Digital State Models for Infrastructure Condition Assessment and Structural Testing
| dc.contributor.author | Lama Salomon, Abraham | en |
| dc.contributor.committeechair | Moen, Cristopher D. | en |
| dc.contributor.committeemember | Leon, Roberto T. | en |
| dc.contributor.committeemember | Roberts-Wollmann, Carin L. | en |
| dc.contributor.committeemember | Parikh, Devi | en |
| dc.contributor.committeemember | Batra, Dhruv | en |
| dc.contributor.department | Civil and Environmental Engineering | en |
| dc.date.accessioned | 2018-08-05T06:00:29Z | en |
| dc.date.available | 2018-08-05T06:00:29Z | en |
| dc.date.issued | 2017-02-10 | en |
| dc.description.abstract | This research introduces and applies the concept of digital state models for civil infrastructure condition assessment and structural testing. Digital state models are defined herein as any transient or permanent 3D model of an object (e.g. textured meshes and point clouds) combined with any electromagnetic radiation (e.g., visible light, infrared, X-ray) or other two-dimensional image-like representation. In this study, digital state models are built using visible light and used to document the transient state of a wide variety of structures (ranging from concrete elements to cold-formed steel columns and hot-rolled steel shear-walls) and civil infrastructures (bridges). The accuracy of digital state models was validated in comparison to traditional sensors (e.g., digital caliper, crack microscope, wire potentiometer). Overall, features measured from the 3D point clouds data presented a maximum error of ±0.10 in. (±2.5 mm); and surface features (i.e., crack widths) measured from the texture information in textured polygon meshes had a maximum error of ±0.010 in. (±0.25 mm). Results showed that digital state models have a similar performance between all specimen surface types and between laboratory and field experiments. Also, it is shown that digital state models have great potential for structural assessment by significantly improving data collection, automation, change detection, visualization, and augmented reality, with significant opportunities for commercial development. Algorithms to analyze and extract information from digital state models such as cracks, displacement, and buckling deformation are developed and tested. Finally, the extensive data sets collected in this effort are shared for research development in computer vision-based infrastructure condition assessment, eliminating the major obstacle for advancing in this field, the absence of publicly available data sets. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:9278 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/84502 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Digital state model | en |
| dc.subject | Condition assessment | en |
| dc.subject | Non-contact measurement | en |
| dc.subject | Computer vision | en |
| dc.subject | Point cloud | en |
| dc.subject | Crack detection | en |
| dc.subject | Change detection | en |
| dc.subject | Corrosion resistant | en |
| dc.subject | Bridge | en |
| dc.title | Digital State Models for Infrastructure Condition Assessment and Structural Testing | 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 | Ph. D. | en |
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