| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | July 31, 2016 |
| Latest Amendment Date: | July 31, 2016 |
| Award Number: | 1634483 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Caglar Oskay
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | August 1, 2016 |
| End Date: | July 31, 2020 (Estimated) |
| Total Intended Award Amount: | $455,747.00 |
| Total Awarded Amount to Date: | $455,747.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 (404)894-4819 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
225 North avenue Atlanta GA US 30332-0420 |
| 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): | Structural and Architectural E |
| 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): | 47.041 |
ABSTRACT
Identifying material properties of existing structural systems is crucial for monitoring their performance and ensuring their safety. Knowledge about these properties allows the determination of the magnitudes and the locations of any deterioration occurring in the system. Such knowledge will allow the proper required actions to avoid any failures. The identification procedures require a significant amount of measured data that typically are associated with uncertainty. Accounting for uncertainty due to different factors is one of the most challenging tasks in structural system identification and damage detection. In this project, uncertainty in measurements due to device tolerances and field conditions will be treated in terms of possible values expressed as intervals, without any prior assumptions on their nature. The rigorous consideration of uncertainty in system identification will lead to a more reliable and dependable predictions of their behavior. Such reliable and dependable predictions are crucial for the safety of civil infrastructure systems, and will contribute to the reduction of life endangering failures and disasters. The research team will offer research opportunities to high school students through summer camps.
While interval-based methods and system identification are both very powerful and well-developed techniques within their respective fields, thus far they have scarcely been used together in the context of inverse problems under uncertainty. By investigating the best ways of analytically, numerically and computationally handling these two methodologies in conjunction with each other, we shall develop a powerful class of techniques not only for the problem at hand, but for a vast number of other inverse problems with partial differential equation (PDE) constraints. In this research an emphasis will be placed on the use of Interval Finite Element Methods (IFEM) in the context of optimization to solve for inverse problems that arise in structural system identification and damage detection under uncertainty without prior assumptions on its nature. This is therefore expected to be the first step of a long lasting research program which will be both broad in its scope of applications across many fields and at the same time remain coherently focused in its underlying computational and mathematical methodologies.
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.
Identifying material properties of existing structural systems is crucial for monitoring their performance and ensuring their safety. The knowledge about these properties allows the determination of the magnitudes and the locations of any occurring deterioration in the system. Such knowledge will allow the proper required actions to avoid any failures. The identification procedures require a significant amount of measured data that typically are associated with uncertainty. Accounting for uncertainty due to different factors is one of the most challenging tasks in structural system identification and damage detection. In this project, uncertainty in measurements due to device tolerances and field conditions has been treated in terms of possible values expressed as intervals, without any prior assumptions on their nature. The rigorous consideration of uncertainty in system identification will lead to a more reliable and dependable predictions of their behavior. Such reliable and dependable predictions are crucial for the safety of civil infrastructure systems, and will contribute to the reduction of life endangering failures and disasters.
During the period of the project the conducted research has investigated, validated, and optimized an innovative class of interval-based computational algorithms for structural parameter identification and damage evaluation under uncertainty. We have combined mathematical and computational methodologies that have matured largely in isolation from each other within different engineering disciplines. Interval Finite Element Methods (IFEM) have been employed in the context of new interval optimization to solve for inverse problems that arise in structural system identification and damage detection under uncertainty.
We have developed fundamental theoretical solutions for the problem under consideration in addition to a new set of computational "interval-tools" that allowed us to pass directly from uncertain raw measurements to sharp and guaranteed bounds estimates of the unknown parameters leading to the prediction of local stiffness and enabling damage identification. Uncertainties in measurement due to device tolerances and field conditions have been treated in terms of possible values expressed as intervals, without any prior assumptions on their nature.
Last Modified: 11/30/2020
Modified by: Rafi L Muhanna
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