Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Smart Structures and Systems Volume 30, Number 3, September 2022 , pages 263-271 DOI: https://doi.org/10.12989/sss.2022.30.3.263 |
|
|
|
On the development of data-based damage diagnosis algorithms for structural health monitoring |
||
Anne S. Kiremidjian
|
||
| Abstract | ||
| In this paper we present an overview of damage diagnosis algorithms that have been developed over the past two decades using vibration signals obtained from structures. Then, the paper focuses primarily on algorithms that can be used following an extreme event such as a large earthquake to identify structural damage for responding in a timely manner. The algorithms presented in the paper use measurements obtained from accelerometers and gyroscope to identify the occurrence of damage and classify the damage. Example algorithms are presented include those based on autoregressive moving average (ARMA), wavelet energies from wavelet transform and rotation models. The algorithms are illustrated through application of data from test structures such as the ASCE Benchmark structure and laboratory tests of scaled bridge columns and steel frames. The paper concludes by identifying needs for research and development in order for such algorithms to become viable in practice. | ||
| Key Words | ||
| auto-regressive models; damage algorithms; damage classification; damage detection; vibration data; wavelet transform models | ||
| Address | ||
| Department of Civil and Environmental Engineering, Stanford University, 478 Via Ortega, Stanford, CA 94022, USA. | ||