Structural identification and damage detection through long-gauge strain measurements

Abstract Strain measurements from traditional point-type gauges only reveal structural local information. This feature significantly limits the development and application of strain modal theory in civil engineering. The long-gauge fiber optic strain sensor emerging recently has the unique merit of reflecting both local and global information of a structure by measuring the averaged strain within a long gauge length (e.g., 1–2 m). Based on this advantage, strain modal identification by processing the dynamic macro strain measurement is investigated. First, the macro strain frequency response function is derived based on the mapping relationship between the long gauge strain and displacement. Then, two methods are proposed for structural strain modal identification and their results are compared. Moreover, damage indexes based on identified macro strain modal shapes are investigated for structural damage detection. The example of a steel stringer bridge model successfully illustrates that the proposed methods accurately identify structural macro strain modal shapes, and the results also reveals that macro strain based indexes are much more robust than traditional modal parameter based indexes for structural damage detection.

[1]  Sreenivas Alampalli,et al.  Estimating Fatigue Life of Bridge Components Using Measured Strains , 2006 .

[2]  Arthur A. Huckelbridge,et al.  Temporal Nature of Fatigue Damage in Highway Bridges , 2009 .

[3]  Y. Edward Zhou Assessment of Bridge Remaining Fatigue Life through Field Strain Measurement , 2006 .

[4]  Zhishen Wu,et al.  Two-level Damage Detection Strategy Based on Modal Parameters from Distributed Dynamic Macro-strain Measurements , 2007 .

[5]  Jan Ming Ko,et al.  Evaluation of typhoon induced fatigue damage for Tsing Ma Bridge , 2002 .

[6]  Harry W. Shenton,et al.  System for In-Service Strain Monitoring of Ordinary Bridges , 2006 .

[7]  Guido De Roeck,et al.  One-year monitoring of the Z24-Bridge : environmental effects versus damage events , 2001 .

[8]  Harry W. Shenton,et al.  Damage Identification Based on Dead Load Redistribution: Methodology , 2006 .

[9]  Neil A. Hoult,et al.  Wireless structural health monitoring at the Humber Bridge UK , 2008 .

[10]  Zhao-Dong Xu,et al.  Energy Damage Detection Strategy Based on Strain Responses for Long-Span Bridge Structures , 2011 .

[11]  T. Leung,et al.  THEORETICAL AND EXPERIMENTAL STUDY OF MODAL STRAIN ANALYSIS , 1996 .

[12]  Mackenzie Melo,et al.  Structural Health Monitoring of the Golden Gate Bridge using Wireless Sensor Networks - Final report - , 2009 .

[13]  Aleksandar Pavic,et al.  Experimental methods for estimating modal mass in footbridges using human-induced dynamic excitation , 2007 .

[14]  Y. Edward Zhou Fatigue Problems in Steel Bridge Structures , 2004 .

[15]  Shigeki Kusuhara,et al.  Structural monitoring and design verification of Akashi Kaikyo bridge , 2008 .

[16]  Zhishen Wu,et al.  Development of Distributed Long-gage Fiber Optic Sensing System for Structural Health Monitoring , 2007 .

[17]  Jian Zhang,et al.  Structural flexibility identification by integrating substructures' measurements , 2012, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[18]  Mustafa Gul,et al.  Nonparametric analysis of structural health monitoring data for identification and localization of changes: Concept, lab, and real-life studies , 2012 .

[19]  Junwon Seo,et al.  Field Validation of a Statistical-Based Bridge Damage-Detection Algorithm , 2013 .

[20]  Jinkoo Kim,et al.  Estimation of the modal mass of a structure with a tuned-mass damper using H-infinity optimal model reduction , 2006 .

[21]  Yi-Qing Ni,et al.  Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data , 2012 .