| NSF Org: |
OISE Office of International Science and Engineering |
| Recipient: |
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| Initial Amendment Date: | June 5, 2015 |
| Latest Amendment Date: | June 5, 2015 |
| Award Number: | 1515459 |
| Award Instrument: | Fellowship Award |
| Program Manager: |
Anne Emig
OISE Office of International Science and Engineering O/D Office Of The Director |
| Start Date: | June 1, 2015 |
| End Date: | May 31, 2016 (Estimated) |
| Total Intended Award Amount: | $5,070.00 |
| Total Awarded Amount to Date: | $5,070.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
Bethlehem PA US 18015-2955 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Kyoto JA |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | EAPSI |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.079 |
ABSTRACT
Structural engineering researchers and practitioners invest an immense amount of time and money into reliable data acquisition systems with the ambition of obtaining up-to-date information about the true behavior of existing infrastructure. However, even state-of-the-art technologies are susceptible to missing packets, erroneous values, and other malfunctions. The finite reliability of sensors and data acquisition systems disrupts data-driven methods designed to extract important structural information. Furthermore, the likelihood of sensing failures and corresponding incomplete datasets increases during extreme weather events. This award supports research using large-scale experiments to discover the true impact of sensor network reliability on the estimation of important structural features of structural health. The work will be conducted under the mentorship of Professors Masayoshi Nakashima and Masahiro Kurata at the world-renown Disaster Prevention Research Institute at Kyoto University, Japan.
The main hypothesis of this project is despite their missing content, there is a substantial amount of crucial features available within incomplete datasets. Without suitable processing methods, this data is often considered damaged beyond repair then partially or fully discarded, leaving important structural information unknown. Structural responses of a five-story steel frame will be measured using a data acquisition system with a low reliability. New techniques that accurately estimate structural modal properties using incomplete datasets will be proposed. Central analytical goals of this project will focus on location-based knowledge in these cases, e.g. the quantification of total spatial information in terms of total network reliability and individual sensor reliability. Consideration of this data class in large-scale structural tests is essential for the development and validation of new computational tools that extract maximal information using measured responses from existing infrastructure. The structural health monitoring methods that support incomplete datasets offer expedited post-event assessments of structural integrity, permitting prompt notification to engineers, local governments, and society. This NSF EAPSI award is funded in collaboration with the Japan Society for the Promotion of Science.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Overview
In this project, the impact of missing data on the accuracy of structural modal property estimates for a steel building was investigated. Multiple large-scale vibration tests were conducted to consider different structural loading conditions and to produce a variety of representative sensor data. A wide range of sensor failure possibilities was simulated and the corresponding information extracted from this data was compared with the perfect data case (no sensor failures).
Immediate Technical Findings
- Structural modal properties were estimated accurately despite significant sensor malfunctions (data losses).
- Mode shape estimates were significantly more sensitive to missing data (sensor failures) than natural frequencies.
- It was demonstrated that structural modal properties could be accurately computed even when all the sensors at a particular floor failed after collecting only 8% of the data (92% missing at each sensor).
- These results indicate that there is important information embedded within these seemingly corrupted data sets, thus it is prudent to adopt analysis techniques that accept incomplete data sets, rather than choosing to discard these types of data.
Broader Impacts
The findings of this study further exemplify that less data does not necessarily mean less information. In the digital age, it is prudent to better quantify the relationship between data and information, not only in structural engineering, but for digital measurements more generally.The results from this project have quantified the value of data provided by (i) cheaper sensing devices and simpler sensor networks and (ii) data acquisition systems that malfunctioned during a rare event such as a natural disaster. Thus, data that were previously regarded as inadequate in terms of quality may now be considered informative and useful.The data analysis techniques implemented, those that support incomplete data sets, offer expedited post-disaster assessments of structural integrity. As a result notification to engineers, local governments, and society may be improved. With the ability to rapidly report structural health in face of technological challenges, emergency response strategies could achieve high effectiveness.
Last Modified: 03/15/2016
Modified by: Thomas J Matarazzo
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