Structural Control & Health Monitoring最新文献

Bridges enable communications and transportation of goods nationally and internationally, underpinning economic and social activities, and thus they are pylons of our prosperity and mobility. Bridges worldwide are constantly subjected to physical wear, ageing, deterioration, hazards, environmental influences, and increased loading. Loss of performance and functionality of bridge structures would have a crucial impact on overall infrastructural resilience and would cause significant negative economic and social consequences. Monitoring their behaviour for different loading conditions relies on accurate estimations of the stress-strain state of various critical components and remaining capacities. These activities are of high importance for better planning and lifespan prolongation, that is, the extension of their service life and prevention of unforeseen collapses, in line with sustainability principles of well-informed maintenance. In many cases, access to the structure is limited or even impossible, which causes the need for the deployment of remote and contactless methods. One such innovative technique, which has recently attracted attention in scientific and practical applications, is the digital image correlation (DIC). DIC is a contactless approach applicable for obtaining the full field of strains and deformations of full-scale real structures. Although the DIC approach has been widely used in world engineering practice for monitoring bridges and has proved to be a reliable and accurate method, there is a lack of systematic integral review on previous practical applications, revealing limitations and perspectives. This is the main motivation and novelty of this study, which will describe selected case studies in which DIC was used on real full-scale bridge structures and propose improvements for the method.

This paper presents an early warning system to investigate deformations in simply supported concrete girder bridges over time, using the information content provided by satellite data, integrated with other available sources. The safety of the existing bridges is a priority for transportation management companies, which should carry out continuous and accurate monitoring campaigns, by exploiting traditional time- and cost-consuming activities that cannot be widely applied to a bridge portfolio scale. To reduce management costs and to define reliable prioritization schemes, new cost-effective technologies can be involved such as the satellite-based Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). This technique can represent a valuable option for observing the displacements induced by different actions and to relate the identified behaviour to possible or future fails. This paper presents an early warning system aimed at exploring possible anomalies in simply supported reinforced concrete girder bridges by efficiently elaborating MTInSAR, combined with additional data (e.g., environmental temperature and structural information knowledge). The proposed framework allows manipulating the persistent scatterers’ information to derive longitudinal and vertical displacements over time. These are compared to appropriate thresholds leading to potential early warnings aimed at supporting road managers in undertaking future surveillance actions. The proposed procedure was tested on a case study, defined according to the most spread typology of bridges in Italy. This application highlights the advantages of the proposed framework which allows for a cost-effective long-term monitoring with outputs that can be automatically updated over time and suitable for network-scale early warning detection.

Bridge health monitoring confronts a critical challenge in extracting meaningful features that are sensitive to structural damage while remaining nonsensitive to operational environments and loads. Most of structural response features such as power spectral density (PSD) in the long-term monitored bridge are influenced by operational vehicle loads. The power spectral density transmissibility (PSDT), defined as the power spectral density ratio of two measured output responses at two different structural locations with the same reference output response, converges independently at the system poles of the applied excitations and transferring outputs. Capitalizing on such a unique property of PSDT around the system poles, the PSDT-based spectral moment is proposed in the paper to establish a robust structural feature in bridge health monitoring taking into account the time-varying characteristics under operational vehicle loads. Numerical simulations and comparisons with PSD-based spectral moment analysis reveal that the PSDT-based spectral moment exhibits an enhanced robustness to traffic flow excitations and heightened sensitivity to changes in structural parameters. Further laboratory experimental results on the beam under moving vehicle confirm that the PSDT-based spectral moment is less affected by moving vehicle loads, but it demonstrates higher sensitivity to structural parameter changes. Given its robust properties of low sensitivity to operational vehicle loads and sensitivity to changes in structural parameters, the proposed PSDT-based spectral moment emerges as an ideal structural feature suitable for the effective applications in the long-term bridge health monitoring, such as structural damage identification, model updating, condition assessment, and safety warning.

Damage detection in bridge structures has always been challenging, particularly for long-span bridges with complex structural forms. In this study, a partial-model-based damage detection method was proposed for the damage identification of long-span steel truss bridges. The proposed method employs partial models to estimate the parameters using the stiffness separation method. This approach obviates the need to construct complete stiffness information for the structure. In contrast, it depends solely on the arrangement of the structural members and material information in the recognized area. This technique can effectively circumvent the construction of an overall structural model and reduce the complexity of damage identification in large structures. A full-scale long-span steel truss bridge in service was used to illustrate the feasibility of the proposed method. The locations of the three partial models were considered in the model analysis, and the parameter estimation efficiency of the Nelder–Mead simplex and quasi-Newton algorithms were compared.

