國立虎尾科技大學 |

Artificial Intelligence Models for Digitized Operations and Maintenance of Large Infrastructure Systems.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Artificial Intelligence Models for Digitized Operations and Maintenance of Large Infrastructure Systems./
作者:	Paladugu, Bala Sai Krishna.
面頁冊數:	1 online resource (120 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Contained By:	Dissertations Abstracts International85-02B.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798380110419

Artificial Intelligence Models for Digitized Operations and Maintenance of Large Infrastructure Systems.
Paladugu, Bala Sai Krishna.

Artificial Intelligence Models for Digitized Operations and Maintenance of Large Infrastructure Systems. - 1 online resource (120 pages)

Source: Dissertations Abstracts International, Volume: 85-02, Section: B.

Thesis (Ph.D.)--Arizona State University, 2023.

Includes bibliographical references

Large-scale civil infrastructure systems are critical for the functioning and development of any society. However, these systems are often vulnerable to degradation and the effects of aging, necessitating consistent monitoring and maintenance. Current methods for infrastructure maintenance primarily rely on human intervention and need the implementation of advanced sensing and computing technologies in field operations and maintenance (O&M) tasks. This research aimed to address these gaps and provide novel contributions. Specifically, the objectives of this study were to leverage artificial intelligence models to enhance point cloud noise processing, to automate tree species detection using Mask R-CNN, and to integrate imagery data and LiDAR datasets for realtime terrain analysis. First, the study proposed leverages neural networks to eliminate unwanted noise from point cloud datasets, enhancing the accuracy and reliability of infrastructure data. Secondly, the research integrated Mask R-CNN into automated tree species detection. This component offers an efficient solution to identify and classify vegetation surrounding infrastructure, enabling infrastructure managers to devise proactive vegetation management strategies, thereby reducing risks associated with tree-related incidents. Lastly, the study fused image and LiDAR datasets to support real-time terrain analysis. This integrated approach provides a comprehensive understanding of terrain characteristics, allowing infrastructure managers to assess slope, elevation, and other relevant factors, facilitating proactive maintenance interventions and mitigating risks associated with erosion. These contributions collectively underscore the potential of artificial intelligence models in advancing the operations and maintenance practices of large civil infrastructure systems. By leveraging these models, infrastructure managers can optimize decision-making processes, streamline maintenance efforts, and enhance critical infrastructure networks' overall resilience and sustainability.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380110419Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Large-scale civil infrastructureIndex Terms--Genre/Form:

554714
Electronic books.

Artificial Intelligence Models for Digitized Operations and Maintenance of Large Infrastructure Systems.
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520 $a Large-scale civil infrastructure systems are critical for the functioning and development of any society. However, these systems are often vulnerable to degradation and the effects of aging, necessitating consistent monitoring and maintenance. Current methods for infrastructure maintenance primarily rely on human intervention and need the implementation of advanced sensing and computing technologies in field operations and maintenance (O&M) tasks. This research aimed to address these gaps and provide novel contributions. Specifically, the objectives of this study were to leverage artificial intelligence models to enhance point cloud noise processing, to automate tree species detection using Mask R-CNN, and to integrate imagery data and LiDAR datasets for realtime terrain analysis. First, the study proposed leverages neural networks to eliminate unwanted noise from point cloud datasets, enhancing the accuracy and reliability of infrastructure data. Secondly, the research integrated Mask R-CNN into automated tree species detection. This component offers an efficient solution to identify and classify vegetation surrounding infrastructure, enabling infrastructure managers to devise proactive vegetation management strategies, thereby reducing risks associated with tree-related incidents. Lastly, the study fused image and LiDAR datasets to support real-time terrain analysis. This integrated approach provides a comprehensive understanding of terrain characteristics, allowing infrastructure managers to assess slope, elevation, and other relevant factors, facilitating proactive maintenance interventions and mitigating risks associated with erosion. These contributions collectively underscore the potential of artificial intelligence models in advancing the operations and maintenance practices of large civil infrastructure systems. By leveraging these models, infrastructure managers can optimize decision-making processes, streamline maintenance efforts, and enhance critical infrastructure networks' overall resilience and sustainability.
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