发表机构
School of Computing and Augmented Intelligence, Arizona State University; University of Florida; Stevens Institute of Technology（亚利桑那州立大学计算与增强智能学院; 佛罗里达大学; 史蒂文斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对灾后损伤评估的传统方法成本高且难以适应动态条件，本研究提出结合水平集估计的成本感知贝叶斯优化框架，引导自主数据采集器流向高信息区域，经模拟和R2D数据验证可高效评估损伤以支持应急响应。
AI 中文摘要
自然灾害频繁对建筑环境造成严重破坏，这需要快速、可靠且具有成本效益的损伤评估以支持应急响应。然而，传统的灾后损伤评估方法往往依赖静态、劳动密集型的数据采集策略，这些策略可能成本过高，且难以适应灾后动态条件。本研究中，我们提出一种成本感知贝叶斯优化框架，结合水平集估计，持续引导自主数据采集器（例如无人机UAV）流向信息最丰富的区域。通过动态更新不同地理区域的损伤估计，我们的方法系统地降低不确定性，同时将运营成本降至最低。该框架首先通过受控合成模拟研究进行验证，证明智能体能够有效追踪损伤边界、恢复潜在损伤图并快速降低预测不确定性。此外，该方法还使用区域韧性测定R2D软件生成的高保真灾害数据进行评估。算法结果提供准确且及时的损伤估计，支持信息充分且快速的应急响应。
英文摘要
Natural disasters frequently inflict severe damage to the built environment, which demands a rapid, reliable, and cost-effective damage assessment for emergency response. However, traditional methods for post-disaster damage assessment often rely on static, labor-intensive data collection strategies that can be prohibitively expensive and struggle to adapt to dynamic post-disaster conditions. In this study, we propose a cost-aware Bayesian optimization framework combined with level-set estimation that continuously guides autonomous data collectors, e.g., an unmanned aerial vehicle (UAV), toward the most informative regions. By dynamically updating damage estimates across different geographic zones, our approach systematically reduces uncertainty while minimizing operational costs. The proposed framework is first validated using a controlled synthetic toy study, demonstrating the agent's ability to efficiently trace damage boundaries, recover the underlying damage map, and rapidly reduce predictive uncertainty. Furthermore, the approach is evaluated using high-fidelity disaster data generated by the Regional Resilience Determination (R2D) software. The results of the algorithm provide accurate and timely damage estimates that support informative and fast emergency response.
Comments11 pages, 7 figures, 1 table. Accepted at the SIAM International Conference on Data Mining (SDM) 2026
