CAREER: Enhancing Safety and Sustainability of Floating Civil Structures through Advanced Digital Twinning

About This Grant

This Faculty Early Career Development (CAREER) award supports research focused on advancing the monitoring and management of key infrastructural assets—floating structures in particular—through cutting-edge digital twinning technology. Digital twins, or virtual replicas, integrate real-time data with computational models to provide essential insights into the operations, structural health, and longevity of these assets. The research supported by this award specifically aims to develop digital twin frameworks tailored to overcome the challenges posed by unpredictable environmental forces like wind, waves, and currents. These dynamic forces, often unmeasured, significantly influence the safety and durability of floating structures. This research seeks to pioneer methods to accurately predict these uncertain forces, enhancing the reliability and predictive accuracy of digital twins. This capability is key for ensuring occupant safety in floating homes, extending the operational life of energy infrastructures, and assessing the integrity of floating transportation systems under extreme weather conditions. Beyond its scientific and practical impacts, this award will support workforce development, enhancing engineering education in STEM fields. A comprehensive outreach strategy is planned to engage participants across various educational levels, including K- 12 students, schoolteachers, undergraduate, and graduate students, with the goals of developing a competitive STEM workforce and enhancing the engagement with science and technology in the United States. This research introduces a novel stochastic system identification framework, Bayesian Load-Agnostic Continuous Estimation for Digital Twinning (B-LACE4DT). This framework is designed to address the critical challenge of managing unknown and uncertain loads and forces such as wind, waves, and currents, which significantly impact floating structures. Unlike conventional methods that depend on measurable external loads or assume stationary known statistics, B-LACE4DT utilizes innovative filtering and smoothing algorithms for output-only state and parameter estimation, thus eliminating the dependency on direct input measurements. The approach enables precise, real-time structural diagnosis and prognosis while effectively incorporating the inherent uncertainties of complex dynamic systems. The research targets three types of floating structures, each selected for their unique technical challenges. For the first structure, the framework will improve assessments of fatigue damage and monitor the health of mooring lines by estimating dynamic responses under uncertain hydrodynamic and aerodynamic loads. For the second, the methodology will enhance finite element model updating and virtual sensing, which will advance damage detection and performance evaluation under variable and stochastic load conditions. In the third case, B-LACE4DT will enable precise tracking of displacements, improving safety and comfort for occupants by effectively managing large-amplitude motions induced by marine dynamics. The methodology integrates physics-based models with real-time data assimilation, employing recursive Bayesian estimation techniques, and includes the development of novel derivative-free nonlinear smoothing algorithms. This comprehensive approach ensures a robust framework for the digital twinning of floating structures. To validate this framework, the project will integrate experimental and operational data, including laboratory wave tank tests of scaled prototypes and field measurements from operational infrastructures. Collaboration with industry partners will further enhance the validation process. Through rigorous testing and validation, B-LACE4DT aims to significantly advance the reliability and predictive capabilities of digital twins for large-scale floating structures, thereby setting new standards in structural health monitoring, remaining useful life estimation, and decision-making under uncertainty. This project is jointly funded by the Dynamics, Control and Systems Diagnostics (DCSD) program, and the Established Program to Stimulate Competitive Research (EPSCoR). 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.