Data-driven Infrastructure Inspection
| dc.contributor.author | Bianchi, Eric Loran | en |
| dc.contributor.committeechair | Hebdon, Matthew Hardy | en |
| dc.contributor.committeemember | Shakiba, Maryam | en |
| dc.contributor.committeemember | Sarlo, Rodrigo | en |
| dc.contributor.committeemember | Abbott, A. Lynn | en |
| dc.contributor.department | Civil and Environmental Engineering | en |
| dc.date.accessioned | 2022-01-19T09:00:38Z | en |
| dc.date.available | 2022-01-19T09:00:38Z | en |
| dc.date.issued | 2022-01-18 | en |
| dc.description.abstract | Bridge inspection and infrastructure inspection are critical steps in the lifecycle of the built environment. Emerging technologies and data are driving factors which are disrupting the traditional processes for conducting these inspections. Because inspections are mainly conducted visually by human inspectors, this paper focuses on improving the visual inspection process with data-driven approaches. Data driven approaches, however, require significant data, which was sparse in the existing literature. Therefore, this research first examined the present state of the existing data in the research domain. We reviewed hundreds of image-based visual inspection papers which used machine learning to augment the inspection process and from this, we compiled a comprehensive catalog of over forty available datasets in the literature and identified promising, emerging techniques and trends in the field. Based on our findings in our review we contributed six significant datasets to target gaps in data in the field. The six datasets comprised of structural material segmentation, corrosion condition state segmentation, crack detection, structural detail detection, and bearing condition state classification. The contributed datasets used novel annotation guidelines and benefitted from a novel semi-automated annotation process for both object detection and pixel-level detection models. Using the data obtained from our collected sources, task-appropriate deep learning models were trained. From these datasets and models, we developed a change detection algorithm to monitor damage evolution between two inspection videos and trained a GAN-Inversion model which generated hyper-realistic synthetic bridge inspection image data and could forecast a future deterioration state of an existing bridge element. While the application of machine learning techniques in civil engineering is not wide-spread yet, this research provides impactful contribution which demonstrates the advantages that data driven sciences can provide to more economically and efficiently inspect structures, catalog deterioration, and forecast potential outcomes. | en |
| dc.description.abstractgeneral | Bridge inspection and infrastructure inspection are critical steps in the lifecycle of the built environment. Emerging technologies and data are driving factors which are disrupting the traditional processes for conducting these inspections. Because inspections are mainly conducted visually by human inspectors, this paper focuses on improving the visual inspection process with data-driven approaches. Data driven approaches, however, require significant data, which was sparse in the existing literature. Therefore, this research first examined the present state of the existing data in the research domain. We reviewed hundreds of image-based visual inspection papers which used machine learning to augment the inspection process and from this, we compiled a comprehensive catalog of over forty available datasets in the literature and identified promising, emerging techniques and trends in the field. Based on our findings in our review we contributed six significant datasets to target gaps in data in the field. The six datasets comprised of structural material detection, corrosion condition state identification, crack detection, structural detail detection, and bearing condition state classification. The contributed datasets used novel labeling guidelines and benefitted from a novel semi-automated labeling process for the artificial intelligence models. Using the data obtained from our collected sources, task-appropriate artificial intelligence models were trained. From these datasets and models, we developed a change detection algorithm to monitor damage evolution between two inspection videos and trained a generative model which generated hyper-realistic synthetic bridge inspection image data and could forecast a future deterioration state of an existing bridge element. While the application of machine learning techniques in civil engineering is not widespread yet, this research provides impactful contribution which demonstrates the advantages that data driven sciences can provide to more economically and efficiently inspect structures, catalog deterioration, and forecast potential outcomes. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:33702 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/107786 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution-NonCommercial 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | en |
| dc.subject | dataset | en |
| dc.subject | data set | en |
| dc.subject | deep learning | en |
| dc.subject | Machine learning | en |
| dc.subject | inspection | en |
| dc.subject | bridge inspection | en |
| dc.subject | GAN | en |
| dc.subject | image-registration | en |
| dc.subject | semantic segmentation | en |
| dc.subject | structural health monitoring | en |
| dc.subject | change detection | en |
| dc.subject | civil engineering | en |
| dc.subject | structural engineering | en |
| dc.title | Data-driven Infrastructure Inspection | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Civil Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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