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

该项目的目的是开发和验证一个用于震后钢筋混凝土（RC）框架建筑检查的计算框架，该框架将能够快速、自动地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器上的高分辨率摄像机收集视频帧开始。建筑工人。然后，采用最先进的检测和提取算法来检测钢筋混凝土柱，识别和表征柱的损伤。构件损伤将采用基于经验的模型进行分类，构件损伤将用于确定建筑物的损伤状态。建筑物的损坏状态、结构和类型将用于查询一组易损性曲线，这些曲线定义了建筑物在余震中倒塌的可能性，从而提高了对风险的理解。交付成果包括基本可见损坏特征的目录、基于模型的识别和分析工具、通过硬件的演示和验证、研究成果的文档、工程学生教育以及对建筑评估人员的外展研讨会。如果成功，本研究的结果将为结构构件和视频损伤识别领域提供第一个鲁棒方法。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、虚拟和增强现实以及其他对工程界重要的应用中。此外，这将是已知的第一个使用稳健的概率方法定量地将建筑物构件（柱或墙）的视觉损伤与建筑物倒塌的可能性联系起来的研究。本项目所寻求的发现有望成为损伤评估新知识库的基础，并促进计算机视觉和结构工程领域之间的知识交流。结果将被传播，以允许创建商业软件，这些软件提高了精度，降低了成本，并且使用了重量更轻的设备。工程专业的研究生和本科生以及K-12学生将通过课堂教学和参与研究而受益。

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.