Field Testing of Concrete Buildings for Damage and Collapse Assessment

在爆炸、地震、火灾或撞击等低概率或特殊事件中，一根或多根柱子倒塌后，美国许多现有建筑物都有部分或完全倒塌的危险。在该合同中，多个数据收集系统将用于监测两个钢筋混凝土停车场结构的损伤进展和动态行为，同时在俄亥俄州立大学校园内，几个柱子在计划拆除之前从结构上物理移除。来自无人机、立体摄像数据采集系统和各种传感器的数据将被用来捕捉每根柱子丢失后结构的运动和动态响应变化。由于在实验室中很难制作和测试全尺寸的建筑试件，而且这种大规模的测试费用昂贵，因此对倒塌的实验研究有限。该项目将利用收集到的实验数据推进当前的结构损伤和倒塌评估程序，从而填补当前知识状态的关键空白。研究结果将通过出版物和演讲传播，并通过与制定建筑物倒塌评估和结构设计技术文件和指南的专业组织的互动，转移到社区。项目数据将存档并在nsf支持的自然灾害工程研究基础设施数据库（https://www.DesignSafe-CI.org）中公开提供。该奖项将有助于NSF在国家减少地震灾害计划（NEHRP）中的作用。数据融合和基于机器视觉的方法的最新进展使结构的损伤自动检测和动态响应监测成为可能。将无人机、摄像头、激光雷达、位移传感器、应变仪采集的实验数据进行融合，捕捉试验结构在每次失柱后的动态特性变化。由于缺乏测试数据和难以从现场观测中分析这种现象，人们对建筑物内的三维（3D）荷载重新分配知之甚少。该项目将：1)开发简化的结构模型，以表征建筑物中一根或多根柱子突然丢失后的稳定性和荷载重新分配机制；2)引入数据融合技术，用于损伤检测和监测；3)开发建筑物的4D或随时间变化的3D映射，以推进对建筑物动态性能和倒塌机制的工程理解。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。

Many existing buildings in the United States are in danger of partial or complete collapse after one or more columns fail during low probability or extraordinary events such as blast, earthquake, fire or impact. In this award, multiple data collection systems will be used to monitor the damage progression and dynamic behavior of two reinforced concrete parking garage structures while several columns are physically removed from the structures prior to their scheduled demolition on the Ohio State University campus. Data from drones, a stereo camera data collection system, and various sensors will be used to capture the movement and change in dynamic response of the structures after each column loss. There is limited experimental research on collapse because it is difficult to construct and test full-scale building specimens in the laboratory, and such large-scale testing is expensive. This project will advance current structural damage and collapse assessment procedures using the collected experimental data, thus filling a critical gap in current state of knowledge. The research results will be disseminated through publications and presentations and transferred to the community through interactions with professional organizations developing technical documents and guidelines for building collapse assessment and structural design. Project data will be archived and made publicly available in the NSF-supported Natural Hazards Engineering Research Infrastructure Data Depot (https://www.DesignSafe-CI.org). This award will contribute to NSF's role in the National Earthquake Hazards Reduction Program (NEHRP). Recent advancements in data fusion and machine vision-based methods enable automatic detection of damage and monitoring of dynamic response of structures. The experimental data collected by drones, cameras, LiDAR, displacement sensors, and strain gauges will be fused to capture the change in dynamic characteristics of the test structures after each column loss. Three-dimensional (3D) load redistribution within a building is poorly understood because of lack of test data and the difficulty in analyzing this phenomenon from field observations. This project will: 1) develop simplified structural models to characterize stability and load redistribution mechanisms after one or more columns are suddenly lost in a building, 2) introduce data fusion techniques for damage detection and monitoring, and 3) develop 4D or time-dependent 3D mapping of the buildings to advance engineering understanding of dynamic performance and collapse mechanism of buildings.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.