| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | May 9, 2023 |
| Latest Amendment Date: | May 9, 2023 |
| Award Number: | 2304544 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Parvathi Chundi
pchundi@nsf.gov (703)292-5198 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | May 15, 2023 |
| End Date: | May 31, 2024 (Estimated) |
| Total Intended Award Amount: | $275,000.00 |
| Total Awarded Amount to Date: | $275,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
31553 SNOWSHOE RD EVERGREEN CO US 80439-7651 (303)808-1729 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1500 Illinois St Golden CO US 80401-1887 |
| 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): | STTR Phase I |
| 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): | 47.084 |
ABSTRACT
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to reduce the cost of underground civil infrastructure construction and make new infrastructure more sustainable by developing a computational software-based tool to create and update ground models in real time using data-driven advanced analytics. Civil infrastructure is increasingly moving underground, including roadways, transit, utilities, and facilities. However, risk in underground construction remains a critical barrier to attracting investment. A significant risk in building underground is the high uncertainty in ground conditions and physical properties influencing design and construction, resulting in increased costs due to over-design and/or delays and failures during construction. This project strives to improve the understanding of ground conditions by providing a solution to update the ground models during construction in a routine and autonomous manner, making full use of the wealth of data collected during construction.
This Small Business Technology Transfer (STTR) Phase I project aims to develop a technical solution that automates the process of back-analyzing ground properties and updating ground models in real time. Several technical challenges will be addressed. Current backanalyses practice is extraordinarily labor-intensive and expensive in managing and integrating data from underground infrastructure projects. The dynamic environment during construction requires 4D inversion analysis on potentially hundreds of unique tunnel-structure interactions. Furthermore, the efficacy of the inversion in estimating geotechnical parameters, which has been demonstrated for only limited situations during the fundamental development of the techniques, needs to be validated. The goals of the proposed research are to (1) develop algorithms to automatically integrate data from geotechnical instrumentation and monitoring, construction process monitoring, existing infrastructure, apriori geostatistical model, etc., (2) develop algorithms to dynamically update the geotechnical parameter inversion in spatial proximity of tunnel construction to adjacent structures, sensors, and ground conditions, (3) characterize the geotechnical parameter inversion efficacy across a broad variety of ground conditions and tunneling-structure interactions, and (4) learn the influence of sensing layout and optimized sensing on inversion efficacy. This Phase I work will lay the foundation for the development of a ?live? ground modelling tool.
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.
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.
Emprise Concepts, LLC, in collaboration with the Colorado School of Mines, was awarded an STTR Phase 1 grant to pursue the development of an innovative, computational software-based routine for characterizing the subsurface and reducing underground construction risk. This tool aims to enable automatic integration and back analysis of data from multiple sources collected during construction to develop/update the geologic and geotechnical ground models in real-time. This innovation strives to improve understanding of the ground conditions by quantifying and reducing uncertainties related to subsurface characterization and interpretation, making new underground infrastructure more sustainable, economical, and safer to construct.
In Phase 1, the following key accomplishments outlined provide a solid case to continue the research and development of our computational software-based routine for characterizing the subsurface and reducing underground construction risk:
1. Successfully developed algorithms to automatically integrate data collected on heavy civil and tunnel construction projects including ground investigation, geotechnical instrumentation and monitoring, construction machines such as tunnel boring machines (TBM), and existing subsurface models.
2. Proven the efficacy of geotechnical parameter inversion with 10 data sets consisting of a variety of data types, geology settings and TBM geometries.
3. Developed algorithm and proven the feasibility of dynamic, automated, real-time prediction updating with a reduced manual labor and computation time of up to 90%. Predictions include settlement, geologic ground model and geotechnical material parameters.
4. Demonstrated the added value of updating predictions with accuracy being significantly improved after the geotechnical parameter inversion and ground model updating.
5. Demonstrated the tool’s applicability to other aspects of tunnel construction in addition to ground deformation, including tool wear and progress schedule, with three data sets.
The broader societal and economic benefits of this innovation through commercialization of this tool includes reducing the cost of civil infrastructure construction (through risk and time reduction) and helping make new infrastructure more sustainable. Improving the understanding of the subsurface characteristics by applying advanced analytics to the wealth of data collected during construction will enable more economical and sustainable design and construction methods. Furthermore, the tool will reduce risks associated with increased costs due to delays and failures during construction.
Last Modified: 06/03/2024
Modified by: Jacob G Grasmick
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