behavior of shield tunnel lining simulated by a phase-field modeling method based on spectral decomposition[J]. Tunnelling and Underground Space Technology, 2022, 119: 104246.
[12]
AndreottiG, CalviG M, SogaK, et al. Cyclic model with damage assessment of longitudinal joints in segmental tunnel linings[J]. Tunnelling and Underground Space Technology, 2020, 103: 103472.
LiXiaoke, GaoYu, LiuHaichao, et al. Vibration response analysis of large diameter shield tunnel for intercity railway with two-orbit trains[J]. Science Technology and Engineering, 2021, 21(3): 1157-1162.
[19]
MaleklooA, OzerE, AlHamaydehM, et al. Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights[J]. Structural Health Monitoring, 2022, 21(4): 1906-1955.
[20]
CapecchiD, CiambellaJ, PauA, et al. Damage identification in a parabolic arch by means of natural frequencies, modal shapes and curvatures[J]. Meccanica, 2016, 51: 2847-2859.
JiangYujing, WuFabo, ZhangXuepeng, et al. Non-destructive quantitative assessment on the damage of concrete structure based on laser Doppler vibrometer technology[J]. Journal of China University of Mining & Technology, 2023, 52(5): 889-903.
[23]
FengL, YiX, ZhuD, et al. Damage detection of metro tunnel structure through transmissibility function and cross correlation analysis using local excitation and measurement[J]. Mechanical Systems and Signal Processing, 2015, 60: 59-74.
YuanHang, YouXinpeng, GuoFengchao. Intelligent deteetion method and device for void of composite bridge tower based on vibration andacoustie signals: take the composite strucure bridge tower of Zhangjinggao Yangtze River Bridge as an examplel[J]. Science Technology and Engineering, 2025, 25(16): 6890-6897.
ZouJinyang, ZhangYating, DingXin, et al. Detection of internal cavities in concrete with laser-acoustic method[J]. Infrared and Laser Engineering, 2023, 52(1): 134-143.
[28]
SugimotoK, SugimotoT, UtagawaN, et al. Detection of internal defects of concrete structures based on statistical evaluation of healthy part of concrete by the noncontact acoustic inspection method[J]. Japanese Journal of Applied Physics, 2018, 57(7S1): 07LC13.
[29]
SugimotoT, SugimotoK, KosugeN, et al. High-speed noncontact acoustic inspection method for civil engineering structure using multitone burst wave[J]. Japanese Journal of Applied Physics, 2017, 56(7S1): 07JC10.
[30]
邹锦阳. 基于激光致声的混凝土内部空洞检测研究[D]. 天津: 天津大学, 2022.
[31]
ZouJinyang. Research on the internal cavity detection of concrete based on laser-acoustic wave[D]. Tianjin: Tianjin University, 2022.
[32]
ChenX, LinZ, YangY, et al. Experimental study on internal damage mechanisms and performance state perception of tunnel linings under different loading paths[J]. Engineering Failure Analysis, 2025, 168: 109084.
[33]
LiK, WuH, LiuX, et al. Intelligent diagnosis of rolling bearing based on ICEEMDAN-WTD of noise reduction and multi-strategy fusion optimization SCNs[J]. IEEE Access, 2024, 12: 36908-36923.
[34]
胡杰. 隧道块状节理岩体破坏前兆规律及块体垮塌监测预警方法[D]. 济南: 山东大学, 2021.
[35]
HuJie. Precursor evolution of blocky jointed rock mass failure and monitoring and early warning method of block collapse disasters in tunnel engineering[D]. Jinan: Shandong University, 2021.
[36]
CaddemiS, CalioI. Exact closed-form solution for the vibration modes of the Euler-Bernoulli beam with multiple open cracks[J]. Journal of Sound and Vibration, 2009, 327(3-5): 473-489.
WangFuke, GaoBingpeng. Composite fault diagnosis of rolling bearing based on optimized VMD-MCKD and spectral kurtosis[J]. Machine Tool and Hydraulics, 2024, 52(19): 196-202.
ZhangLei, ChenXuejun, MaLin, et al. Study on free vibration characteristics of suspension frame system of maglev train[J]. Journal of Guizhou University (Natural Sciences), 2024, 41(5): 32-40, 94.
LiHongqiao. Research on the theory and method of tunnel structure damage identification based on the dynamic response of in-service trains[J]. Shanghai: Tongji University, 2021.
ZhangYonglai. Research on subway tunnel damageidentification mechanism and deep learningmethod based on the dynamic response of in service trains[D]. Shanghai: Tongji University, 2022.