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Performance-Based Wind Engineering: Knowledge and Computational Modeling Advances for Collapse Characterization

基于性能的风工程：塌陷表征的知识和计算建模进展

基本信息

批准号：
2118488
负责人：
Seymour Spence
金额：
$ 41.48万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118488&HistoricalAwards=false
关键词：
Performance Based Wind Engineering Knowledge

项目摘要

Each year, extreme windstorms cause significant economic and societal losses in the United States. Current performance-based wind engineering (PBWE) methodologies do not provide a means to characterize the collapse performance of engineered building systems subject to extreme winds. This not only limits the design innovation necessary for achieving building systems that mitigate these losses at reduced costs and environmental impacts, but also hinders the development of the next generation of guidelines and standards for PBWE that would lead to the advancement of national prosperity and welfare through better performing building infrastructure. This award will provide the necessary advances in knowledge and modeling to address this fundamental gap through 1) creation of new knowledge on the probabilistic collapse capacity of typical engineered building systems subject to extreme winds, 2) creation of practical and validated methodologies for the rapid probabilistic collapse assessment of engineered building systems, and 3) technology transfer to the wind engineering practicing community. Impact on graduate and undergraduate education will be created through integrated research activities that foster greater participation of underrepresented groups by leveraging multiple programs at the University of Michigan. The computational methodologies resulting from this award will be shared through the National Science Foundation-supported Natural Hazards Engineering Research Infrastructure (NHERI) Computational Modeling and Simulation Center (https://simcenter.designsafe-ci.org/). Project data will be archived and made publicly available in NHERI’s Data Depot (https://www.designsafe-ci.org). This research will contribute to NSF's role in the National Windstorm Impact Reduction Program.This award will create a holistic collapse modeling environment for the structural and envelope systems of engineered buildings subject to extreme winds. A new class of multivariate stochastic pressure models will characterize the extreme loads associated with both synoptic and hurricane events. The damage susceptibility of the building system will be captured through coupling high-fidelity finite element modeling with progressive and interdependent multi-demand fragility analysis. Optimal stratified sampling schemes will enable rapid estimation of probabilities associated with rare events, e.g., system collapse, over a full range of wind load and modeling uncertainties. By leveraging the high-performance computing infrastructure of NHERI, fundamental knowledge on the collapse fragility of typical building systems will be created through the collapse analysis of a suite of archetype buildings specifically identified for the advancement of PBWE. This knowledge will provide the foundation for not only the development of practical and validated probabilistic methodologies for the holistic collapse assessment of engineered building systems, but also the advancement of guidelines and standards for PBWE.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

每年，极端风暴都会给美国造成重大的经济和社会损失。目前基于性能的风工程（PBWE）方法并没有提供一种方法来表征工程建筑系统在极端风作用下的倒塌性能。这不仅限制了实现以降低成本和环境影响来减轻这些损失的建筑系统所需的设计创新，而且还阻碍了下一代PBWE指南和标准的发展，这些指南和标准将通过更好的建筑基础设施来促进国家繁荣和福利。该奖项将在知识和建模方面提供必要的进步，通过以下途径解决这一基本差距：1)创造关于典型工程建筑系统在极端风下的概率倒塌能力的新知识；2)为工程建筑系统的快速概率倒塌评估创造实用且经过验证的方法；3)向风工程实践社区转让技术。将通过综合研究活动对研究生和本科教育产生影响，通过利用密歇根大学的多个项目，促进代表性不足群体的更多参与。由该奖项产生的计算方法将通过国家科学基金会支持的自然灾害工程研究基础设施（NHERI）计算建模和仿真中心（https://simcenter.designsafe-ci.org/）共享。项目数据将存档并在NHERI的数据仓库（https://www.designsafe-ci.org）中公开提供。这项研究将有助于美国国家科学基金会在国家减少风暴影响计划中的作用。该奖项将为受极端风影响的工程建筑的结构和围护结构系统创建一个整体的倒塌建模环境。一类新的多变量随机压力模型将描述与天气和飓风事件相关的极端负荷。建筑系统的损伤敏感性将通过高保真有限元建模与渐进和相互依赖的多需求脆弱性分析相结合来捕获。最佳分层抽样方案将能够快速估计与罕见事件相关的概率，例如，系统崩溃，在风荷载和建模不确定性的全范围内。通过利用NHERI的高性能计算基础设施，将通过对一套原型建筑的倒塌分析来创建典型建筑系统倒塌脆弱性的基本知识，这些原型建筑是专门为PBWE的发展而确定的。这些知识不仅将为工程建筑系统整体倒塌评估的实用和有效的概率方法的发展提供基础，而且还将为PBWE的指导方针和标准的进步提供基础。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Multi-fidelity Information Fusion for Efficient Estimation of Small Failure Probabilities Considering Multiple Limit States

考虑多个极限状态的多保真度信息融合有效估计小故障概率

DOI：
发表时间：
2023
期刊：
14th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP14
影响因子：
0
作者：
Li, Min;Arunachalam, Srinivasan;Spence, Seymour M.
通讯作者：
Spence, Seymour M.

Generalized Stratified Sampling for Efficient Reliability Assessment of Structures Against Natural Hazards

DOI：
10.1061/jenmdt.emeng-7021
发表时间：
2023-05
期刊：
ArXiv
影响因子：
0
作者：
S. Arunachalam;S. Spence
通讯作者：
S. Arunachalam;S. Spence

Validation and Error Quantification of Data-Informed Stochastic Wind Models for Performance-Based Wind Engineering Applications

基于性能的风工程应用的基于数据的随机风模型的验证和误差量化

DOI：
发表时间：
2023
期刊：
16th ICWE International Conference on Wind Engineering (ICWE16
影响因子：
0
作者：
Duarte, Thays G.;Arunachalam, Srinivasan;Subgranon, Arthriya;Spence, Seymour M.
通讯作者：
Spence, Seymour M.

Reliability-Based Collapse Assessment of Wind-Excited Steel Structures within Performance-Based Wind Engineering

基于性能的风工程中风激钢结构的基于可靠性的倒塌评估