Field Testing of Concrete Buildings for Damage and Collapse Assessment

混凝土建筑物损坏和倒塌评估的现场测试

基本信息

批准号：
2036193
负责人：
Halil Sezen
金额：
$ 30万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036193&HistoricalAwards=false
关键词：
Field Testing Concrete Buildings Damage

项目摘要

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.

美国许多现有建筑物在爆炸、地震、火灾或撞击等低概率或特殊事件中一根或多根柱子失效后，面临部分或完全倒塌的危险。在该奖项中，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坏进展和动态行为，同时在俄亥俄州立大学校园的预定拆除之前将几根柱子从结构中物理拆除。来自无人机、立体相机数据收集系统和各种传感器的数据将用于捕获每次柱损失后结构的运动和动态响应的变化。关于倒塌的实验研究有限，因为很难在实验室建造和测试全尺寸的建筑样本，而且这种大规模的测试成本高昂。该项目将利用收集到的实验数据推进当前的结构损坏和倒塌评估程序，从而填补当前知识水平的关键空白。研究成果将通过出版物和演示文稿传播，并通过与专业组织的互动，制定建筑倒塌评估和结构设计的技术文件和指南，并将其传递给社区。项目数据将在 NSF 支持的自然灾害工程研究基础设施数据仓库 (https://www.DesignSafe-CI.org) 中存档并公开提供。该奖项将有助于 NSF 在国家地震减灾计划 (NEHRP) 中发挥作用。数据融合和基于机器视觉的方法的最新进展使得能够自动检测损伤并监测结构的动态响应。无人机、摄像机、激光雷达、位移传感器和应变仪收集的实验数据将被融合，以捕捉每次柱损失后测试结构动态特性的变化。由于缺乏测试数据并且很难从现场观察中分析这种现象，人们对建筑物内的三维 (3D) 荷载重新分布知之甚少。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载荷重新分配机制，2) 引入用于损坏检测和监控的数据融合技术，以及 3) 开发 4D 或时间相关的模型建筑物的 3D 测绘可促进工程对建筑物动态性能和倒塌机制的理解。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Deep Convolutional Neural Networks for Comprehensive Structural Health Monitoring and Damage Detection

用于全面结构健康监测和损伤检测的深度卷积神经网络

DOI：
10.12783/shm2019/32491
发表时间：
2019
期刊：
12th International Workshop on Structural Health Monitoring
影响因子：
0
作者：
Zha, B;Bai, Y;Yilmaz, A;Sezen, H
通讯作者：
Sezen, H

A volumetric change detection framework using UAV oblique photogrammetry – a case study of ultra-high-resolution monitoring of progressive building collapse

DOI：
10.1080/17538947.2021.1966527
发表时间：
2021-08
期刊：
International Journal of Digital Earth
影响因子：
5.1
作者：
N. Xu;Debao Huang;Shuang Song;Xiao Ling;Chris Strasbaugh;A. Yilmaz;H. Sezen;R. Qin
通讯作者：
N. Xu;Debao Huang;Shuang Song;Xiao Ling;Chris Strasbaugh;A. Yilmaz;H. Sezen;R. Qin

DEEP CASCADED NEURAL NETWORKS FOR AUTOMATIC DETECTION OF STRUCTURAL DAMAGE AND CRACKS FROM IMAGES

DOI：
10.5194/isprs-annals-v-2-2020-411-2020
发表时间：
2020-08
期刊：
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
影响因子：
0
作者：
Y. Bai;B. Zha;H. Sezen;A. Yilmaz
通讯作者：
Y. Bai;B. Zha;H. Sezen;A. Yilmaz

End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales