RAPID: Urgent Collection of Perishable Condition Data from Structures Affected by the Haiti Earthquake

这项快速反应研究（Rapid）拨款的目的是开发一种快速、基于图像的半自动化方法，用于评估钢筋混凝土结构的损坏和倒塌风险，以减少所需的时间，并提高事后检查的可靠性。美国最近发生的地震的后果表明，即使是影响大都市地区的中等强度地震，也可能需要数周或数月的时间来检查并允许进入受损建筑物。在这段时间里，家庭没有住房，企业没有设施，复苏被推迟。研究小组试图通过开发一种快速、基于图像的半自动化方法来评估钢筋混凝土结构的损伤和倒塌风险，从而减少所需的时间并提高事后检查的可靠性。利用该方法，对受损结构的视频进行处理，识别和表征损伤，并利用这些损伤数据来估计结构构件的性能状态，最终确定结构的倒塌易损性。该研究小组将前往海地，收集在最近地震中受损的混凝土建筑的损坏数据和设计信息。这些数据将用于验证图像处理算法和损伤评估技术，从而推进所设想的快速检查方法。数据收集活动对研究小组的研究生成员来说是一次重要的教育经历。研究小组收集的全面的、基于图像的数据集将供世界各地的地震工程教育工作者和研究人员使用。

The objective of this Rapid Response Research (RAPID) grant is to develop a rapid, image-based, semi-automated method for assessing damage and collapse risk for reinforced concrete structures to both reduce the time needed for, and to improve the reliability of, post-event inspection. The aftermath of recent earthquakes in the United States suggests that for even a moderate intensity earthquake affecting a metropolitan area, it could take weeks or months to inspect, and thereby grant access to, damaged buildings. During this time, families are without homes, businesses are without facilities, and recovery is delayed. The research team seeks to both reduce the time needed for and improve the reliability of post-event inspection by developing a rapid, image-based, semi-automated method for assessing damage and collapse risk for reinforced concrete structures. Using this method, video of damaged structures is processed to identify and characterize damage and these damage data are used to estimate the performance state of structural components and, ultimately, the collapse vulnerability of the structure.The research team will travel to Haiti and collect damage data and design information for concrete buildings damaged during the recent earthquake. These data will be used to validate image processing algorithms and damage assessment techniques and, thereby, to advance the envisioned rapid inspection method. The data collection activities represent a significant educational experience for the graduate student members of the research team. The comprehensive, image-based data sets collected by the research team will be made available for use by earthquake engineering educators and researchers around the world.