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The American Society of Civil Engineers (ASCE) Report Card for America’s Infrastructure gave bridges a C+ (mediocre) grade in 2017. Approximately, 1 in 5 rural bridges are in critical condition, which presents serious challenges to public safety and economic growth. Fortunately, during a series of workshops on this topic organized by the authors, it has become clear that Big Data could provide a timely solution to these critical problems. In this work in progress paper, we describe a conceptual framework for developing SMart big data pipelines for Aging Rural bridge Transportation Infrastructure (SMARTI). Our framework and associated research questions are organized around four ingredients: • Next-Generation Health Monitoring: Sensors; Unmanned Aerial Vehicle/System (UAV/UAS); wireless networks • Data Management: Data security and quality; intellectual property; standards and shared best practices; curation • Decision Support Systems: Analysis and modeling; data analytics; decision making; visualization, • Socio-Technological Impact: Policy; societal, economic and environmental impact; disaster and crisis management.
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Rethinking Resilience Analytics
Abstract The concept of “resilience analytics” has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data‐driven analytic techniques, as well as the prevalence of high‐profile natural and man‐made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber–physical–social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamental surprise.
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- Award ID(s):
- 1760739
- PAR ID:
- 10102174
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Risk Analysis
- Volume:
- 39
- Issue:
- 9
- ISSN:
- 0272-4332
- Page Range / eLocation ID:
- p. 1870-1884
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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