Artificial neural network modeling of stress in concrete under step-loading using non-linear ultrasonic test results
Clicks: 2
ID: 285210
2016
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
Popular Article
0.3
/100
2 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
PopularRanked #1,092 of 3,757 articles by views in Malay Journal
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 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
This study deals with the prediction of stress in concrete using the non-linear ultrasonic test and artificial neural network (ANN). Data were obtained from 36 cube specimens and 27 beam specimens subjected to step loading. For the cubes, the ordinary concrete (ORC), fiber reinforced concrete (FRC) and concrete with varying size of aggregate were studied. For the reinforced concrete beams, 7 types of concrete were investigated with varying amount of reinforcements and water to cement ratio (w/c). The fundamental harmonic amplitude (A1), second harmonic amplitude (A2), third harmonic amplitude (A3), strain/neutral axis (NA) and peak to peak amplitude (PPA) were found to be the significant input parameters for ANN based on the results of the Spearman’s rank order correlation. The optimum models were determined based on the Pearson correlation coefficient (R), mean square error (MSE) and soundness of the behavior of the input parameter with the stress of the concrete. The Daponte’s amplitude sensitivity (DA) was used in analyzing the result of the parametric study. Results of the sensitivity analysis show that for the ORC and FRC, the A2 and strain were long range sensitive for all w/c. The A3 decreased its sensitivity as the water to cement ratio was increased. For the size of aggregate study, PPA and A3 decreased their sensitivity as the size of aggregate increases. A1 was only sensitive for small aggregate concrete. The A2 and strain were long range sensitive and varying the size of aggregate did not affect their sensitivity. In the study of reinforced concrete beams, the PPA was sensitive for all types of concrete except WC40B. The A1 was very sensitive to the load having long range sensitivity for all types of concrete except WC40A. The A2 and NA were sensitive for all types of concrete. Lastly, A3 decreased its sensitivity as the water to cement ratio increased. The specific damping capacity (S) of concrete was also investigated and ANN models were produced using the load, neutral axis and loading branch as the input parameters to predict the damping in concrete using the energy method. The highest magnitude for S occurs at the first load of the concrete. S would decrease in magnitude when repetitive load was applied. When a higher load was introduced, a new peak was observed. The results of this study highlight and improve the non-destructive evaluation capabilities of non-linear ultrasonic test.
| Reference Key |
persistent_1760654946_68f17662dfaca
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Soberano, Marcus Karyl C. |
| Journal | Malay Journal |
| Year | 2016 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
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.