| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | June 24, 2014 |
| Latest Amendment Date: | June 24, 2014 |
| Award Number: | 1435923 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Yick Hsuan
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | September 1, 2014 |
| End Date: | August 31, 2017 (Estimated) |
| Total Intended Award Amount: | $281,664.00 |
| Total Awarded Amount to Date: | $281,664.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
633 CLARK ST EVANSTON IL US 60208-0001 (312)503-7955 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
2145 Sheridan Rd Evanston IL US 60208-3109 |
| 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
The recent American Society of Civil Engineers report on the assessment of infrastructure condition resulted in an overall grade of D+, which is rather unsatisfactory. Long-term aging and deterioration of structures pose a significant threat to structural resiliency in the event of natural and man-made hazards. Reinforced concrete structures are difficult to model analytically because of interaction between concrete and reinforcing bars. Hence, the overarching goal of this project is to improve the capability of the engineering community to analyze reinforced concrete structures computationally by establishing a large-scale physics-based computational framework for the simulation of infrastructure. With accurate computational simulation models it would be possible to simulate deteriorating agents such as corrosion, cracks, chemical attack on concrete etc. This research will advance the understanding of failure mechanisms of aged concrete structures. The rigorous mathematical and computational developments involved in the research will provide an ideal training platform for graduate students and will bring opportunities for educational advancement in the civil engineering undergraduate curriculum.
Accurate analytical modeling of failure behavior of quasi-brittle materials and structures in presence of long-term deterioration is a complicated problem that is still unsolved. Reliable simulations require accurate descriptions of various meso-scale phenomena, which are not taken into account by available phenomenological computational models. Meso-scale (the scale of coarse aggregate particles in concrete) models capture these meso-scale features of failure but they are extremely intensive from a computational point of view. Based on a recently formulated discrete model called the Lattice Discrete Particle Model (LDPM), effective multi-scale techniques suitable for up-scaling discrete systems will be developed in this research. The main contribution of this project to the knowledge base will be the formulation and validation of a multi-scale framework for the simulation of reinforced concrete (1) accounting for the most common long-term deterioration mechanisms, e.g., freeze-thaw cycles, alkali-silica reaction, shrinkage and creep; (2) including a bond algorithm to couple the concrete model with steel reinforcing bars and their expansive effect associated with steel corrosion; and, finally, (3) featuring multiple scale algorithms for the simulation of reinforced concrete frames.
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.
The design of civil engineering structures relies to a large extent on established and standardized procedures that are mandated by design codes and specifications. This approach, while relatively simple to follow, limits the complexity of the structural forms that can be built and hamper the ability of designers to innovate and to adopt newly available technologies in the design process. In contrast, more modern performance-based approaches require to satisfy certain performance criteria and designers have the freedom of utilizing whatever technological solution they deem appropriate to achieve such performance. However, designers also have the burden of demonstrating that the selected solution indeed satisfies the targeted performance criteria. In many situations, this calls for the use of accurate and reliable computational tools and computer programs able to simulate structural failure. Such tools are scarcely available at the moment.
The research outcomes of this project consist of a suite of models for the simulation of failure of concrete and reinforced concrete. These models are based on the fundamental assumption that failure is intrinsically related to the behavior of concrete at the level of its constituents. Consistently, the fundamental model adopted in this project was the so-called Lattice Discrete Particle Model (LDPM) that simulates the interaction between concrete aggregate and the embedding mortar. In this project, LDPM was adopted successfully to obtain multiscale models allowing the simulation of a large variety of material and structural systems, from plain concrete samples to reinforced concrete frames, with unprecedented accuracy. The response of the models was validated with a rigorous comparison with experimental data available in the literature. Finally, in order to maximize the impact of the research to practice, the developed computational models were implemented in commercial structural analysis software and they are already available to practitioners.
Last Modified: 12/15/2017
Modified by: Gianluca Cusatis
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