Collaborative Research: Multi-Agent Adaptive Data Collection for Automated Post-Disaster Rapid Damage Assessment

协作研究:用于灾后自动化快速损害评估的多智能体自适应数据收集

基本信息

  • 批准号:
    2316653
  • 负责人:
  • 金额:
    $ 19.04万
  • 依托单位:
  • 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:
    2023-09-01 至 2026-08-31
  • 项目状态:
    未结题

项目摘要

In the immediate aftermath of a disaster, reconnaissance effort to identify building damage severity and distribution is critical for search and rescue and other time-sensitive decisions. However, existing data collection and analytical processes are less responsive to unforeseen and unexpected circumstances. Therefore, this project develops a novel adaptive data collection framework that constantly analyzes the most recent observations to determine and update the trajectory of data collector agents toward areas with the greatest potential for information gain. It enables these agents to collect reliable data under severe time and resource constraints. The outcomes of this project set the stage for automated damage assessment systems to improve the resilience of built environments and citizens in hazard-prone regions. A set of educational and outreach efforts are envisioned for broadly disseminating the research findings and integrating them into undergraduate and graduate courses.The adaptive data collection system is built on a novel hierarchical Bayesian framework for modeling disaster damage levels and a Bayesian optimization for adaptive destination identification and trajectory planning. This method first relies on a pre-disaster preliminary probabilistic model of physical damage levels for different types of structures at the census tract level of granularity using priori information and spatial attributes available to the public. It then creates and constantly updates distributions of physical damage levels across census tracts by integrating with the collected data from visited zones. Next, it dynamically and adaptively determines the trajectories for multiple agents to maximize the information gain in the shortest possible time. The Bayesian probabilistic models could be transferable to other complex problems such as environmental pollution assessment.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.
在灾难发生后,确定建筑物损坏严重程度和分布的侦察工作对于搜救和其他时间敏感的决策至关重要。然而,现有的数据收集和分析过程对不可预见和意外情况的反应较差。因此,该项目开发了一种新颖的自适应数据收集框架,该框架不断分析最新的观察结果,以确定和更新数据收集代理的轨迹,以获得最大的信息潜力。它使这些代理能够在严格的时间和资源限制下收集可靠的数据。该项目的成果为自动损害评估系统奠定了基础,以提高灾害易发地区建筑环境和公民的复原力。为广泛传播研究成果并将其纳入本科和研究生课程,设想了一系列教育和推广工作。自适应数据采集系统建立在一种新的分层贝叶斯框架上,用于灾害破坏级别建模,并用于自适应目的地识别和轨迹规划的贝叶斯优化。该方法首先利用公众可用的先验信息和空间属性,在人口普查区粒度级别上建立了不同类型结构的灾前物理破坏程度的初步概率模型。然后,它通过整合从访问区域收集的数据,创建并不断更新人口普查区的物理破坏水平分布。其次,它动态自适应地确定多个智能体的运动轨迹,以在最短的时间内获得最大的信息增益。贝叶斯概率模型可以应用于其他复杂问题,如环境污染评价。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。

项目成果

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Mohammad Ilbeigi其他文献

A Gamified Method for Construction Engineering Education: Learning through Guided Active Exploration
建筑工程教育的游戏化方法:通过引导式主动探索进行学习
  • DOI:
    10.1061/jceecd.eieng-2019
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    1.6
  • 作者:
    Mohammad Ilbeigi;D. Bairaktarova;Romina Ehsani
  • 通讯作者:
    Romina Ehsani
Citizens’ Socioeconomic Background and Energy Accessibility during Extreme Events
极端事件期间公民的社会经济背景和能源可及性
TEACHING LINEAR SCHEDULING THROUGH GAMIFIED ACTIVE EXPLORATION
通过游戏化主动探索教授线性调度
Modeling and Forecasting the Price of Asphalt Cement Using Generalized Auto Regressive Conditional Heteroscedasticity
使用广义自回归条件异方差对沥青水泥价格进行建模和预测
  • DOI:
    10.1061/9780784479827.071
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mohammad Ilbeigi;A. Joukar;B. Ashuri
  • 通讯作者:
    B. Ashuri
AUTOMATION IN CONSTRUCTION: EDUCATION AND TRAINING FOR WORKFORCE DEVELOPMENT
建筑自动化:劳动力发展的教育和培训

Mohammad Ilbeigi的其他文献

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{{ truncateString('Mohammad Ilbeigi', 18)}}的其他基金

Collaborative Research: Research Initiation: Understanding of Engineering Core Concepts Contextualized in Domain-Specific Settings Through Active Exploration
协作研究:研究启动:通过主动探索理解特定领域背景下的工程核心概念
  • 批准号:
    2106257
  • 财政年份:
    2021
  • 资助金额:
    $ 19.04万
  • 项目类别:
    Standard Grant
Collaborative Research: A Dynamic Disruption Prediction System for Transportation Networks at a Road-Segment Level of Granularity
合作研究:路段粒度级交通网络动态中断预测系统
  • 批准号:
    2026795
  • 财政年份:
    2020
  • 资助金额:
    $ 19.04万
  • 项目类别:
    Standard Grant

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