Damage identification in underground tunnel structures with wavelet based residual force vector

Abstract Most current studies with vibration-based Structure Health Monitoring (SHM) techniques has been focused on aboveground civil infrastructure. However, a few studies have been conducted for underground structures. The development and application of using dynamic responses measured from underground tunnel structures for condition assessment and damage identification are highly desirable since this may avoid the difficulties in local non-destructive testing in tunnels and improve the efficiency of regular inspections. Since underground tunnel structures are subjected to a more complicated loading and boundary condition, developing reliable and efficient damage identification approach is essential. In this paper, a new damage index based on wavelet based residual force vector to identify damage in a tunnel structure is proposed. Numerical finite element model of a metro tunnel is built and different types of structural damages are introduced at multiple locations of the tunnel. Numerical results demonstrate that the introduced damages can be successfully located. Laboratory tests are further conducted to verify the performance of using the proposed approach for damage identification. A scaled aluminum pipe model which is subjected to the moving train load excitations is placed in a soil box, and experimental dynamic tests are performed for structural damage identification. The feasibility and effectiveness of using this approach for damage identification in tunnel structures are investigated. Damage detection results demonstrate that the proposed damage index can be employed as an efficient and functional damage identification indicator.

[1]  Satish Nagarajaiah,et al.  Output only modal identification and structural damage detection using time frequency & wavelet techniques , 2009 .

[2]  Hisao Fukunaga,et al.  Damage identification of laminated composite structures based on dynamic residual forces , 2001 .

[3]  Jun Li,et al.  Dynamic Assessment of Shear Connectors in Composite Bridges with Ambient Vibration Measurements , 2014 .

[4]  Xiaohua Yi,et al.  Damage detection of metro tunnel structure through transmissibility function and cross correlation analysis using local excitation and measurement , 2015 .

[5]  A. K. Pandey,et al.  Damage Detection in Structures Using Changes in Flexibility , 1994 .

[6]  G. Ghodrati Amiri,et al.  A Two-Stage Method for Structural Damage Prognosis in Shear Frames Based on Story Displacement Index and Modal Residual Force , 2015 .

[7]  Shunhua Zhou,et al.  Differential Settlement and Induced Structural Damage in a Cut-and-Cover Subway Tunnel in a Soft Deposit , 2016 .

[8]  Biswajit Basu,et al.  A padding method to reduce edge effects for enhanced damage identification using wavelet analysis , 2015 .

[9]  Ling Yu,et al.  Both the Highway Tunnel Secondary Lining Crack Damage Causes and the Renovation , 2015 .

[10]  Shirley J. Dyke,et al.  A parameter subset selection method using residual force vector for detecting multiple damage locations , 2010 .

[11]  Haim Baruh,et al.  Damage Detection in Flexible Structures , 1993 .

[12]  Dennis Gabor,et al.  Theory of communication , 1946 .

[13]  Sang Jun Lee,et al.  Damage detection sensitivity characterization of acousto-ultrasound-based structural health monitoring techniques , 2016 .

[14]  Hong Hao,et al.  Damage detection in bridge structures under moving loads with phase trajectory change of multi-type vibration measurements , 2017 .

[15]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[16]  Johan A. K. Suykens,et al.  Automated structural health monitoring based on adaptive kernel spectral clustering , 2017 .

[17]  Krzysztof Wilde,et al.  Application of continuous wavelet transform in vibration based damage detection method for beams and plates , 2006 .

[18]  S. A. Ravanfar,et al.  A two-step damage identification approach for beam structures based on wavelet transform and genetic algorithm , 2016 .

[19]  Arun Kumar Pandey,et al.  Damage detection from changes in curvature mode shapes , 1991 .

[20]  Qingxia Zhang,et al.  Simultaneous Identification of Moving Vehicles and Bridge Damages Considering Road Rough Surface , 2013 .

[21]  Kai-Yuen Wong,et al.  Design of a structural health monitoring system for long-span bridges , 2007 .

[22]  Charles R. Farrar,et al.  Comparative study of damage identification algorithms applied to a bridge: I. Experiment , 1998 .

[23]  Maosen Cao,et al.  A multi-scale pseudo-force model in wavelet domain for identification of damage in structural components , 2012 .

[24]  Andrzej Katunin,et al.  Vibration-based damage identification in composite circular plates using polar discrete wavelet transform , 2013 .

[25]  Atef Eraky,et al.  Damage detection of plate-like structures based on residual force vector , 2016 .

[26]  Yong Xia,et al.  DAMAGE DETECTION OF SHEAR CONNECTORS IN BRIDGE STRUCTURES WITH TRANSMISSIBILITY IN FREQUENCY DOMAIN , 2014 .

[27]  Magd Abdel Wahab,et al.  Damage localization and quantification of composite stratified beam structures using residual force method , 2017 .

[28]  S. Shen,et al.  Ground fissures in Xi’an and measures to prevent damage to the Metro tunnel system due to geohazards , 2016, Environmental Earth Sciences.

[29]  Su Zong-xian,et al.  SHELL-SPRING-CONTACT MODEL FOR SHIELD TUNNEL SEGMENTAL LINING ANALYSIS AND ITS APPLICATION , 2007 .

[30]  Edwin Reynders,et al.  Output-only structural health monitoring in changing environmental conditions by means of nonlinear system identification , 2014 .

[31]  Miroslaw J. Skibniewski,et al.  Developing a cloud model based risk assessment methodology for tunnel-induced damage to existing pipelines , 2015, Stochastic Environmental Research and Risk Assessment.