Limitations in the Structural Identification of Long-Span Bridges

Writers discuss an in-depth analysis of the limits in St -Id of large structures, including the mitigation of human errors and the control of uncertainty due to the complexity of large structures and their loading environment. After globally and locally calibrating a 25,218 DOF 3D FE model of a major bridge, the average error between the model and experimental frequencies is determined to be 2.3 % for the frequencies of the first 15 modes. The confidence in the simulated local characteristics such as the stresses and distortions is judged to be around 50-75%, based on local calibration results. It is important to note that inherent assumption in ambient monitoring data analysis is that the structure remains stationary, observable and reasonably linear during the course of the monitoring. The data collected during a large number of intermittent time windows over several weeks included periods of different levels of traffic-induced vibration, various wind environments and temperature ranges. Averaging such data represents an effective composite of various inputs and the corresponding structural states and behaviors throughout the course of the monitoring, therefore in effect smearing all non-linearity and non-stationarity and effectively linearizing the structure.

[1]  Arthur J. Helmicki,et al.  Structural Identification for Condition Assessment: Experimental Arts , 1997 .

[2]  A. Emin Aktan,et al.  On Structural Identification of Constructed Facilities , 1996 .

[3]  John C. Wilson,et al.  Modelling of a cable‐stayed bridge for dynamic analysis , 1991 .

[4]  John E. Mottershead,et al.  Model Updating In Structural Dynamics: A Survey , 1993 .

[5]  H. A. Cole,et al.  On-the-line analysis of random vibrations. , 1968 .

[6]  John T. DeWolf,et al.  Experimental Study of Bridge Monitoring Technique , 1990 .

[7]  A. Emin Aktan,et al.  CONDITION AND DAMAGE ASSESSMENT: ISSUES AND SOME PROMISING INDICES , 2002 .

[8]  A. M. Abdel-Ghaffar,et al.  Ambient Vibration Studies of Golden Gate Bridge , 1985 .

[9]  Robert H. Scanlan,et al.  Deer Isle Bridge: Field and Computed Vibrations , 1989 .

[10]  Gary C. Hart,et al.  System Identification in Structural Dynamics , 1977 .

[11]  Yozo Fujino,et al.  Monitoring of Hakucho Suspension Bridge by ambient vibration measurement , 2000, Smart Structures.

[12]  James M. W. Brownjohn,et al.  Ambient vibration survey of the fatih sultan mehmet (second Bosporus) suspension bridge , 1992 .

[13]  Chih-Chen Chang,et al.  Finite-Element Model Updating for the Kap Shui Mun Cable-Stayed Bridge , 2001 .

[14]  A. R. Flint,et al.  Planning and implementation of the structural health monitoring system for cable-supported bridges in Hong Kong , 2000, Smart Structures.

[15]  Raimundo Delgado,et al.  Dynamic Tests on Large Cable-Stayed Bridge , 2001 .

[16]  M. Lenett,et al.  Modal Analysis of Multi-reference Impact Test Data for Steel Stringer Bridges , 1997 .

[17]  A. Emin Aktan,et al.  Parameter Estimation for Multiple-Input Multiple-Output Modal Analysis of Large Structures , 2004 .

[18]  James M. W. Brownjohn,et al.  Dynamic Assessment of Curved Cable-Stayed Bridge by Model Updating , 2000 .

[19]  C. Farrar,et al.  SYSTEM IDENTIFICATION FROM AMBIENT VIBRATION MEASUREMENTS ON A BRIDGE , 1997 .

[20]  Ahmet Turer,et al.  Structural Identification: Analytical Aspects , 1998 .

[21]  J. C. Asmussen,et al.  Modal Analysis Based on the Random Decrement Technique: application to civil engineering structures , 1997 .

[22]  Chih-Chen Chang,et al.  AMBIENT VIBRATION OF LONG-SPAN CABLE-STAYED BRIDGE , 2001 .

[23]  M. S. Agbabian,et al.  System identification approach to detection of structural changes , 1991 .

[24]  Dean S. Carder,et al.  Observed vibrations of bridges , 1937 .

[25]  Christopher L. Barrett,et al.  TRANSIMS for urban planning , 1999 .