Opportunity Information: Apply for W81EWF 22 SOI 0010
The Department of Defense Engineer Research and Development Center (ERDC) is seeking one cooperative agreement project to push structural health monitoring (SHM) beyond lab demonstrations and into tools that infrastructure portfolio managers can actually use to understand current condition and forecast future reliability. The central idea is to combine advanced sensing, data collection, modeling, and analytics into a practical, multi-scale "digital surrogate" or digital twin of real government-identified water-resources assets. The work is expected to follow the core principles of ISO 55000 asset management, meaning the research should not just produce technical results, but should translate into decision-ready information that supports risk-based, cost-effective maintenance and investment planning across an asset lifecycle.
A major objective is the development and deployment of multi-scale digital twins for large civil infrastructure, specifically tied to water-resources infrastructure selected by the government. These digital twins are expected to integrate physics-based and data-driven models and present outputs in a way that helps end users make decisions, including visualizations of decision-support metrics. The opportunity explicitly encourages considering augmented reality and virtual reality features so inspectors and decision-makers can interact with the twin, potentially seeing condition indicators and predicted performance in an intuitive, spatial context rather than only in traditional dashboards or reports.
Another core focus is non-contact sensing for large structures that are difficult, dangerous, or expensive to access. The solicitation highlights modalities like computer vision, LiDAR, sonar, and ultrasonic methods, with the goal of measuring structural responses such as displacement, acceleration, and strain, while also identifying visible or surface condition issues such as corrosion, spalling, and scour. Because the target assets are water-resources structures, the sensing solutions must be viable in challenging environments, including above-water and below-water inspections, variable turbidity, limited lighting, and other real-world constraints. A key requirement is that whatever sensing approach is developed must feed directly into the digital twin framework so that sensing is not treated as a standalone experiment but as part of an integrated condition assessment and prediction system.
Complementing non-contact methods, the opportunity calls for novel sensing and data acquisition approaches that improve the efficiency, accuracy, and cost of collecting data needed for multi-scale digital surrogate models. This can include new sensor designs, improved sensing systems, or better data acquisition techniques that enable robust long-term monitoring. The government will identify specific detection targets and infrastructure problems of interest, and applicants are expected to tailor sensing and acquisition solutions to those operational needs, again with the expectation that outputs will be integrated into the digital twin.
The solicitation also places heavy emphasis on robotic and unmanned inspection as a practical delivery mechanism for the sensing and data collection methods. Applicants are expected to adapt existing remote or unmanned inspection technologies to operate on large-scale water-resources infrastructure, using these platforms to deploy the non-contact and novel sensing methods described above. The program also encourages integrating AR/VR capabilities into inspection platforms to support remote, real-time visualization and enable experts to inspect or guide inspections without being physically on site, which is especially relevant for hard-to-access or hazardous environments.
On the analytics side, the opportunity seeks machine learning and artificial intelligence methods that turn multi-modal sensing data into actionable decision support. This includes processing and fusing data from different sensors and inspection platforms, extracting damage-sensitive features, updating models (including solving inverse problems and model updating), and producing reliability or risk predictions that can inform maintenance actions. The solicitation explicitly mentions Bayesian risk and decision-making approaches as relevant, and also asks for ML/AI methods that can help control robotic inspection platforms, indicating interest in autonomy or semi-autonomous operation that improves inspection effectiveness and consistency.
In terms of who should apply, the government is looking for teams with deep expertise in SHM for large, complex structures, with demonstrated experience applying SHM in operational environments rather than only in academic or small-scale settings. Strong qualifications include prior work in statistical pattern recognition for damage detection and experience designing SHM systems that maximize risk reduction per dollar spent, reflecting a practical, asset-management-driven mindset. Because the effort spans sensing, modeling, and decision support, applicants are also expected to bring capabilities in sensor development, structural analysis, statistical modeling, machine learning, systems engineering, 3D physics-based multi-scale modeling, surrogate modeling/digital twins, and model updating.
