Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains
| dc.citation.articleNumber | 7445 | |
| dc.citation.issueNumber | 17 | |
| dc.citation.journalTitle | Sensors | |
| dc.citation.volumeNumber | 23 | |
| dc.contributor.author | Pal, Ashish | |
| dc.contributor.author | Meng, Wei | |
| dc.contributor.author | Nagarajaiah, Satish | |
| dc.date.accessioned | 2024-05-03T15:51:21Z | |
| dc.date.available | 2024-05-03T15:51:21Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 × 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network’s capability to apply to materials exhibiting a similar stress–strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S4). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S4. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization. | |
| dc.identifier.citation | Pal, A., Meng, W., & Nagarajaiah, S. (2023). Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains. Sensors, 23(17), Article 17. https://doi.org/10.3390/s23177445 | |
| dc.identifier.digital | sensors-23-07445-v2 | |
| dc.identifier.doi | https://doi.org/10.3390/s23177445 | |
| dc.identifier.uri | https://hdl.handle.net/1911/115630 | |
| dc.language.iso | eng | |
| dc.publisher | MDPI | |
| dc.rights | Except where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains | |
| dc.type | Journal article | |
| dc.type.dcmi | Text | |
| dc.type.publication | publisher version |
Files
Original bundle
1 - 1 of 1