Innovative Technologies in Structural Health Monitoring for Condition Assessment and Future Reliability Prediction | W81EWF 22 SOI 0010

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.

• 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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