Showing papers in "Journal of Civil Structural Health Monitoring in 2023"

Full-scale computational fluid dynamics study on wind condition of the long-span Queensferry Crossing Bridge

Abstract: Abstract To date, the majority of numerical modelling [computational fluid dynamics (CFD)] studies on long-span bridges have been carried out on scaled physical models, and without field-data for validation. For the first time, a full-scale bridge aerodynamic CFD study was conducted in this paper. A full-scale three-dimensional CFD model of the middle span and central tower of the Queensferry Crossing, United Kingdom, was created. The aim of this work was accurately simulating the wind field around the bridge. The CFD simulations were developed in OpenFOAM with the k − ω SST turbulence model. Atmospheric boundary layer inflows were configured based on wind profiles provided by a full-scale Weather Research and Forecasting (WRF) model. CFD predictions were validated with field data which were collected from an on-site Structural Health Monitoring System. The simulated fluctuating wind field closely satisfied the characteristic of field data and demonstrated that the modelling approach had good potential to be used in practical bridge aerodynamic studies. Meanwhile, comparisons and sensitivity analyses on mesh density provided a reference modelling approach for any future works on full-scale bridge aerodynamic models. Additionally, a cylindrical-like domain was applied in bridge aerodynamics for the first time and verified as being a convenient and reliable way to be used in bridge studies that involve changes in yaw angle.

Fire risk assessment of bridges: from state of the art to structural vulnerability mitigation

Abstract: Abstract In recent years, due to the rapid urbanization, the fire risk in transport infrastructures is becoming more critical. These fires, typically caused by highly flammable materials, can significantly compromise the stability of the structure, as well as cause significant economic and social losses. However, in current regulations, no fire design or verification criteria are provided for bridges and the buildings prescriptions are not directly applicable due to the significant differences among the fire conditions. Therefore, starting from a deep literature review, different performance levels for bridges’ structural fire resistance were proposed. These levels were linked to the fire risk classification suggested by Kodur et al., for identifying the most vulnerable bridges to fire. This methodology was applied both to the prescriptive and performance-based approaches, using nominal and natural fire curves derived by advanced zone models of several bridge fire scenarios. To better investigate the structural fire performance of bridges, parametric analyses of a typological bridge were conducted for identifying the most critical structural systems and fire scenarios. One of the most relevant finding is that the use of performance-based approach allows to consider more realistic fire conditions, to satisfy higher performance levels with an optimization of the fire protection design. Therefore, the proposed approach can be useful both for designers and industrial category to assess the bridge performances in fire, not only according to prescriptive approach but also considering the performance-based one.

Validation of an FE model updating procedure for damage assessment using a modular laboratory experiment with a reversible damage mechanism

Abstract: Abstract In this work, the systematic validation of a deterministic finite element (FE) model updating procedure for damage assessment is presented using a self-developed modular laboratory experiment. A fundamental, systematic validation of damage assessment methods is rarely conducted and in many experimental investigations, only one type of defect is introduced at only one position. Often, the damage inserted is irreversible and inspections are only performed visually. Thus, the damage introduced and, with it, the results of the damage assessment method considered are often not entirely analyzed in terms of quantity and quality. To address this shortcoming, a modular steel cantilever beam is designed with nine reversible damage positions and the option to insert different damage scenarios in a controlled manner. The measurement data are made available in open-access form which enables a systematic experimental validation of damage assessment methods. To demonstrate such a systematic validation using the modular laboratory experiment, a deterministic FE model updating procedure previously introduced by the authors is applied and extended. The FE model updating approach uses different parameterized damage distribution functions to update the stiffness properties of the structure considered. The mathematical formulation allows for an updating procedure that is independent of the FE mesh resolution and free of assumptions about the defect location while only needing few design variables. In this work, the FE model updating procedure is based only on eigenfrequency deviations. The results show a precise localization within $$\pm \, {0.05}{\textrm{m}}$$ ± 0.05 m of the nine different damage positions and a correct relative quantification of the three different damage scenarios considered. With that, first, it is shown that the deterministic FE model updating procedure presented is suitable for precise damage assessment. Second, this work demonstrates that the opportunity to introduce several reversible damage positions and distinctly defined types and severities of damage into the laboratory experiment presented generally enables the systematic experimental validation of damage assessment methods.

Methodology for selecting measurement points that optimize information gain for model updating

Abstract: Abstract Information collected through sensor measurements has the potential to improve knowledge of complex-system behavior, leading to better decisions related to system management. In this situation, and particularly when using digital twins, the quality of sensor data determines the improvement that sensors have on decision-making. The choice of the monitoring system, including sensor types and their configuration, is typically made using engineering judgement alone. As the price of sensor devices is usually low, large sensor networks have been implemented. As sensors are often used to monitor at high frequencies over long periods, very large data sets are collected. However, model predictions of system behavior are often influenced by only a few parameters. Informative data sets are thus difficult to extract as they are often hidden amid redundant and other types of irrelevant data when updating key parameter values. This study presents a methodology for selecting informative measurements within large data sets for a given model-updating task. By selecting the smallest set that maximizes the information gain, data sets can be significantly refined, leading to increased data-interpretation efficiency. Results of an excavation case study show that the information gains with refined measurement sets that are much smaller than the entire data set are better than using the data set prior to refinement for the same probability of identification, while the computational time of model updating is significantly reduced. This methodology thus supports engineers for significant data filtering to improve model-updating performance.

Influence of system changes on closely spaced modes of a large-scale concrete tower for the application to structural health monitoring

Abstract: Abstract Concrete steel towers are increasingly being used for onshore wind turbines. The lower part consists of separated segmented concrete rings connected with dry joints. Due to slight deviations from the axisymmetric cross-section, closely spaced modes occur. Therefore, the influences of small system changes on closely spaced modes, particularly the mode shapes, should be investigated to enable reliable vibration-based monitoring. In this context, the influence of imperfections due to the waviness of the dry joints requires attention. As no acceleration measurements on concrete towers considering small system changes have been performed so far, this has not yet been investigated. Therefore, an experiment is carried out using a large-scale laboratory model of a prestressed concrete segment tower. The system modifications are introduced by changing the preload. This changes the influence of imperfections of the surfaces of the horizontal dry joints, estimated by measuring strain and displacement at the lowest joint. An increasing preload causes the first two pairs of bending modes to move closer together. This enables to study the effect of the closeness of natural frequencies on the related mode shapes based on the same structure. Thus, the known effects of increasing uncertainty of the alignment and a rotation of the mode shape in the mode subspace with closer natural frequencies can be shown experimentally. In this work, the operational modal analysis (OMA) methods Bayesian-OMA (BAYOMA) and Stochastic Subspace Identification (SSI) are used. Local imperfections can significantly affect modal parameters, so these should be considered for vibration-based monitoring.