Modal-parameter identification and vibration-based damage detection of a damaged steel truss bridge

By K. Chang, Chul‐Woo Kim. 2016

207 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/c15d7f4ed514678b47c70240dab34cb5510bb497

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. Serkan Kiranyaz
2. Moncef Gabbouj
3. Onur Avcı
4. Osama Abdeljaber
5. Daniel J. Inman
6. J. J. Moughty
7. J. Rius
8. Alper Erturk

In Mechanics of Materials

1. Onur Avcı
2. Osama Abdeljaber
3. Serkan Kiranyaz
4. Moncef Gabbouj
5. J. J. Moughty
6. Daniel J. Inman
7. J. Rius
8. Chee Kiong Soh

Citations in the corpus

• A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, background): “Researchers focused on the time, frequency and modal domains searching for the presence, location and the severity of damage on engineering structures [100–103].”
• A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions (2017, methodology): “For instance, Chang & Kim’s [77] use of the Mahalanobis ‐ Taguchi System, which combines multiple modal ‐ based DSFs at once to enhance their collective damage sensitivity and performance consistency when applied to a steel truss bridge subjected to vehicle induced excitation.”