Collaboration expectations are built into the program. The awardee is expected to work well with multiple organizations, be open to joint publications and presentations when warranted, and support student involvement through travel to ERDC facilities during academic breaks to conduct research alongside ERDC personnel. The awardee must cover student travel and living costs from the award funds in alignment with Department of Defense Joint Travel Regulations (or comparable organizational travel standards). Reporting requirements include three quarterly status reports and one annual report each year for the duration of the cooperative agreement, ensuring routine progress updates and accountability.
Key administrative details from the source listing include the opportunity title "Innovative Technologies in Structural Health Monitoring for Condition Assessment and Future Reliability Prediction," Funding Opportunity Number W81EWF 22 SOI 0010, and CFDA 12.630. The instrument type is a cooperative agreement under a science and technology/research and development category. The anticipated award ceiling is $2,500,000 with one expected award. The posting date was March 10, 2022, with an original closing date of May 11, 2022.Apply for W81EWF 22 SOI 0010
- The Department of Defense, Engineer Research and Development Center in the science and technology and other research and development sector is offering a public funding opportunity titled "Innovative Technologies in Structural Health Monitoring for Condition Assessment and Future Reliability Prediction" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 12.630.
- This funding opportunity was created on Mar 10, 2022.
- Applicants must submit their applications by May 11, 2022. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $2,500,000.00 in funding.
- The number of recipients for this funding is limited to 1 candidate(s).
- Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).
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Frequently Asked Questions (FAQs)
What is the title of this funding opportunity?
The opportunity is titled "Innovative Technologies in Structural Health Monitoring for Condition Assessment and Future Reliability Prediction."
Who is offering this opportunity?
The opportunity is offered by the Department of Defense Engineer Research and Development Center (ERDC).
What type of award is expected?
The instrument type is a cooperative agreement in the science and technology / research and development category.
How many awards does ERDC expect to make?
ERDC anticipates making one award under this opportunity.
What is the maximum funding amount (award ceiling)?
The anticipated award ceiling is $2,500,000.
What is the Funding Opportunity Number (FON)?
The Funding Opportunity Number is W81EWF 22 SOI 0010.
What is the CFDA number listed for this opportunity?
The CFDA number is 12.630.
What problem is this program trying to solve?
The program seeks to move structural health monitoring (SHM) beyond lab demonstrations and into practical tools that infrastructure portfolio managers can use to understand current condition and forecast future reliability of real assets.
What is the main technical concept being pursued?
The central idea is to combine advanced sensing, data collection, modeling, and analytics into a practical, multi-scale "digital surrogate" (digital twin) of government-identified water-resources assets.
What kinds of infrastructure are in scope?
The focus is on large civil infrastructure tied to water-resources assets that will be selected/identified by the government.
What is meant by a "multi-scale digital twin" in this context?
It refers to a digital surrogate model that spans multiple scales (for example, component-to-system behavior) and is capable of integrating sensing data, physics-based modeling, and data-driven analytics to support condition assessment and reliability prediction.
How should the work align with asset management practices?
The solicitation expects the work to follow the core principles of ISO 55000 asset management, meaning the research should translate into decision-ready information that supports risk-based, cost-effective maintenance and investment planning across the asset lifecycle.
What kinds of modeling approaches are expected for the digital twin?
The digital twins are expected to integrate physics-based models and data-driven models, with analytics that can update models and support prediction (including inverse problems and model updating).
What kinds of outputs should the digital twin provide to end users?
Outputs should be presented in ways that help end users make decisions, including visualizations of decision-support metrics related to condition, reliability, and risk.
Are augmented reality (AR) or virtual reality (VR) features required?
The opportunity explicitly encourages considering AR/VR features so inspectors and decision-makers can interact with the digital twin in an intuitive spatial context (for example, seeing condition indicators and predicted performance), rather than relying only on traditional dashboards or reports.
Why does the solicitation emphasize non-contact sensing?
Because many large structures are difficult, dangerous, or expensive to access, the program emphasizes non-contact sensing methods that can measure structural response and detect surface/visible condition issues without requiring direct physical contact.
What non-contact sensing modalities are highlighted?
The solicitation highlights computer vision, LiDAR, sonar, and ultrasonic methods.
What types of structural responses should sensing methods be able to measure?
The sensing approaches are aimed at measuring structural responses such as displacement, acceleration, and strain.
What types of condition or damage indicators are of interest?
