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Uncovering Potential Risks of Wind-induced Cascading Damages to Construction Projects and Neighboring Communities

发现风致连锁损害对建设项目和邻近社区的潜在风险

基本信息

批准号：
1832187
负责人：
Youngjib Ham
金额：
$ 34.45万
依托单位：
Texas A&M University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-01-01 至 2021-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1832187&HistoricalAwards=false
关键词：
Uncovering Potential Risks Wind induced

项目摘要

Unstructured construction sites, including incomplete structures and unsecured resources (e.g., materials, equipment, and temporary facilities), are among the most vulnerable environments to windstorms such as hurricanes. Wind-induced damages to construction sites cause substantial losses, disruption, and considerable schedule delays, and thus negatively impact the efficiency of construction projects. Moreover, wind-induced damage caused by equipment, materials or structural elements, for example, originating from construction sites negatively affect neighboring communities, triggering structural damage, serious injuries, and casualties, as well as economic losses. This project will create and validate a new streamlined Imaging-to-Simulation framework to prevent wind hazard events from causing catastrophic damage to construction projects and neighboring communities. This project will benefit our society as it will significantly enhance current windstorm preparedness and mitigation plans, which ultimately promote public safety, property loss reduction, insurance cost reduction, and induce a culture of preparedness for disasters. This multidisciplinary research will help broaden participation of a new generation of young people in the Science, Technology, Engineering and Math (STEM) fields through integrated research and pedagogical activities.Using knowledge on potential at-risk construction resources obtained through experimental testing of extreme wind events, this project will partially or fully automatically model the current state of construction sites through machine vision techniques using multimodal visual data obtained from construction workers and camera-equipped unmanned aerial vehicles. To perform multi-physics simulation of multiple discrete objects in unstructured construction sites, an impulse-based discrete element method will be conceptualized. This method explicitly accounts for impulse-based dynamics by realizing efficient computing, which enable balancing between significant speed-up and reasonable simulation fidelity. The approach can describe the collective motion of mutually interacting components over time. Component-based vulnerability and impact analysis with 3D Building or Civil Information Models (BIM/CIMs) will then be conducted to generate fundamental and highly specific knowledge on wind-induced damage mechanisms. Finally, the entire system will be validated in real-world construction projects and within a 12-fan Wall of Wind facility that can generate up to hurricane category 5 wind speeds.

无结构的建筑工地，包括不完整的结构和无保障的资源（例如，材料、设备和临时设施）是最容易受到飓风等风暴影响的环境。建筑工地的风致破坏会造成重大损失、中断和相当大的进度延误，从而对建筑项目的效率产生负面影响。此外，由例如源自建筑工地的设备、材料或结构元件引起的风致损坏对邻近社区产生负面影响，引发结构损坏、严重伤害和人员伤亡以及经济损失。该项目将创建并验证一个新的简化的成像到模拟框架，以防止风灾事件对建筑项目和邻近社区造成灾难性破坏。该项目将使我们的社会受益，因为它将大大加强目前的风暴防备和减灾计划，最终促进公共安全，减少财产损失，降低保险成本，并诱导备灾文化。这项多学科研究将有助于通过综合研究和教学活动扩大新一代年轻人在科学、技术、工程和数学（STEM）领域的参与。利用通过极端风事件实验测试获得的潜在风险建筑资源知识，该项目将通过机器视觉技术，利用从以下方面获得的多模态视觉数据，部分或全部自动模拟建筑工地的当前状态：建筑工人和配备摄像头的无人机。为了对非结构化施工现场中的多个离散对象进行多物理场仿真，将概念化基于脉冲的离散元方法。该方法通过实现高效的计算来明确地考虑基于脉冲的动态，这使得能够在显著的加速和合理的仿真保真度之间进行平衡。该方法可以描述随着时间的推移相互作用的组件的集体运动。然后，将使用3D建筑或土木信息模型（BIM/CIM）进行基于可靠性的脆弱性和影响分析，以生成关于风致破坏机制的基本和高度具体的知识。最后，整个系统将在现实世界的建筑项目中进行验证，并在一个12风扇的风墙设施中进行验证，该设施可以产生高达5级飓风的风速。

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）

Participatory sensing-based geospatial localization of distant objects for disaster preparedness in urban built environments

DOI：
10.1016/j.autcon.2019.102960
发表时间：
2019-11
期刊：
Automation in Construction
影响因子：
10.3
作者：
Hongjo Kim;Youngjib Ham
通讯作者：
Hongjo Kim;Youngjib Ham

Systematic Camera Placement Framework for Operation-Level Visual Monitoring on Construction Jobsites

DOI：
10.1061/(asce)co.1943-7862.0001636
发表时间：
2019-04-01
期刊：
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
影响因子：
5.1
作者：
Kim, Jinwoo;Ham, Youngjib;Chi, Seokho
通讯作者：
Chi, Seokho

Automated Filtering Big Visual Data from Drones for Enhanced Visual Analytics in Construction

自动过滤来自无人机的大视觉数据，以增强施工中的视觉分析

DOI：
10.1061/9780784481264.039
发表时间：
2018
期刊：
ASCE Construction Research Congress 2018
影响因子：
0
作者：
Kamari, MirSalar;Ham, Youngjib
通讯作者：
Ham, Youngjib

Automated content-based filtering for enhanced vision-based documentation in construction toward exploiting big visual data from drones

DOI：
10.1016/j.autcon.2019.102831
发表时间：
2019-09
期刊：
Automation in Construction
影响因子：
10.3
作者：
Youngjib Ham;M. Kamari
通讯作者：
Youngjib Ham;M. Kamari

Camera Placement Optimization for Vision-based Monitoring on Construction Sites

建筑工地基于视觉的监控的摄像机放置优化

DOI：
10.22260/isarc2018/0102
发表时间：
2018
期刊：
Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC
影响因子：
0
作者：
Kim, Jinwoo;Ham, Youngjib;Chung, Yohun;Chi, Seokho
通讯作者：
Chi, Seokho