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

• 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

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.

在美国，许多现有的建筑物在一个或多个柱在低概率或特殊事件（如爆炸、地震、火灾或撞击）中失效后处于部分或完全倒塌的危险中。在这个奖项中，多个数据收集系统将被用来监测损坏的进展和动态行为的两个钢筋混凝土停车库结构，而几个列物理删除的结构之前，他们预定的拆除俄亥俄州州立大学校园。来自无人机、立体摄像机数据收集系统和各种传感器的数据将用于捕捉每次柱损失后结构的动态响应的移动和变化。倒塌的实验研究有限，因为很难在实验室中建造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。该项目将使用收集的实验数据推进当前的结构损伤和倒塌评估程序，从而填补了当前知识水平的关键空白。研究结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传播，为建筑物倒塌评估和结构设计制定技术文件和指南。项目数据将在NSF支持的自然灾害工程研究基础设施数据库（https：//www.example.com）中存档并公开提供。www.DesignSafe-CI.org 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。无人机、相机、激光雷达、位移传感器和应变计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。三维（3D）的建筑物内的负荷重分布是知之甚少，因为缺乏测试数据和难以分析这种现象，从现场观察。该项目将：1）开发简化的结构模型以表征在建筑物中突然失去一个或多个柱之后的稳定性和载荷重新分配机制，2）引入用于损伤检测和监测的数据融合技术，3）发展4D或时间-独立的建筑物三维映射，以提高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，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。

项目成果

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
• 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

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
• 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
• 作者: Y. Bai;H. Sezen;A. Yilmaz

Automatic Displacement and Vibration Measurement in Laboratory Experiments with A Deep Learning Method

• DOI: 10.1109/sensors47087.2021.9639455
• 发表时间: 2021-09
• 期刊: 2021 IEEE Sensors
• 作者: Y. Bai;R. M. Abduallah;H. Sezen;A. Yilmaz