Experimental validation of a damage detection approach on a full-scale highway sign support truss

Highway sign support structures enhance traffic safety by allowing messages to be delivered to motorists related to directions and warning of hazards ahead, and facilitating the monitoring of traffic speed and flow. These structures are exposed to adverse environmental conditions while in service. Strong wind and vibration accelerate their deterioration. Typical damage to this type of structure includes local fatigue fractures and partial loosening of bolted connections. The occurrence of these types of damage can lead to a failure in large portions of the structure, jeopardizing the safety of passing traffic. Therefore, it is important to have effective damage detection approaches to ensure the integrity of these structures. In this study, an extension of the Angle-between-String-and-Horizon (ASH) flexibility-based approach [32] is applied to locate damage in sign support truss structures at bay level. Ambient excitations (e.g. wind) can be considered as a significant source of vibration in these structures. Considering that ambient excitation is immeasurable, a pseudo ASH flexibility matrix constructed from output-only derived operational deflection shapes is proposed. A damage detection method based on the use of pseudo flexibility matrices is proposed to address several of the challenges posed in real-world applications. Tests are conducted on a 17.5-m long full-scale sign support truss structure to validate the effectiveness of the proposed method. Damage cases associated with loosened bolts and weld failures are considered. These cases are realistic for this type of structure. The results successfully demonstrate the efficacy of the proposed method to locate the two common forms of damage on sign support truss structures instrumented with a few accelerometers.

[1]  Biswajit Basu.  Identification of stiffness degradation in structures using wavelet analysis , 2005 .

[2]  Alessandro De Stefano,et al.  Structural damage detection using residual forces based on wavelet transform , 2010 .

[3]  Guirong Yan,et al.  Damage localization in ambient vibration by constructing proportional flexibility matrix , 2003 .

[4]  Guido De Roeck,et al.  System Identification and Damage Detection of a Prestressed Concrete Beam , 2006 .

[5]  Chung Bang Yun,et al.  Neural networks-based damage detection for bridges considering errors in baseline finite element models , 2003 .

[6]  Sia Nemat-Nasser,et al.  Damage detection with spatially distributed 2D Continuous Wavelet Transform , 2009 .

[7]  J. Ye,et al.  Structural damage detection using digital video imaging technique and wavelet transformation , 2005 .

[8]  Qiuhai Lu,et al.  MULTIPLE DAMAGE LOCATION WITH FLEXIBILITY CURVATURE AND RELATIVE FREQUENCY CHANGE FOR BEAM STRUCTURES , 2002 .

[9]  Dionisio Bernal,et al.  Flexibility-Based Approach for Damage Characterization: Benchmark Application , 2004 .

[10]  Daniele Zonta,et al.  A Strain-Flexibility-Based Approach to Damage Location , 2003 .

[11]  H. Y. Guo,et al.  Structural damage detection using information fusion technique , 2006 .

[12]  A. K. Pandey,et al.  Experimental verification of flexibility difference method for locating damage in structures , 1995 .

[13]  Charles R. Farrar,et al.  Computation of structural flexibility for bridge health monitoring using ambient modal data , 1996 .

[14]  Palle Andersen,et al.  Modal Identification from Ambient Responses using Frequency Domain Decomposition , 2000 .

[15]  Jinping Ou,et al.  Damage detection for beam structures using an angle-between-string-and-horizon flexibility matrix , 2010 .

[16]  Sayed A. Nassar,et al.  Handbook of bolts and bolted joints , 1998 .

[17]  Ugo Galvanetto,et al.  Numerical investigation of a new damage detection method based on proper orthogonal decomposition , 2007 .

[18]  Zhishen Wu,et al.  Modal macro‐strain flexibility methods for damage localization in flexural structures using long‐gage FBG sensors , 2011 .

[19]  Vikram Pakrashi,et al.  Structural damage detection and calibration using a wavelet-kurtosis technique , 2007 .

[20]  Hoon Sohn,et al.  Overview of Piezoelectric Impedance-Based Health Monitoring and Path Forward , 2003 .

[21]  Scott W. Doebling,et al.  COMPUTING STATICALLY COMPLETE FLEXIBILITY FROM DYNAMICALLY MEASURED FLEXIBILITY , 1997 .

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

[23]  Chris P. Pantelides,et al.  Rehabilitation of Cracked Aluminum Connections with GFRP Composites for Fatigue Stresses , 2007 .

[24]  Chenyang Lu,et al.  Experimental validation of a multi-level damage localization technique with distributed computation , 2010 .

[25]  Yozo Fujino,et al.  Experimental study of laser Doppler vibrometer and ambient vibration for vibration-based damage detection , 2006 .

[26]  Helong Li,et al.  Structural damage detection using the combination method of EMD and wavelet analysis , 2007 .

[27]  Xinqun Zhu,et al.  Structural damage detection from wavelet packet sensitivity , 2005 .

[28]  Patrick Guillaume,et al.  On-line damage detection on a wing panel using transmission of multisine ultrasonic waves , 2008 .

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