Probabilistic assessment of time to cracking of concrete cover due to corrosion using semantic segmentation of imaging probe sensor data
This paper presents a framework for segmentation of imaging probe corrosion sensor data using a deep learning algorithm and estimation of the remaining service life of the structure using the segmented data. The sensor consists of a sacrificial metal foil that is imaged using the optical probe and t...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/160753 |
| _version_ | 1811679277428506624 |
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| author | Ramani, Vasantha Zhang, Limao Kuang, Kevin Sze Chiang |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Ramani, Vasantha Zhang, Limao Kuang, Kevin Sze Chiang |
| author_sort | Ramani, Vasantha |
| collection | NTU |
| description | This paper presents a framework for segmentation of imaging probe corrosion sensor data using a deep learning algorithm and estimation of the remaining service life of the structure using the segmented data. The sensor consists of a sacrificial metal foil that is imaged using the optical probe and the changes in the images as corrosion develops can be used as a proxy to monitor the condition of the concrete. In this paper, DeepLabV3+ which is a deep learning network architecture is implemented for the segmentation of sensor images. The neural network model trained on labeled corroded and uncorroded images of foil captured under various chloride levels yields a test accuracy of 95%. The mass loss of steel is estimated using a Bayesian curve fitted over the estimated mass loss from the segmented images and the mass loss from the accelerated corrosion test. This is then used for the estimation of the corrosion rate, which is given as the input for the probabilistic estimation of the time at which the concrete cover is expected to crack. A case study is presented to demonstrate how the segmented images from the neural network model can be used for estimating the time to cracking of concretes. |
| first_indexed | 2024-10-01T03:06:36Z |
| format | Journal Article |
| id | ntu-10356/160753 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:06:36Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1607532022-08-02T05:01:57Z Probabilistic assessment of time to cracking of concrete cover due to corrosion using semantic segmentation of imaging probe sensor data Ramani, Vasantha Zhang, Limao Kuang, Kevin Sze Chiang School of Civil and Environmental Engineering Engineering::Civil engineering Probabilistic Assessment Corrosion This paper presents a framework for segmentation of imaging probe corrosion sensor data using a deep learning algorithm and estimation of the remaining service life of the structure using the segmented data. The sensor consists of a sacrificial metal foil that is imaged using the optical probe and the changes in the images as corrosion develops can be used as a proxy to monitor the condition of the concrete. In this paper, DeepLabV3+ which is a deep learning network architecture is implemented for the segmentation of sensor images. The neural network model trained on labeled corroded and uncorroded images of foil captured under various chloride levels yields a test accuracy of 95%. The mass loss of steel is estimated using a Bayesian curve fitted over the estimated mass loss from the segmented images and the mass loss from the accelerated corrosion test. This is then used for the estimation of the corrosion rate, which is given as the input for the probabilistic estimation of the time at which the concrete cover is expected to crack. A case study is presented to demonstrate how the segmented images from the neural network model can be used for estimating the time to cracking of concretes. Ministry of Education (MOE) Nanyang Technological University The Ministry of Education Tier 1 Grants, Singapore (No. 04MNP000279C120, No. 04MNP002126C120) and the Start-Up Grant at Nanyang Technological University, Singapore (No. 04INS000423C120) are acknowledged for their financial support of this research. 2022-08-02T05:01:57Z 2022-08-02T05:01:57Z 2021 Journal Article Ramani, V., Zhang, L. & Kuang, K. S. C. (2021). Probabilistic assessment of time to cracking of concrete cover due to corrosion using semantic segmentation of imaging probe sensor data. Automation in Construction, 132, 103963-. https://dx.doi.org/10.1016/j.autcon.2021.103963 0926-5805 https://hdl.handle.net/10356/160753 10.1016/j.autcon.2021.103963 2-s2.0-85115887826 132 103963 en 04MNP000279C120 04MNP002126C120 04INS000423C120 Automation in Construction © 2021 Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Civil engineering Probabilistic Assessment Corrosion Ramani, Vasantha Zhang, Limao Kuang, Kevin Sze Chiang Probabilistic assessment of time to cracking of concrete cover due to corrosion using semantic segmentation of imaging probe sensor data |
| title | Probabilistic assessment of time to cracking of concrete cover due to corrosion using semantic segmentation of imaging probe sensor data |
| title_full | Probabilistic assessment of time to cracking of concrete cover due to corrosion using semantic segmentation of imaging probe sensor data |
| title_fullStr | Probabilistic assessment of time to cracking of concrete cover due to corrosion using semantic segmentation of imaging probe sensor data |
| title_full_unstemmed | Probabilistic assessment of time to cracking of concrete cover due to corrosion using semantic segmentation of imaging probe sensor data |
| title_short | Probabilistic assessment of time to cracking of concrete cover due to corrosion using semantic segmentation of imaging probe sensor data |
| title_sort | probabilistic assessment of time to cracking of concrete cover due to corrosion using semantic segmentation of imaging probe sensor data |
| topic | Engineering::Civil engineering Probabilistic Assessment Corrosion |
| url | https://hdl.handle.net/10356/160753 |
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