OpportunityTitle: Innovative Technologies in Structural Health Monitoring for Condition Assessment and Future Reliability Prediction
AdditionalInformationOnEligibility: This opportunity is restricted to the non-federal partners of the Californian Cooperative Ecosystems Studies Unit (CESU)
Description: Brief Description of Anticipated Work:The purpose of this research is to advance structural health monitoring (SHM) technologies, including innovative sensors, sensing modalities, telemetry, data analysis, feature extraction, numerical models, and statistical models, for their readiness for use by infrastructure portfolio managers to assess current future condition and reliability of infrastructure components and systems. The ultimate form in which these technologies will be utilized is a multi-scale digital surrogate model of specific infrastructure assets as identified by the government. All efforts toward the research objectives should be aligned with the basic tenants of ISO 55000 standards for asset management. Specifically, the objectives include:Objective 1. Implementation/deployment of digital twins Develop and deploy multi-scale digital surrogate models/digital twins of large civil infrastructure. The digital twin shall be deployed for specific water-resource asset(s) as identified by the government as part of this cooperative agreement. The digital twin must include visualization of decision support metrics with consideration of integration of augmented reality (AR) and/or virtual reality (VR) tools for use by the end user.Objective 2. Non-contact sensing Develop new or modify existing technologies to implement non-contact sensing of large scale, civil infrastructure that is otherwise difficult to physically access. Example modalities to consider can include, but are not limited to, computer vision, LiDAR, sonar, and ultrasonic. Sensing targets may be to supplement traditional contact sensing, and aim to acquire displacement, acceleration, strain, etc. Other targets of interest may be obtaining information specific to the visible spectrum, such as the presence of corrosion, spalling, scour, etc. Technologies shall be investigated for use above and below water with varying levels of turbidity, in environments and locations with limited lighting, etc. Methods developed shall be implemented on water-resources infrastructure identified by the government and shall be developed in a manner to be implemented directly into the digital twin outlined in objective 1.Objective 3: Novel sensing and data acquisitionImplement existing or develop innovative sensors, sensing systems, data acquisitions, or sensing techniques for the efficient, accurate, and economical collection of data to support multi-scale digital surrogate models of large civil infrastructure systems. The developed methodologies shall address problems and specific infrastructure detection targets as identified by the government and shall be implemented/integrated into the digital twins outlined in objective 1.Objective 4. Robotic/unmanned inspectionModify existing technologies to be implemented in the remote/unmanned inspection of large-scale infrastructure. This should be leveraged as a platform to implement/deliver the methods developed in objectives 2 and 3, as well as other applicable methods found in the literature, and shall be in direct support of the development of the digital twins as outlined in objective 1. Acceptable target infrastructure shall be water resources infrastructure as identified by the government. Consideration shall be given to the integration of AR/VR technologies into the inspection platforms to allow for remote, real-time visualization and inspection of the targeted infrastructure.Objective 5. Machine Learning/Artificial intelligence for data analytics and decision supportDevelop new or modify existing data analytics methodologies for decision support of the infrastructure specified by the government. Develop new or modify existing ML/AI methods to facilitate the processing data gathered from methodologies outlined in objectives 2, 3 and 4 (and elsewhere) to support decision-making and integration into the digital twin framework outlined in objective 1. Develop new or modify existing ML/AI methods for control of robotic inspection platforms outlined in objective 4. Other methods investigated may include, but are not limited to, Bayesian risk and decision making for specific water resources infrastructure as identified by the government and model updating based on information gleaned from many sensing modalities.Successful applicants should have expert knowledge in the field of structural health monitoring and a record that demonstrates experience with researching and applying SHM principles to assets in an operational environment. The candidates shall be able to demonstrate a record of prior experience with use of statistical pattern recognition for detecting and assessing damage of structural systems. The candidates shall be able to demonstrate a record of experience with designing SHM systems to maximize the reduction of risk per dollar cost.Successful applicants should have expert knowledge and work experience in the field of structural health monitoring, especially with application to large complex structures. The objectives described herein will also require expertise with a variety of sensor types and their development, structural analysis, statistical modeling, machine learning and pattern recognition, system engineering, 3D physics-based multi-scale models, the inverse problem for models and model updating, and surrogate modeling or digital twins. The vendor shall have a record of collaborative research with multiple organizations and must be amenable to joint publications and presentations when the level of contribution of partners is warranted, and such contribution should be actively encouraged by the vendor. The vendor will encourage an as-yet-undetermined number of students to travel to ERDC facilities during specific periods when school is not in session to conduct research toward the objectives of this agreement alongside ERDC researchers who will provide oversight and facility resources during those times. The student travel and living expenses will be paid for by the vendor from the funds provided in this agreement and shall be in line with travel limitations and reimbursements as described by the Department of Defense Joint Travel Regulations current at the time of travel or comparable vendor travel standards. The candidates will also be required to submit three (3) quarterly status reports and one (1) annual report each year of the cooperative agreement to provide updates on the implementation of the project.
GrantorContactEmailDescription: Derek Howard
GrantorContactText: Derek Howard &lt;br/&gt;Contract Specialist &lt;br/&gt;Phone 601-634-3310