Collaborative Research: Resource-Constrained Optimal Learning Framework for Post-Seismic Regional Building Damage Inference

这笔赠款将支持研究，这些研究将为震后建筑破坏评估贡献与资源分配有关的新知识，促进科学进步和维护国家福利。在地震或其他灾害后的紧急救援行动中，准确评估受影响地区的基础设施受损情况至关重要。在劳动密集型的侦察调查能够检查每一栋建筑之前，关键资源必须迅速分配。因此，应优先进行某些检查，以便及时提供最佳损失评估调查，使第一反应救济工作受益。该奖项支持基础研究，以开发一个数学建模框架，指导视察队完成震后侦察任务。这一新方法将全面确定要优先检查的建筑物，并设计检查时间表，以便在有限的时间和检查人员的情况下有效地访问这些建筑物。这项研究的结果将加快区域灾害损失评估，这将改善灾害管理，从而有助于拯救人类生命，确保资源的道德分配，并维护我们社会的福利。该项目将培养未来的土木工程师、数学家和统计学家具有多学科知识，并将扩大代表不足的群体参与对工程教育产生积极影响的研究。这项研究将统计和最优学习的概念与路线和调度模型相结合。这两个方面已经单独进行了广泛的研究，但从未联合进行过。这一知识差距对视察队的指导构成了严重挑战，视察队在资源有限的情况下收集实地信息。研究小组将开发一个集成的建模框架，将最优学习和组合优化联系起来，以确定检查路线和时间表，以最大限度地提高机器学习模型的预测能力，用于震后建筑破坏评估。新方法将在真实世界的基准上进行验证，包括2011年智利和2015年尼泊尔地震的数据，以及旧金山湾的区域地震模拟试验台。这一结果将改善危机管理，同时也为面临数据成本和信息收益之间严格权衡的其他领域(如健康和疾病控制)提供新的见解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This grant will support research that will contribute new knowledge related to resource allocation for post-seismic building damage assessment, promoting the progress of science and preserving the national welfare. During emergency relief operations after an earthquake or other disaster, it is critical to accurately assess the infrastructural damage across the impacted region. Critical resources must be allocated quickly, before labor-intensive reconnaissance surveys are able to inspect each building. Thus, certain inspections should be prioritized in order to deliver an optimal damage assessment survey in time to benefit first-response relief efforts. This award supports fundamental research to develop a mathematical modeling framework to guide inspection teams through post-seismic reconnaissance missions. This new approach will holistically identify buildings to be prioritized for inspection and design inspection schedules to efficiently visit these buildings with limited time and inspection crew members. Results from this research will expedite regional hazard damage assessment, which will improve disaster management and thereby help save human lives, ensure ethical resource allocation, and preserve the welfare of our society. The project will prepare future civil engineers, mathematicians and statisticians with multi-disciplinary knowledge, and will broaden the participation of underrepresented groups in research which positively impact engineering education.This research integrates concepts from statistical and optimal learning with models for routing and scheduling. These two aspects have been extensively studied separately, but never jointly. This knowledge gap poses a serious challenge to the guidance of inspection teams, which collect information in the field subject to resource constraints. The research team will develop an integrated modeling framework which bridges optimal learning and combinatorial optimization to identify inspection routes and schedules that maximize the predictive power of machine learning models for post-seismic building damage assessment. The new methodology will be validated on real-world benchmarks, including data from the 2011 Chile and 2015 Nepal earthquakes, as well as a regional earthquake simulation testbed for the San Francisco Bay. The results will improve crisis management, while also providing new insights into other domains (such as health and disease control) that face a tight tradeoff between data expense and information gain.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.