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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
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151005
- ISBN
- 9798382121178
- DDC
- 690
- 저자명
- Wu, Zihan.
- 서명/저자
- Optimized Structural Health Monitoring for Inland Waterways Infrastructure Using Model-Based Diagnostics and Prognostics
- 발행사항
- [Sl] : University of California, San Diego, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 211 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
- 주기사항
- Advisor: Todd, Michael D.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- 초록/해제
- 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.
- 키워드
- Infrastructure
- 키워드
- Inland waterways
- 키워드
- Deep learning
- 기타저자
- University of California, San Diego Structural Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-10B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798382121178
■035 ▼a(MiAaPQ)AAI30994695
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a690
■1001 ▼aWu, Zihan.
■24510▼aOptimized Structural Health Monitoring for Inland Waterways Infrastructure Using Model-Based Diagnostics and Prognostics
■260 ▼a[Sl]▼bUniversity of California, San Diego▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a211 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-10, Section: B.
■500 ▼aAdvisor: Todd, Michael D.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2024.
■520 ▼aInland 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.
■590 ▼aSchool code: 0033.
■650 4▼aArchitectural engineering
■650 4▼aEnvironmental engineering
■650 4▼aWater resources management
■653 ▼aInfrastructure
■653 ▼aStructural damage
■653 ▼aStructural Health Monitoring
■653 ▼aInland waterways
■653 ▼aDeep learning
■690 ▼a0543
■690 ▼a0775
■690 ▼a0462
■690 ▼a0595
■71020▼aUniversity of California, San Diego▼bStructural Engineering.
■7730 ▼tDissertations Abstracts International▼g85-10B.
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160367▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
