Spokes: MEDIUM: MIDWEST: Smart Big Data Pipeline for Aging Rural Bridge Transportation Infrastructure (SMARTI)
Spokes: MEDIUM: MIDWEST: Smart Big Data Pipeline for Aging Rural Bridge Transportation Infrastructure (SMARTI)
批准号:
1762034
负责人:
Robin Gandhi
金额:
$100.0万
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
项目状态:
已结题
起止时间:
2018-09-01 至 2023-08-31
中文摘要
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英文摘要
America's bridges received a C+ from the American Society of Civil Engineers (ASCE) in 2017. Additionally, the US ranks only 11th world-wide in terms of infrastructure competitiveness. America's infrastructure, particularly its 50-100+ year-old bridges, is in poor health, representing a hidden crisis. Rural areas, due to their lower population density and distance from urban centers, are acutely affected by this crisis, particularly in terms of public safety and economic growth. Limited budgets for planning and maintenance only serve to exacerbate the crisis. To better inform future research, the research team held collaborative conferences and workshops, and conducted stakeholder surveys to identify issues impacting rural bridge health. Outcomes from these activities underscored the value of big data technologies to address the crisis and informed the proposed work. The mission of this multi-institution and multi-sector project is to produce a big data pipeline for rural bridge health management that improves transportation network performance and enhances safety.The research team will combine existing and new datasets to address challenges of relevance to bridge owners using scalable and replicable big data pipeline components. Activities will inform bridge owner decision-making by integrating existing datasets and data collected using next-generation health monitoring technologies (e.g., contact and non-contact sensors, unmanned aerial vehicles) with innovative data management techniques. Socio-technical impacts associated with potential decisions will also be assessed. Aging, rural bridge testbeds will be selected in consultation with public and private owners to produce data products that facilitate decision-making and ultimately, provide economical and reliable solutions that improve bridge health. Results from the research will be shared with engineers, owners, and builders at workshops hosted by the research team. Project findings will be disseminated through publications, conferences, meetings, and forums. The research team will engage with the Big Data Hubs and Spokes network to make data, methods, and results available across regions. By extension, project findings will also benefit activities used to monitor and manage other important infrastructure assets, including highways, buildings, power grids, offshore oil platforms, water networks, and other civil infrastructures.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.
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Chapter 2 - Robust Output Only Health Monitoring of Steel Railway Bridges: Analysis of Applicability of Different Sensors
第 2 章 - 钢制铁路桥梁的仅鲁棒输出健康监测:不同传感器的适用性分析
DOI:
10.4018/978-1-7998-2772-6
发表时间:
2020
期刊:
Handbook of Research on Engineering Innovations and Technology Management in Organizations
影响因子:
--
作者:
[Rageh, A, Lopez, S., Linzell, D., Eftekhar Azam, S.]
通讯作者:
Eftekhar Azam, S.
Identifying Predictors of Bridge Deterioration in the United States from a Data Science Perspective
从数据科学的角度识别美国桥梁恶化的预测因素
DOI:
--
发表时间:
2019
期刊:
ProQuest Dissertation and Theses
影响因子:
--
作者:
[Kale, Akshay]
通讯作者:
Kale, Akshay
DOI:
10.1002/stc.2288
发表时间:
2019-02-01
期刊:
STRUCTURAL CONTROL & HEALTH MONITORING
影响因子:
5.4
作者:
[Azam, Saeed Eftekhar, Rageh, Ahmed, Linzell, Daniel]
通讯作者:
Linzell, Daniel
A Proposal for Research on the Application of AI/ML in ITPM: Intelligent Project Management
AI/ML在ITPM中的应用研究建议:智能项目管理
DOI:
10.4018/ijitpm.315290
发表时间:
2023
期刊:
International Journal of Information Technology Project Management
影响因子:
0.8
作者:
[Mishra, Anoop, Tripathi, Abhishek, Khazanchi, Deepak]
通讯作者:
Khazanchi, Deepak
Steel railway bridge fatigue damage detection using numerical models and machine learning: Mitigating influence of modeling uncertainty
使用数值模型和机器学习进行钢制铁路桥梁疲劳损伤检测:减轻建模不确定性的影响
DOI:
10.1016/j.ijfatigue.2019.105458
发表时间:
2020
期刊:
International Journal of Fatigue
影响因子:
6
作者:
[Rageh, Ahmed, Eftekhar Azam, Saeed, Linzell, Daniel G.]
通讯作者:
Linzell, Daniel G.
共 15 条
BD Spokes: PLANNING: MIDWEST: Big Data Innovations for Bridge Health
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批准号:1636805
-
项目类别:Standard Grant
-
资助金额:$10.0万
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财政年份:2016
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负责人:Robin Gandhi
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依托单位:
海外基金