Smart structural health monitoring using computer vision and edge computing
Structural health monitoring (SHM) provides real-time data on the condition and performance of infrastructure, enabling timely and cost-effective maintenance interventions, and hence enhanced safety and extended service life. The computer vision-based non-contact sensor has emerged as a promising al...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
2024
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| Online Access: | http://purl.org/au-research/grants/arc/FT190100801 http://hdl.handle.net/20.500.11937/96054 |
| _version_ | 1848766084436459520 |
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| author | Peng, Zhen Li, Jun Hao, Hong Zhong, Yue |
| author_facet | Peng, Zhen Li, Jun Hao, Hong Zhong, Yue |
| author_sort | Peng, Zhen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Structural health monitoring (SHM) provides real-time data on the condition and performance of infrastructure, enabling timely and cost-effective maintenance interventions, and hence enhanced safety and extended service life. The computer vision-based non-contact sensor has emerged as a promising alternative to conventional contact-type sensors for structural displacement measurement and SHM. Many of the currently reported vision-based structural displacement measurement systems typically temporarily set up a video camera from a distance to the structure. The collected images or videos are usually stored locally and post-processed offline to obtain structural displacement responses, which is cumbersome and limited to short-term SHM applications. The recent development of technologies empowered by the Internet of Things (IoT) and edge computing has enabled real-time video processing and analysis at the source, minimizing latency, reducing bandwidth requirements, and enabling prompt decision-making, thereby enhancing efficiency and responsiveness compared to traditional offline video recording and processing systems. In this paper, an edge computing vision-based displacement measurement system (EdgeCVDMS) is developed. Video recording, processing, and displacement response identification are entirely performed on an edge device integrated with the vision-based displacement tracking algorithm, thereby greatly reducing the amount of data transmitted to the cloud server. The feasibility and applicability of the developed sensing system are experimentally validated on a laboratory-scaled transmission tower structure. The proposed EdgeCVDMS is cost-effective, easily deployable, and of great potential to be applied for the condition assessment of a larger population of aging civil infrastructure. |
| first_indexed | 2025-11-14T11:45:31Z |
| format | Journal Article |
| id | curtin-20.500.11937-96054 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:45:31Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-960542024-11-07T00:52:28Z Smart structural health monitoring using computer vision and edge computing Peng, Zhen Li, Jun Hao, Hong Zhong, Yue Structural health monitoring (SHM) provides real-time data on the condition and performance of infrastructure, enabling timely and cost-effective maintenance interventions, and hence enhanced safety and extended service life. The computer vision-based non-contact sensor has emerged as a promising alternative to conventional contact-type sensors for structural displacement measurement and SHM. Many of the currently reported vision-based structural displacement measurement systems typically temporarily set up a video camera from a distance to the structure. The collected images or videos are usually stored locally and post-processed offline to obtain structural displacement responses, which is cumbersome and limited to short-term SHM applications. The recent development of technologies empowered by the Internet of Things (IoT) and edge computing has enabled real-time video processing and analysis at the source, minimizing latency, reducing bandwidth requirements, and enabling prompt decision-making, thereby enhancing efficiency and responsiveness compared to traditional offline video recording and processing systems. In this paper, an edge computing vision-based displacement measurement system (EdgeCVDMS) is developed. Video recording, processing, and displacement response identification are entirely performed on an edge device integrated with the vision-based displacement tracking algorithm, thereby greatly reducing the amount of data transmitted to the cloud server. The feasibility and applicability of the developed sensing system are experimentally validated on a laboratory-scaled transmission tower structure. The proposed EdgeCVDMS is cost-effective, easily deployable, and of great potential to be applied for the condition assessment of a larger population of aging civil infrastructure. 2024 Journal Article http://hdl.handle.net/20.500.11937/96054 10.1016/j.engstruct.2024.118809 http://purl.org/au-research/grants/arc/FT190100801 https://creativecommons.org/licenses/by-nc-nd/4.0/ fulltext |
| spellingShingle | Peng, Zhen Li, Jun Hao, Hong Zhong, Yue Smart structural health monitoring using computer vision and edge computing |
| title | Smart structural health monitoring using computer vision and edge computing |
| title_full | Smart structural health monitoring using computer vision and edge computing |
| title_fullStr | Smart structural health monitoring using computer vision and edge computing |
| title_full_unstemmed | Smart structural health monitoring using computer vision and edge computing |
| title_short | Smart structural health monitoring using computer vision and edge computing |
| title_sort | smart structural health monitoring using computer vision and edge computing |
| url | http://purl.org/au-research/grants/arc/FT190100801 http://hdl.handle.net/20.500.11937/96054 |