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Optimized Structural Health Monitoring for Inland Waterways Infrastructure Using Model-Based Diagnostics and Prognostics.
Optimized Structural Health Monitoring for Inland Waterways Infrastructure Using Model-Based Diagnostics and Prognostics.
- 자료유형
- 학위논문
- Control Number
- 0017160367
- International Standard Book Number
- 9798382121178
- Dewey Decimal Classification Number
- 690
- Main Entry-Personal Name
- Wu, Zihan.
- Publication, Distribution, etc. (Imprint
- [S.l.] : University of California, San Diego., 2024
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- Physical Description
- 211 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
- General Note
- Advisor: Todd, Michael D.
- Dissertation Note
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- Summary, Etc.
- 요약Inland waterways infrastructure such as miter gates are subject to damage like cracking and corrosion due to long (∼50 years) service lives with extensive water exposure. With the advancement of modern sensing technologies, there's a vast potential for Structural Health Monitoring (SHM) to transition into a more intelligent and efficient technology that can integrate multiple data sources for enhanced damage diagnostics and inform predictive inspection and maintenance strategies. This research presents a comprehensive optimization framework for the diagnosis and prognosis of such infrastructure. The framework first proposes a novel iterative global-local method for efficient and accurate forward modeling of structural damage in miter gates. It then develops an innovative diagnostic and prognostic framework that not only integrates multiple data sources for structures with multi-failure modes but also analyzes the environmental factors influencing SHM, offering insights into the challenges and solutions for real-world inspections. Furthermore, it introduces a physics-informed inspection planning framework, underpinned by model-based diagnostics and prognostics, leveraging the benefits of digital twin and deep learning technologies. This work represents a significant advancement for a certain class of SHM, providing a robust methodology for improving the lifespan and ensuring the safety of critical waterway infrastructure, marking a crucial step toward the future of infrastructure inspection and maintenance.
- Subject Added Entry-Topical Term
- Architectural engineering.
- Subject Added Entry-Topical Term
- Environmental engineering.
- Subject Added Entry-Topical Term
- Water resources management.
- Index Term-Uncontrolled
- Infrastructure
- Index Term-Uncontrolled
- Structural damage
- Index Term-Uncontrolled
- Structural Health Monitoring
- Index Term-Uncontrolled
- Inland waterways
- Index Term-Uncontrolled
- Deep learning
- Added Entry-Corporate Name
- University of California, San Diego Structural Engineering
- Host Item Entry
- Dissertations Abstracts International. 85-10B.
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:657485
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