Vibration-based damage detection in civil engineering: excitation sources and temperature effects

By B. Peeters, J. Maeck, Guido De Roeck. 2001

420 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/a630cf973c6f946f2b838428080984d102fc1d81

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. A. Deraemaeker
2. Keith Worden
3. G. Manson
4. Guang Zhou
5. J. Nichols
6. T. Yi
7. Hoon Sohn
8. Dana Hughes

In Mechanics of Materials

1. Keith Worden
2. Guang Zhou
3. J. Nichols
4. T. Yi

Citations in the corpus

• A method for detecting damage-induced nonlinearities in structures using information theory (2006, background): “[1] found that temperature alone caused the eigenfrequencies of the Z24 bridge in Switzerland to change by as much as 18%.”
• A comparison of linear approaches to filter out environmental effects in structural health monitoring (2018, background): “Important variability of the dynamic properties of structures under ambient vibrations has been identified in a number of studies [8, 9, 10, 11].”
• Vibration-based monitoring of civil infrastructure: challenges and successes (2011, background): “It is now realised that for VBDD based on modal parameter changes to have a chance of working, procedures must incorporate procedures to compensate for or filter out the environmental and noise effects (Peeters et al., 2001; Deraemaeker et al., 2008) and be able to deal with effects of measurement a”
• A Summary Review of Correlations between Temperatures and Vibration Properties of Long-Span Bridges (2014, methodology): “[62, 63] proposed a dynamic linear regression model called ARX to filter out the temperature effects from the measured frequencies using monitoring data obtained from the healthy Z24-Bridge in Switzerland.”
• Features for damage detection with insensitivity to environmental and operational variations (2012, background): “An accurate regression effectively acts as a filter to remove the influence of the benign conditions from the model error, which can then be used as a damage-sensitive, environmentally insensitive feature (Sohn et al. 1998; Peeters et al. 2001; Worden et al. 2002).”