Abstract
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.
Highlights
Live loadings on large structures such as bridges, tall buildings, and wind turbines are not always well-known due to their stochastic nature, limited information concerning site-specific conditions, and other uncertainties in the existing load models
This work investigates the incorporation of Gaussian process latent force models (GP-LFMs), which can characterize the evolution of the wind loading
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, avoiding the accumulation of errors
Summary
Live loadings on large structures such as bridges, tall buildings, and wind turbines are not always well-known due to their stochastic nature, limited information concerning site-specific conditions, and other uncertainties in the existing load models. Reducing these uncertainties is an important objective in structural health monitoring (SHM)-based infrastructure management [1], as the loads are important inputs in calculations for fatigue, extreme values, and serviceability criteria. In the field of wind engineering, wind load models used in bridge design are mainly based on theoretical formulas, environmental data, wind tunnel tests, and computational simulations These data sources do not always account for the environmental complexities leading to the actual load conditions on a specific bridge.
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