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

合作研究：震后区域建筑损伤推断的资源受限最优学习框架

基本信息

批准号：
2112828
负责人：
Ilya Ryzhov
金额：
$ 16.17万
依托单位：
University of Maryland, College Park
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：
2021-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112828&HistoricalAwards=false
关键词：
Collaborative Research Resource Constrained Optimal

项目摘要

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.

该基金将支持为震后建筑损伤评估资源分配提供新知识的研究，促进科学进步，维护国家福利。在地震或其他灾害发生后的紧急救援行动中，准确评估受灾地区基础设施的损坏情况至关重要。在劳动密集型的侦察调查能够检查每座建筑之前，必须迅速分配关键资源。因此，某些检查应优先考虑，以便及时提供最佳的损害评估调查，以有利于第一反应救援工作。该合同支持基础研究，以开发数学建模框架，指导检查小组完成地震后侦察任务。这种新方法将从整体上确定需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有效地访问这些建筑物。本研究结果将加快区域灾害损失评估，从而改善灾害管理，从而有助于拯救人类生命，确保资源的道德分配，维护社会福利。该项目将培养具有多学科知识的未来土木工程师、数学家和统计学家，并将扩大代表性不足的群体参与对工程教育产生积极影响的研究。该研究将统计和最优学习的概念与路由和调度模型相结合。这两个方面分别被广泛研究过，但从未被联合研究过。这种知识差距对视察组的指导构成了严重的挑战，视察组在资源有限的情况下在实地收集信息。研究小组将开发一个集成的建模框架，将最佳学习和组合优化连接起来，以确定检查路线和时间表，从而最大限度地提高机器学习模型对地震后建筑损伤评估的预测能力。新方法将在现实世界的基准上得到验证，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。研究结果将改善危机管理，同时也为面临数据费用和信息获取之间的紧张权衡的其他领域（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。