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Physics-Informed Data-Driven Methods for Long-Term Structural Health Monitoring of Concrete Structures
Physics-Informed Data-Driven Methods for Long-Term Structural Health Monitoring of Concrete Structures
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152939
- ISBN
- 9798346759430
- DDC
- 620.11
- 서명/저자
- Physics-Informed Data-Driven Methods for Long-Term Structural Health Monitoring of Concrete Structures
- 발행사항
- [Sl] : Princeton University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 151 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Glisic, Branko.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2024.
- 초록/해제
- 요약Concrete exhibits long-term time-dependent behavior due to rheological effects that impacts the safety and serviceability of civil infrastructure, such as bridges and high-rise buildings. In real concrete structures, long-term prediction is challenging due to the presence of variable environment conditions, such as temperature, humidity, as well as uncertainty in loading and boundary conditions, and the random nature of creep and shrinkage. Existing cutting-edge methods for prediction of long-term time-dependent behavior at structural scale involves computationally intensive numerical methods in which several semi-empirical rheological models are applied. However, these models are targeted at structural design and adjusted based on experimental data that may not be the most accurate for an existing structure. Structural health monitoring can improve long-term prediction by providing structure-specific in-situ measurements. This dissertation introduces novel methods for the prediction of long-term behavior of concrete structures that integrate structural health monitoring, structural modeling and analysis, and machine learning. A new method proposed integrates probabilistic neural networks and analytical structural model, together with generalized creep and shrinkage models, for the detection of gradual anomalies in real structural data that are difficult to detect with existing methods. A creative integration of analytical structural modeling and neural networks is used for reconstruction of 2D normal strain field on a real structure over multiple years. Finally, a new method for the prediction of long-term behavior in high-rise buildings integrates analytical modeling with generalized creep and shrinkage models, with rigorous uncertainty quantification, is presented. The dissertation contributes to the literature in long-term monitoring of structures with innovative integration of structural modeling, and machine learning.
- 일반주제명
- Materials science
- 키워드
- Creep
- 키워드
- Shrinkage
- 기타저자
- Princeton University Civil and Environmental Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152939
■006m o d
■007cr#unu||||||||
■020 ▼a9798346759430
■035 ▼a(MiAaPQ)AAI31564613
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a620.11
■1001 ▼aPereira, Mauricio.
■24510▼aPhysics-Informed Data-Driven Methods for Long-Term Structural Health Monitoring of Concrete Structures
■260 ▼a[Sl]▼bPrinceton University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a151 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Glisic, Branko.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2024.
■520 ▼aConcrete exhibits long-term time-dependent behavior due to rheological effects that impacts the safety and serviceability of civil infrastructure, such as bridges and high-rise buildings. In real concrete structures, long-term prediction is challenging due to the presence of variable environment conditions, such as temperature, humidity, as well as uncertainty in loading and boundary conditions, and the random nature of creep and shrinkage. Existing cutting-edge methods for prediction of long-term time-dependent behavior at structural scale involves computationally intensive numerical methods in which several semi-empirical rheological models are applied. However, these models are targeted at structural design and adjusted based on experimental data that may not be the most accurate for an existing structure. Structural health monitoring can improve long-term prediction by providing structure-specific in-situ measurements. This dissertation introduces novel methods for the prediction of long-term behavior of concrete structures that integrate structural health monitoring, structural modeling and analysis, and machine learning. A new method proposed integrates probabilistic neural networks and analytical structural model, together with generalized creep and shrinkage models, for the detection of gradual anomalies in real structural data that are difficult to detect with existing methods. A creative integration of analytical structural modeling and neural networks is used for reconstruction of 2D normal strain field on a real structure over multiple years. Finally, a new method for the prediction of long-term behavior in high-rise buildings integrates analytical modeling with generalized creep and shrinkage models, with rigorous uncertainty quantification, is presented. The dissertation contributes to the literature in long-term monitoring of structures with innovative integration of structural modeling, and machine learning.
■590 ▼aSchool code: 0181.
■650 4▼aMaterials science
■650 4▼aEnvironmental engineering
■653 ▼aAnomaly detection
■653 ▼aCreep
■653 ▼aPredictive modeling
■653 ▼aStructural health monitoring
■653 ▼aUncertainty quantification
■653 ▼aShrinkage
■690 ▼a0543
■690 ▼a0794
■690 ▼a0775
■71020▼aPrinceton University▼bCivil and Environmental Engineering.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164253▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
