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Buffeting-induced accelerations and displacements of bridge deck girders commonly drive the bridge design’s comfort, operational, and strength limit states. The scattered nature of the main wind characteristics and bridge responses recorded in multiple monitoring campaigns make deterministic approaches insufficient to assess the bridge’s performance along its life span. This study reports comprehensive sensitivity and reliability studies conducted to unveil the influence of multiple parameters controlling long-span bridges’ buffeting responses. The impact of several sets of random variables on the reliability of the Great Belt Bridge is systematically studied. A detailed treatment of the uncertainty of flutter derivatives consisting of combining their frequency-dependent random definition with their experimentally defined correlation is proposed. Results show the drastic impact of uncertainty in the flutter derivatives, the vertical turbulence intensity, the mean wind velocity, and the definition of the buffeting loads, particularly the slopes of the force coefficients and the aerodynamic admittance, on the buffeting-induced accelerations. The influence of aerodynamic admittance on the results is analyzed in the context of random definitions of mean velocity, turbulent intensities, length scales, structural damping, and aerodynamic characteristics. The computational efficiency of gradient-based reliability methods is discussed, showing its potential to address high-dimensional problems within design frameworks. 

Wind-sensitive bridges are commonly designed based on their aeroelastic responses under synoptic winds. However, a holistic aero-structural design framework must address all potential wind scenarios along the bridge life cycle, including non-synoptic events and synoptic winds with relevant variations in the mean angle of attack due to wind-induced static deck deformation or complex terrain effects. This requires the evaluation of the aeroelastic responses considering the sensitivity of the fluid-structure interaction parameters to the wind angle of attack. Aiming at properly modeling these effects within design frameworks, this study proposes harnessing a multi-directional aeroelastic Kriging surrogate trained with forced vibration CFD simulations to emulate the flutter derivatives as a function of the deck shape, reduced velocity, and mean angle of attack. A bridge deck with a variable depth ranging from streamlined to bluff configurations is studied in detail, showing drastic changes in relevant flutter derivatives. The deck shape drives the impact of the mean angle of attack in some critical flutter derivatives, including the occurrence of A2* sign flipping, with its implications in the torsional stability. The resulting aeroelastic surrogate is conceived to be integrated into aero-structural optimization frameworks for optimally shaping bridge decks under synoptic and non-synoptic wind scenarios. 

Contemporary aero-structural design frameworks for wind-sensitive bridges are mostly based on the assessment of aeroelastic responses under synoptic winds. However, holistic design methodologies must address all potential wind scenarios, such as non-synoptic wind events and variations in the angle of attack due to complex terrains. This requires the evaluation of the aeroelastic responses considering the sensitivity of the fluid-structure interaction parameters with the angle of attack. Hence, this study proposes a Kriging-based multi-directional aeroelastic surrogate to emulate the flutter derivatives of bridge decks as a function of the deck shape, frequency of oscillation of the deck, and the mean incident angles of wind. This design tool is pivotal to properly modeling the nonlinear features of flutter derivatives at low reduced velocities and their sensitivity with the angle of attack. The aeroelastic surrogate will be later integrated into aero-structural design frameworks for the shape optimization of bridge decks under non-stationary winds. 

The aero-structural design of bridges is mainly controlled by the deck cross-section design. Design modifications on bridge decks impact the deck aerodynamics and the deck mechanical contribution, which also affect the bridge aeroelastic responses. The nonlinear inherent nature of bluff body aerodynamics combined with the nonlinearities of multimodal aeroelastic analyses result in complex relationships between the full bridge aeroelastic responses and deck shape design variables. This fact impacts the design process as it may lead to the development of complex feasible design regions in the chosen design domain, including disjoint feasible regions that may cause local minima. Given the limitations of metaheuristic optimization methods to deal with optimization problems with large sets of design variables, as required in holistic bridge design problems, gradient-based optimization algorithms can be recast to address global optimization problems. In this study, we propose the use of tunneling optimization methods to address this challenge. 

