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.

在低概率或非凡事件（例如爆炸，地震，火灾或撞击）失败后，美国的许多现有建筑物都有部分或完全崩溃的危险。在该奖项中，将使用多个数据收集系统来监视两个钢筋混凝土停车场结构的损坏进度和动态行为，而在俄亥俄州立大学校园预定拆除之前，将几列从结构中进行物理删除。来自无人机，立体相机数据收集系统以及各种传感器的数据将用于捕获每个列丢失后结构的动态响应的运动和变化。关于崩溃的实验研究有限，因为它很难在实验室中构造和测试全尺寸建筑标本，而且这种大规模测试很昂贵。该项目将使用收集的实验数据来促进当前的结构损害和崩溃评估程序，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，并通过与专业组织的互动来转移到社区，以制定技术文档和建筑崩溃评估和结构设计指南。项目数据将在NSF支持的自然危害工程研究基础设施数据台（https://www.designsignsafe-ci.org）中归档并公开提供。该奖项将有助于NSF在国家地震危害计划（NEHRP）中的作用。数据融合和基于机器视觉的方法的最新进展可以自动检测损坏和监测结构的动态响应。无人机，相机，激光镜头，位移传感器和应变测量值收集的实验数据将融合，以捕获每个列损耗后测试结构的动态特征的变化。由于缺乏测试数据以及从现场观察结果分析这种现象的困难，因此对建筑物内的三维（3D）负载重新分布的理解很少。该项目将：1）开发简化的结构模型，以表征一个或多个列突然在建筑物中丢失后的稳定性和负载重新分配机制，2）引入数据融合技术以进行损害检测和监视，3）3）开发4D或时间依赖于时间的3D映射建筑物的建筑物以提高对建筑物的范围和崩溃的范围。使用基金会的智力优点和更广泛的影响评估标准进行评估。

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

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

DOI：
10.1109/icpr48806.2021.9413041
发表时间：
2021-01-01
期刊：
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
影响因子：
0
作者：
Bai, Yongsheng;Sezen, Halil;Yilmaz, Alper
通讯作者：
Yilmaz, Alper

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

DETECTING CRACKS AND SPALLING AUTOMATICALLY IN EXTREME EVENTS BY END-TO-END DEEP LEARNING FRAMEWORKS

DOI：
10.5194/isprs-annals-v-2-2021-161-2021
发表时间：
2021-06
期刊：
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
影响因子：
0
作者：
Y. Bai;H. Sezen;A. Yilmaz
通讯作者：
Y. Bai;H. Sezen;A. Yilmaz