CAREER: An Integrated Framework for Resilience Analytics: From Physics-based Modeling of Building Components to Dynamics of Community Level Recovery

这个教师早期职业发展（Career）项目将进行基础研究，推进弹性分析领域，提高民用基础设施系统应对自然灾害的能力。该研究将通过开发一种整体方法来定义和量化从单个建筑物到整个社区的多个尺度上的基础设施损坏和功能完整性（在危险事件发生后继续运行的能力），从而推动基础设施恢复能力的前沿。这项研究将解释弹性的概率性质。这项研究将提供定量数据，为灾害易发地区的设计、适应和缓解战略提供信息，从而显著减少自然灾害的经济损失和负面社会影响。此外，基础设施子系统（例如建筑物）内部和之间的依赖关系的集成允许系统地描述损坏和中断的类型和程度、供应链响应以及如何在社区规模上实现恢复的估计之间的关系。此外，该提案从教育和推广的角度提供了积极的社会影响的机会。相关活动包括培训具有计算能力和经验的弹性工程下一代学者，并利用PIs在包容性和多样性方面的良好记录，扩大代表性不足群体在STEM领域的参与。本研究的具体目标是通过结合理论和计算研究来推进基础设施弹性的边界，从而对弹性的所有维度进行全面、系统和有效的评估。实现这一目标的活动包括：(i)调整统计物理学和离散建模技术，以模拟结构和非结构要素和子系统的破坏类型和程度，并预测系统的功能完整性。主要思想是利用统计物理和离散建模技术的多功能性来建模复杂的故障机制，以及它们在定义性能和脆弱性之间的精确关联方面的能力。对复杂结构行为建模的适应性补充了局部和全局结构阻尼机制的建模策略；（二）发展新的学习策略，这种策略以智能和最佳抽样和多保真度信息融合为基础，以便在发生低概率极端事件时准确和有效地估计损害（和损失）的统计数字；（iii）设计一个系统动力学平台，为供应链响应建模，该模型通过整合建筑内部和内部的依赖关系来估计社区级的总体损害和恢复轨迹。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。

This Faculty Early Career Development (CAREER) program will perform fundamental research that advances the field of resilience analytics and enhances the ability of civil infrastructure systems to cope with natural disasters. The research will push the frontier on infrastructure resilience by developing a holistic methodology to define and quantify infrastructure damage and functional integrity (the ability to continue to function after a hazard event) at multiple scales ranging from a single building to an entire community. The research will account for the probabilistic nature of resilience. The research will provide quantitative data that inform design, adaptation and mitigation strategies in hazard prone areas, and as such lead to significant reductions in economic loss and negative societal impacts of natural hazards. Furthermore, the integration of dependencies within and between infrastructure subsystems (e.g. buildings) allows for systematic characterization of the relationship between the type and extent of damage and disruption, the supply chain response, and the estimation of how recovery can be achieved at the community scale. In addition, this proposal provides opportunities for positive societal impacts from the perspectives of education and outreach. Relevant activities include training the next generation of scholars in resilience engineering with strong emphasis and experience on computation, and broadening the participation of underrepresented groups in STEM fields by capitalizing on the PIs strong track record on impactful commitment to inclusiveness and diversity. The specific goal of this research is to advance the boundaries of infrastructure resilience through integration of a combined theoretical and computational study that allows for a holistic, systematic and efficient evaluation of all dimensions of resilience. The activities to achieve this goal include: (i) Adapting statistical physics and discrete modeling techniques to model the type and extent of damage in structural and non-structural elements and subsystems, as well as to predict the functional integrity of the system. The primary idea is to leverage the versatility of statistical physics and discrete modeling techniques in modeling complex failure mechanisms and their capability in defining precise associations between performance and vulnerability. The adaptation to modeling complex structural behavior is complemented with strategies for modeling local and global structural damping mechanisms; (ii) Development of novel learning strategies that rest on smart and optimal sampling and multi-fidelity information fusion allowing for accurate and efficient estimation of the statistics of damage (and loss) in the presence of low-probability extreme events; (iii) Devising a systems dynamics platform for modeling supply chain response that feeds from integration of inter- and intra-building dependencies for estimation of aggregated community level damage and recovery trajectory.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.