Multimodal Disaster Impact Assessment Models for Enhanced Resilience

增强抵御能力的多模式灾害影响评估模型

• 批准号: 2242767
• 负责人: Henry Burton
• 金额: $ 39.99万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2026-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242767&HistoricalAwards=false
• 关键词: Multimodal; Disaster; Impact; Assessment; Models

Immediately after a major hazard event (e.g., wildfire, earthquake, flood), a prompt assessment of the geographic distribution and severity of infrastructure damage is vital to the success of the emergency response and early recovery planning. This situational awareness is an important part of the decision-making processes that are implemented by facility owners, users, emergency responders and local and state officials. Conversely, a general lack of knowledge about the impacted state of the built environment can lead to a disorganized public response and slower recovery. While a comprehensive assessment of the extent and distribution of infrastructure damage can be obtained from in-person inspections conducted by building professionals, depending on the scale of the event, this can be a lengthy, resource intensive process. This Disaster Resilience Research Grants (DRRG) project will address this challenge by utilizing principles from artificial intelligence (AI) to develop near real-time infrastructure damage prediction models that can process and utilize different types of data and information (e.g., images, text, tabular data). By advancing our ability to effectively integrate disparate information sources, this project aims to transform the way that physical damage to infrastructure is estimated in the aftermath of a major disaster event, thereby enhancing the emergency response and recovery planning phases that follow. The research will provide training for doctoral and masters students and an opportunity to teach undergraduates from different backgrounds how science and engineering coupled with AI technologies can be used to improve community response to extreme events.Fundamental concepts and methodological advancements in multimodal learning will be used to transform and enhance infrastructure damage prediction models for use in the immediate post-event environment. The state-of-the-art in image-based damage assessment will be advanced along two dimensions: (1) developing Vision Transformer-based methods and (2) establishing a self-supervised learning methodology for training the models using large collections of unlabeled data. A new knowledge base will be established around the broad area of multimodal data fusion for infrastructure damage prediction models. Specific questions regarding unified representation, translation across and alignment between modalities and data fusion will be answered. A new type of hazard-agnostic infrastructure damage prediction model will also emerge from this research. Such a model will have the ability to receive an integrated representation of one or more types of input modalities (i.e., image, text, and engineering/tabular data) and produce, as output, an infrastructure damage level that is agnostic to the type of causal event (e.g., earthquake or hurricane). Using a comprehensive data set from multiple natural hazard events (hurricane and earthquake), the project will include experiments to shed new light on the ability of both hazard-specific and hazard-agnostic multimodal models to enhance early-stage infrastructure damage assessments for increased situational awareness and enhanced resilience.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.

在发生重大危害事件（例如野火，地震，洪水）之后，立即对基础设施损害的地理分布和严重性进行迅速评估对于紧急响应和早期恢复计划的成功至关重要。这种情境意识是由设施所有者，用户，紧急响应者以及地方和州官员实施的决策过程的重要组成部分。相反，人们普遍缺乏对建筑环境影响状态的知识，可能会导致公众反应混乱和恢复较慢。虽然可以从建筑专业人员进行的面对面检查中对基础设施损害的范围和分布进行全面评估，这取决于事件的规模，但这可能是一个冗长的，资源密集的过程。这项灾难弹性研究补助金（DRRG）项目将通过利用人工智能（AI）的原则来应对这一挑战，以开发近实时基础架构损害预测模型，这些模型可以处理并利用不同类型的数据和信息（例如图像，文本，表格数据）。通过提高我们有效整合不同信息源的能力，该项目旨在改变在重大灾难事件后估计基础设施的物理损害的方式，从而增强了随后的紧急响应和恢复计划阶段。这项研究将为博士和硕士学生提供培训，并有机会教授不同背景的本科生，科学和工程以及AI技术的结合如何可用于改善社区对极端事件的反应。多模式学习的基础概念和方法学的进步将用于转化和增强基础结构损害损害模型，以实现直接范围内的现有环境。基于图像的损害评估中最新的损害评估将沿两个维度进行：（1）开发基于视觉变压器的方法，以及（2）建立一种自我监督的学习方法，用于使用大量未标记数据培训模型来培训模型。将在基础设施损害预测模型的多模式数据融合的广泛领域建立一个新的知识库。有关统一表示形式，跨越模式和数据融合之间的一致性的具体问题。这项研究还将出现一种新型的危害 - 反应基础设施损害预测模型。这种模型将有能力接收一种或多种类型的输入模式（即图像，文本和工程/表格数据）的集成表示，并作为输出产生一种基础架构损害水平，这对因果事件的类型（例如地震或飓风）而言是不可思议的。使用来自多个自然危害事件（飓风和地震）的全面数据集，该项目将包括实验，以使危险特定和危害 - 危险性和危险性多模型模型的能力有了新的启示，以增强早期基础设施损害评估，以提高情境意识和通过增强的恢复性奖励。优点和更广泛的影响审查标准。