Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
A deep learning-based approach towards automating visual reinforced concrete bridge inspections
Visual inspections are fundamental to the maintenance of RC bridge infrastructure. However, their highly subjective nature often compromises the accuracy of inspection results and ultimately leads to inaccurate prioritisation of repair and rehabilitation activities. Visual inspections are also known...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Civil Engineering
2022
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613231196930048 |
|---|---|
| access_status_str | Open Access |
| author | Dube, Bright N |
| author2 | Moyo, Pilate |
| author_browse | Dube, Bright N Moyo, Pilate |
| author_facet | Moyo, Pilate Dube, Bright N |
| author_sort | Dube, Bright N |
| collection | Thesis |
| description | Visual inspections are fundamental to the maintenance of RC bridge infrastructure. However, their highly subjective nature often compromises the accuracy of inspection results and ultimately leads to inaccurate prioritisation of repair and rehabilitation activities. Visual inspections are also known to expose inspectors to height and trafficrelated hazards, and sometimes require the use of costly access equipment. Therefore, the present study investigated state-of-the-art Unmanned Aerial Vehicles (UAVs) and algorithms capable of automating visual RC bridge inspections in order to reduce inspector subjectivity, minimise inspection costs and enhance inspector safety. Convolutional neural network (CNN) algorithms are state-of-the-art in relation to the automatic detection of RC bridge defects. However, much of the prior research in this area focused on detecting the presence of defects and gave little to no attention to characterizing them according to defect type and degree (D) or extent (E) ratings. Four proof-of-concept CNN models were therefore developed, namely a defect-type detector, crack-type detector, exposed-rebar detector and a shrinkage crack D-rating model. Each model was built by first compiling defect images, labelling them according to defect/crack type and creating training and test sets at a 90-10% split. The training sets were then used to train the CNN models through transfer learning and fine-tuning using the fastai deep learning python library. The performance of each model was ultimately evaluated based on prediction accuracies on the test sets and their robustness to noise. Test accuracies ≥ 87% were attained by the trained models. This result shows that CNNs are capable of accurately identifying RC bridge corrosion, spalling, ASR, cracking and efflorescence, and assigning appropriate D ratings to shrinkage cracks. It was concluded that CNN models can be built to identify and allocate D and E ratings to any visible defect type, provided the requisite training data that sufficiently represents noisy real-world inspection conditions can be acquired. This formed the basis upon which a practical framework for UAV-enabled and deep learning-based RC bridge inspections was developed. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/35602 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:51.499Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Civil Engineering |
| publisherStr | Department of Civil Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/35602 A deep learning-based approach towards automating visual reinforced concrete bridge inspections Dube, Bright N Moyo, Pilate Matongo, Kabani Engineering Visual inspections are fundamental to the maintenance of RC bridge infrastructure. However, their highly subjective nature often compromises the accuracy of inspection results and ultimately leads to inaccurate prioritisation of repair and rehabilitation activities. Visual inspections are also known to expose inspectors to height and trafficrelated hazards, and sometimes require the use of costly access equipment. Therefore, the present study investigated state-of-the-art Unmanned Aerial Vehicles (UAVs) and algorithms capable of automating visual RC bridge inspections in order to reduce inspector subjectivity, minimise inspection costs and enhance inspector safety. Convolutional neural network (CNN) algorithms are state-of-the-art in relation to the automatic detection of RC bridge defects. However, much of the prior research in this area focused on detecting the presence of defects and gave little to no attention to characterizing them according to defect type and degree (D) or extent (E) ratings. Four proof-of-concept CNN models were therefore developed, namely a defect-type detector, crack-type detector, exposed-rebar detector and a shrinkage crack D-rating model. Each model was built by first compiling defect images, labelling them according to defect/crack type and creating training and test sets at a 90-10% split. The training sets were then used to train the CNN models through transfer learning and fine-tuning using the fastai deep learning python library. The performance of each model was ultimately evaluated based on prediction accuracies on the test sets and their robustness to noise. Test accuracies ≥ 87% were attained by the trained models. This result shows that CNNs are capable of accurately identifying RC bridge corrosion, spalling, ASR, cracking and efflorescence, and assigning appropriate D ratings to shrinkage cracks. It was concluded that CNN models can be built to identify and allocate D and E ratings to any visible defect type, provided the requisite training data that sufficiently represents noisy real-world inspection conditions can be acquired. This formed the basis upon which a practical framework for UAV-enabled and deep learning-based RC bridge inspections was developed. 2022-01-27T14:29:12Z 2022-01-27T14:29:12Z 2021 2022-01-27T14:28:10Z Master Thesis Masters MSc http://hdl.handle.net/11427/35602 eng application/pdf Department of Civil Engineering Faculty of Engineering and the Built Environment |
| spellingShingle | Engineering Dube, Bright N A deep learning-based approach towards automating visual reinforced concrete bridge inspections |
| thesis_degree_str | Master's |
| title | A deep learning-based approach towards automating visual reinforced concrete bridge inspections |
| title_full | A deep learning-based approach towards automating visual reinforced concrete bridge inspections |
| title_fullStr | A deep learning-based approach towards automating visual reinforced concrete bridge inspections |
| title_full_unstemmed | A deep learning-based approach towards automating visual reinforced concrete bridge inspections |
| title_short | A deep learning-based approach towards automating visual reinforced concrete bridge inspections |
| title_sort | deep learning based approach towards automating visual reinforced concrete bridge inspections |
| topic | Engineering |
| url | http://hdl.handle.net/11427/35602 |
| work_keys_str_mv | AT dubebrightn adeeplearningbasedapproachtowardsautomatingvisualreinforcedconcretebridgeinspections AT dubebrightn deeplearningbasedapproachtowardsautomatingvisualreinforcedconcretebridgeinspections |