Wind load estimation and virtual sensing in long-span suspension bridges using physics-informed Gaussian process latent force models

• Published in: Mechanical systems and signal processing Vol. 170; p. 108742
• Main Authors: Petersen, Ø.W., Øiseth, O., Lourens, E.
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.05.2022; Elsevier BV
• Subjects: Accelerometers; Aerodynamic coefficients; Algorithms; Bridge loads; Force identification; Gaussian process; Inverse estimation; Kalman filters; Latent force model; Reduced order models; Response prediction; Structural health monitoring; Structural monitoring; Structural response; Suspension bridge; Suspension bridges; Vibration monitoring; Virtual sensing; Wind engineering; Wind loads; Wind tunnel testing; Wind tunnels; Wind engineering; Gaussian process; Structural monitoring; Virtual sensing; Force identification; Suspension bridge; Response prediction; Latent force model
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2021.108742

Wind loading is an essential aspect in the design and assessment of long-span bridges, but it is often not well-known and cannot be measured directly. Most structural health monitoring systems can easily measure structural responses at discrete locations using accelerometers. This data can be combined with reduced-order modal models in Kalman filter-based algorithms for an inverse estimation of wind loads and system states. As a further development, this work investigates the incorporation of Gaussian process latent force models (GP-LFMs), which can characterize the evolution of the wind loading. The Hardanger Bridge, a 1310 m long suspension bridge instrumented with a monitoring system for wind and vibrations, is used as a case study. It is shown how the LFMs can be enriched with physical information about the stochastic wind loads using monitoring anemometer data and aerodynamic coefficients from wind tunnel tests. It is found that the estimates of the modal wind loads and modal states obtained from a Kalman filter and Rauch–Tung–Striebel smoother are stable for acceleration output only, thus avoiding the accumulation of errors. The proposed approach demonstrates how physical or environmental data can be injected as valuable information for global monitoring strategies and virtual sensing in bridges. •Load estimation is improved by fusing latent force models with state-space models.•Latent force models can be enriched with physical information on wind loads.•Modal wind loads and responses for a long-span suspension bridge are estimated.•The method utilizes both recorded structural accelerations and wind velocity data.