Collaborative Research: Machine Vision Enhanced Post Earthquake Inspection and Rapid Loss Estimation

合作研究：机器视觉增强震后检查和快速损失估算

• 批准号: 1000440
• 负责人: Laura Lowes
• 金额: $ 17.5万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1000440&HistoricalAwards=false
• 关键词: Collaborative; Research; Machine; Vision; Enhanced

The objective of this project is to develop and validate a computational framework for post-earthquake inspection of reinforced concrete (RC) frame buildings that will enable rapid, automated assessment of the damage state of the structure and of the cost and time required to repair the structure. The proposed automated procedure will start with collection of video frames using a high-resolution video camera mounted on an inspector?s hardhat. Then, state-of-the-art detection and extraction algorithms will be employed to detect RC columns and identify and characterize column damage. Component damage will be classified using empirically based models, and component damage will be used to determine the damage state of the building. Building damage state, configuration and type will be used to query a set of fragility curves defining the likelihood of building collapse during an aftershock and, thereby, provide an improved understanding of risk. Deliverables include a catalog of fundamental visible damage characteristics, model-based recognition and analysis tools, demonstration and validation via hardware, documentation of research results, engineering student education, and outreach seminars to building evaluators.If successful, the results of this research will provide the first robust method in the area of structural member and damage recognition from video. This scientific breakthrough will allow researchers to integrate this work in as-built building information modeling, project monitoring, virtual and augmented reality and other applications of importance to the engineering community. Also, this will be the first known study to quantitatively link visual damage in a building component (column or wall), to the likelihood of building collapse using robust probabilistic methods. The discoveries sought in this project are expected to serve as a foundation for a new knowledge base in damage assessment and to promote intellectual cross-pollination among the fields of computer vision and structural engineering. The results will be disseminated to allow the creation of commercial software that have increased precision, reduced cost, and work with reduced weight devices. Graduate and undergraduate engineering students and K-12 students will benefit through classroom instruction and involvement in the research.

该项目的目标是开发和验证一个计算框架，用于钢筋混凝土（RC）框架建筑物的地震后检查，这将使快速，自动评估结构的损坏状态以及修复结构所需的成本和时间。拟议的自动化程序将从使用安装在检查员身上的高分辨率摄像机收集视频帧开始？的安全帽。然后，国家的最先进的检测和提取算法将被用来检测钢筋混凝土柱和识别和表征柱损伤。将使用基于经验的模型对组件损坏进行分类，并将使用组件损坏来确定建筑物的损坏状态。建筑物损坏的状态，配置和类型将被用来查询一组脆弱性曲线，确定建筑物倒塌的可能性在余震，从而提供了一个更好的了解风险。 这些成果包括基本可见损伤特征的目录、基于模型的识别和分析工具、通过硬件的演示和验证、研究结果的文档、工程专业学生教育以及针对建筑评估人员的推广研讨会。如果成功，这项研究的结果将为结构构件和损伤识别领域提供第一个可靠的方法。这一科学突破将使研究人员能够将这项工作整合到竣工建筑信息建模、项目监控、虚拟现实和增强现实以及其他对工程界具有重要意义的应用中。此外，这将是第一个已知的研究，定量地联系在建筑物组件（柱或墙）的视觉损坏，建筑物倒塌的可能性使用强大的概率方法。 该项目中寻求的发现有望成为损害评估新知识库的基础，并促进计算机视觉和结构工程领域的知识交叉。结果将被传播，以允许创建商业软件，提高精度，降低成本，并与减轻重量的设备一起工作。研究生和本科工程专业学生和K-12学生将通过课堂教学和参与研究受益。