Abstract The rise of exascale supercomputing has motivated an increase in high‐fidelity computational fluid dynamics (CFD) simulations. The detail in these simulations, often involving shape‐dependent, time‐variant flow domains and low‐speed, complex, turbulent flows, is essential for fueling innovations in fields like wind, civil, automotive, or aerospace engineering. However, the massive amount of data these simulations produce can overwhelm storage systems and negatively affect conventional data management and postprocessing workflows, including iterative procedures such as design space exploration, optimization, and uncertainty quantification. This study proposes a novel sampling method harnessing the signed distance function (SDF) concept: SDF‐biased flow importance sampling (BiFIS) and implicit compression based on implicit neural network representations for transforming large‐size, shape‐dependent flow fields into reduced‐size shape‐agnostic images. Designed to alleviate the above‐mentioned problems, our approach achieves near‐lossless compression ratios of approximately :, reducing the size of a bridge aerodynamics forced‐vibration simulation from roughly to about while maintaining low reproduction errors, in most cases below , which is unachievable with other sampling approaches. Our approach also allows for real‐time analysis and visualization of these massive simulations and does not involve decompression preprocessing steps that yield full simulation data again. Given that image sampling is a fundamental step for any image‐based flow field prediction model, the proposed BiFIS method can significantly improve the accuracy and efficiency of such models, helping any application that relies on precise flow field predictions. The BiFIS code is available onGitHub. 

The shape design and optimization of bluff decks prone to aeroelastic phenomena require emulating the fluid-structure interaction parameters as a function of the body shape and the oscillation frequency. This is particularly relevant for long- and medium-span bridges equipped with single-box decks that are far from being considered streamlined and for other girder typologies such as traditional truss decks and modern twin- and multi-box decks. The success of aero-structural design frameworks, which are inherently iterative, relies on the efficient and accurate numerical evaluation of the wind-induced responses. This study proposes emulating the fluid-structure interaction parameters of bluff decks using surrogate modeling techniques to integrate them into aero-structural optimization frameworks. The surrogate is trained with data extracted from forced-vibration CFD simulations of a typical single-box girder to emulate the values of the flutter derivatives as a function of the deck shape and reduced velocity. The focus is on deck configurations ranging from streamlined to bluff cross-sections and on low reduced velocities to capture eventual aerodynamic nonlinearities. The girder cross-section geometry is tailored based on its buffeting performance. This design tool is fundamental to finding the optimum balance between the structural and aeroelastic requirements that drive the design of bluff deck bridges. 

Controlling wind-induced responses is a challenging and fundamental step in the design of wind-sensitive critical infrastructures (CI). While passive design modifications and passive control devices are effective alternatives to a certain extent, further actions are required to fulfill design specifications under some demanding circumstances. Active countermeasures, such as active dampers, active aerodynamic devices, and operational control systems, stand out as a smart alternative that allows extra control over wind-induced responses of tall buildings, long-span bridges, wind turbines, and solar trackers. To make this possible, CI are equipped with operational technology (OT) and cyber–physical systems (CPS). However, as with any other OT/CPS, these systems can be threatened by cyberattacks. Changing their intended use could result in severe structural damage or even the eventual collapse of the structure. This study analyzes the potential consequences of cyberattacks against wind-sensitive structures equipped with OT/CPS based on case studies reported in the structural control literature. Several cyberattacks, scenarios, and possible defenses, including cyber-secure aero-structural design methods, are discussed. Furthermore, we conceptually introduce and analyze a new cyberattack, the ‘‘Wind-Leveraged False Data Injection’’ (WindFDI), that can be specifically developed by taking advantage of the positive feedback between wind loads and the misuse of active control systems. 

Abstract Aero‐structural shape design and optimization of bridge decks rely on accurately estimating their self‐excited aeroelastic forces within the design domain. The inherent nonlinear features of bluff body aerodynamics and the high cost of wind tunnel tests and computational fluid dynamics (CFD) simulations make their emulation as a function of deck shape and reduced velocity challenging. State‐of‐the‐art methods address deck shape tailoring by interpolating discrete values of integrated flutter derivatives (FDs) in the frequency domain. Nevertheless, more sophisticated strategies can improve surrogate accuracy and potentially reduce the required number of samples. We propose a time domain emulation strategy harnessing temporal fusion transformers (TFTs) to predict the self‐excited forces time series before their integration into FDs. Emulating aeroelastic forces in the time domain permits the inclusion of time‐series amplitudes, frequencies, phases, and other properties in the training process, enabling a more solid learning strategy that is independent of the self‐excited forces modeling order and the inherent loss of information during the identification of FDs. TFTs' long‐ and short‐term context awareness, combined with their interpretability and enhanced ability to deal with static and time‐dependent covariates, make them an ideal choice for predicting unseen aeroelastic forces time series. The proposed TFT‐based metamodel offers a powerful technique for drastically improving the accuracy and versatility of wind‐resistant design optimization frameworks.