statistical pattern-based assessment of structural health monitoring data
Clicks: 151
ID: 256421
2014
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
151 views
40 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #337 of 1,093 articles by views in journal of power sources
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,093 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
In structural health monitoring (SHM), various sensors are installed at critical locations of a structure. The signals from sensors are either continuously or periodically analyzed to determine the state and performance of the structure. An objective comparison of the sensor data at different time ranges is essential for assessing the structural condition or excessive load experienced by the structure which leads to potential damage in the structure. The objectives of the current study are to establish a relationship between the data from various sensors to estimate the reliability of the data and potential damage using the statistical pattern matching techniques. In order to achieve these goals, new methodologies based on statistical pattern recognition techniques have been developed. The proposed methodologies have been developed and validated using sensor data obtained from an instrumented bridge and road test data from heavy vehicles. The application of statistical pattern matching techniques are relatively new in SHM data interpretation and current research demonstrates that it has high potential in assessing structural conditions, especially when the data are noisy and susceptible to environmental disturbances.
| Reference Key |
islam2014mathematicalstatistical
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Mohammad S. Islam;Ashutosh Bagchi |
| Journal | journal of power sources |
| Year | 2014 |
| DOI |
10.1155/2014/926079
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.