國立虎尾科技大學 |

Automated Condition Assessment of Infrastructure Systems via 3D Computer Vision.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Automated Condition Assessment of Infrastructure Systems via 3D Computer Vision./
作者:	Khaloo, Ali.
面頁冊數:	1 online resource (168 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Contained By:	Dissertation Abstracts International79-11B(E).
標題:	Civil engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780438115231

Automated Condition Assessment of Infrastructure Systems via 3D Computer Vision.
Khaloo, Ali.

Automated Condition Assessment of Infrastructure Systems via 3D Computer Vision. - 1 online resource (168 pages)

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.

Thesis (Ph.D.)--George Mason University, 2018.

Includes bibliographical references

Accurate condition assessment of in-service infrastructure systems is critical for system-wide prioritization decisions. Manual visual inspection is currently the main form of assessing the physical and functional conditions of civil infrastructure at regular intervals in order to ensure the infrastructure still meets its expected service requirements. During a routine inspection, qualified and trained inspectors visually observe and manually record their observations, a costly and time-consuming process that often results in subjective and variable final reports. Thus, making the inspection process less costly, less obtrusive, more quantitative, and more consistent is a major research need. In particular, there is a need for better methods of recording the visual representation of a structure at a given inspection interval, in order to create a more consistent and repeatable record of structural health. This dissertation presents a contact-less and nondestructive computational framework which attempts to integrate computer vision, robotics, and remote sensing to provide a quantitative inspection methodology that can decrease cost, expedite inspection and facilitate access in comparison with the current inspection routine. First, it uses a set of two-dimensional (2D) digital images to produce a high-resolution and scale-accurate photorealistic three-dimensional (3D) model through a multi-scale and adaptive photogrammetric approach, followed by a fully automated robust segmentation of structural elements (e.g., columns and beams) in 3D models and a systematic and autonomous damage detection method. The accuracy, effectiveness, adaptability, and feasibility of the presented framework were evaluated by comparing its performance against conventional methods on large-scale infrastructure systems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438115231Subjects--Topical Terms:

561339
Civil engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Automated Condition Assessment of Infrastructure Systems via 3D Computer Vision.
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504 $a Includes bibliographical references
520 $a Accurate condition assessment of in-service infrastructure systems is critical for system-wide prioritization decisions. Manual visual inspection is currently the main form of assessing the physical and functional conditions of civil infrastructure at regular intervals in order to ensure the infrastructure still meets its expected service requirements. During a routine inspection, qualified and trained inspectors visually observe and manually record their observations, a costly and time-consuming process that often results in subjective and variable final reports. Thus, making the inspection process less costly, less obtrusive, more quantitative, and more consistent is a major research need. In particular, there is a need for better methods of recording the visual representation of a structure at a given inspection interval, in order to create a more consistent and repeatable record of structural health. This dissertation presents a contact-less and nondestructive computational framework which attempts to integrate computer vision, robotics, and remote sensing to provide a quantitative inspection methodology that can decrease cost, expedite inspection and facilitate access in comparison with the current inspection routine. First, it uses a set of two-dimensional (2D) digital images to produce a high-resolution and scale-accurate photorealistic three-dimensional (3D) model through a multi-scale and adaptive photogrammetric approach, followed by a fully automated robust segmentation of structural elements (e.g., columns and beams) in 3D models and a systematic and autonomous damage detection method. The accuracy, effectiveness, adaptability, and feasibility of the presented framework were evaluated by comparing its performance against conventional methods on large-scale infrastructure systems.
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