The solicitation mentions visible or surface condition issues such as corrosion, spalling, and scour.
What environmental or operational constraints must sensing solutions handle?
Because the assets are water-resources structures, sensing solutions must be viable in challenging real-world environments, including above-water and below-water inspections, variable turbidity, limited lighting, and other field constraints.
Does the program allow sensing research as a standalone effort?
No. A key requirement is that sensing approaches must feed directly into the digital twin framework so sensing is part of an integrated condition assessment and prediction system, not a standalone experiment.
What is meant by "novel sensing and data acquisition approaches"?
This includes new sensor designs, improved sensing systems, or better data acquisition techniques that improve the efficiency, accuracy, and cost of collecting the data needed for multi-scale digital surrogate models and robust long-term monitoring.
Who defines the specific detection targets and infrastructure problems?
The government will identify specific detection targets and infrastructure problems of interest, and applicants are expected to tailor sensing and data acquisition solutions to those operational needs.
How important are robotics and unmanned systems in this program?
They are a major emphasis. The solicitation highlights robotic and unmanned inspection as a practical delivery mechanism for deploying non-contact and novel sensing methods on large-scale water-resources infrastructure.
What is expected regarding remote or unmanned inspection technologies?
Applicants are expected to adapt existing remote or unmanned inspection technologies to operate on large-scale water-resources infrastructure and use these platforms to deploy the sensing methods described in the solicitation.
How does AR/VR relate to robotic inspection in this program?
The program encourages integrating AR/VR capabilities into inspection platforms to support remote, real-time visualization and enable experts to inspect or guide inspections without being physically on site, which is particularly relevant for hazardous or hard-to-access environments.
What analytics capabilities does ERDC want to see?
The solicitation seeks machine learning (ML) and artificial intelligence (AI) methods that convert multi-modal sensing data into actionable decision support, including data processing and fusion, feature extraction, model updating, and reliability/risk prediction.
What does "multi-modal data fusion" imply for this effort?
It implies combining and analyzing data from different sensors and inspection platforms (for example, vision, LiDAR, sonar, ultrasonic) to produce a more complete and decision-relevant picture of condition and expected performance.
Are Bayesian methods specifically relevant?
Yes. The solicitation explicitly mentions Bayesian risk and decision-making approaches as relevant.
Does the solicitation mention autonomy for robotic inspection platforms?
Yes. It asks for ML/AI methods that can help control robotic inspection platforms, indicating interest in autonomy or semi-autonomous operation that improves inspection effectiveness and consistency.
What kind of team is ERDC looking for?
ERDC is looking for teams with deep expertise in SHM for large, complex structures and demonstrated experience applying SHM in operational environments (not only academic or small-scale settings).
What qualifications or experience are highlighted as strong fits?
Highlighted strengths include prior work in statistical pattern recognition for damage detection and experience designing SHM systems that maximize risk reduction per dollar spent, aligning with a practical, asset-management-driven mindset.
What technical capabilities are expected from applicants?
Applicants are expected to bring capabilities spanning sensor development, structural analysis, statistical modeling, machine learning, systems engineering, 3D physics-based multi-scale modeling, surrogate modeling/digital twins, and model updating.
What collaboration expectations are included?
The awardee is expected to work well with multiple organizations, be open to joint publications and presentations when warranted, and support student involvement through travel to ERDC facilities during academic breaks to conduct research alongside ERDC personnel.
Are student travel costs allowable under the award?
The awardee must cover student travel and living costs from award funds, in alignment with Department of Defense Joint Travel Regulations (or comparable organizational travel standards).
What are the reporting requirements?
Reporting requirements include three quarterly status reports and one annual report each year for the duration of the cooperative agreement.
When was the opportunity posted and when did it close?
The posting date was March 10, 2022. The original closing date was May 11, 2022.
Is the focus limited to laboratory demonstrations?
No. A central goal is to deliver practical tools suitable for real asset management and decision-making, pushing SHM beyond lab demonstrations and into operational use for government-identified assets.
What does "decision-ready information" mean in this solicitation?
In this context, it means outputs that directly support risk-based, cost-effective maintenance and investment planning across an asset lifecycle, consistent with ISO 55000 asset management principles.
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