Active flap is an advanced aerodynamic measure that can effectively increase the flutter performance of flexible bridges, but its control mechanism is still confusing due to the complex phenomenon of aerodynamic interference between the deck and flaps. This study proposes an assessment method to clarify the flutter control mechanism of the deck-flap system by the computational fluid dynamics (CFD) method and quantifies the contribution of the aerodynamic damping from the active flaps. It is found that the composition of active flap to the improvement of flutter performance can be divided into torque effect and interference effect. Also, the torque effect of the flaps mainly provides equivalent positive aerodynamic damping ratio under effective control parameters, but the interference effects with the deck and two flaps are not the same, and the mutual interference effect between the two flaps is very weak. For the purpose of investigating the aerodynamic interference influence between the girder and flaps, the research further discussed the impact of the distance between the deck mounting position and the bridge girder on the system flutter performance. As the distance increases, the flutter performance of the system gradually improves. Also, the torque effect of the leading and trailing flaps will increase with distance. However, the interference effects of the flaps on both sides show different rules. In total aerodynamic damping ratio of the deck-flap system, the torque effect accounts for about 70% and interference effect accounts for 30%. As the distance increases, the torque effect gradually becomes stronger and the interference effect gradually weakens.

Many modern pedestrian bridges exhibit flexibility and susceptibility to vibrations due to the use of lightweight and high-strength materials, which can cause discomfort for pedestrians and affect their serviceability. Although gait biomechanics have been extensively studied and optimisation techniques for gait prediction on rigid surfaces have been previously employed, there is a paucity of studies investigating the effects of human-structure interaction on pedestrian crossings over flexible structures. In this study, inverse dynamics and optimisation techniques were employed to predict human gait on a flexible structure in the sagittal plane. Gait was formulated as an optimal motor task subject to multiple constraints, with the performance criterion being the minimization of mechanical energy expenditure throughout a complete gait cycle. Segmental movements, pedestrian-applied forces, and bridge vibrations were predicted based on parameters describing gait (such as gait speed, gait frequency, and double support duration), as well as physical and dynamic parameters characterizing the pedestrian bridge (including natural frequency, damping coefficient, and bridge length).

This research aims to develop and validate a fiber Bragg grating (FBG) accelerometer, designed with a bearing and flexure hinge structure, to accurately measure medium- and high-frequency vibrations caused by wheel-rail excitation. The structural parameters of the accelerometer are optimized through theoretical mechanics analysis, and its dynamic characteristics are verified by experimental vibration testing and compared with the finite element simulated results. Key findings reveal that the proposed sensor has a wide operational frequency range of 10–1200 Hz and a high acceleration sensitivity of 3 pm/m·s⁻², in addition to excellent linearity and repeatability. Moreover, the sensor demonstrates immunity to temperature variations, making it suitable for use in fluctuating temperature environments. Laboratory model experiment tests of high-speed train tracks show that the FBG accelerometer effectively identifies medium- to high-frequency vibration signals caused by wheel-rail excitation, corroborated by traditional piezoelectric accelerometers. The results confirm the sensor’s ability to capture vertical axle box vibration acceleration (ABVA) and its potential for assessing axle box structural dynamics in high-speed railway applications.

The finite element model updating (FEMU) and structural optimization of high-fidelity numerical models for large civil structures require significant computational resources and efficient optimization algorithms. However, prior research has predominantly relied on commercial software, which has more restrictions compared to open-source ones. A cluster computing-aided programming framework for the FEMU of large civil structures was developed based on the open-source platforms OpenSees and Python. The high-performance computing (HPC) cluster was built to connect the cloud/local computing resources. Then, the cluster computing-aided particle swarm optimization (PSO) algorithm, suitable for scientific computing on HPC cluster, was developed. The software interfaces were programmed to connect OpenSees with HPC cluster to achieve high-performance FEMU and structural optimization. The advantages of the framework include (1) an open-source cluster computing platform suitable for FEMU and structural design optimization is developed utilizing dispy; (2) the framework is convenient to use, highly efficient in computation, and is capable of fully utilizing both local and cloud computational resources to improve computational efficiency; and (3) it has strong compatibility and is flexible to be customized for various engineering problems by embedding objective functions. Four examples were used to illustrate the applications of this framework in different fields.

Bridges in areas with high seismic risk are constantly exposed to earthquake threats. Therefore, comprehensive bridge damage assessments are essential for postearthquake retrofitting and safety assurance. However, traditional methods of assessing damage and collecting data are time-consuming and labor-intensive. To address this issue, this study proposes a deep generative adversarial network (GAN)-based approach to predict the surface damage patterns of bridge columns. Using visual patterns from experimental tests, the proposed approach can generate surface damage to the synthetic column, such as cracks and concrete splinters. The study also investigates the effects of different data representation schemes, such as grayscale, black and white, and obstacle-removed images, and uses the corresponding damage indices as additional constraints to improve network training. The results show that the proposed approach can offer a reliable reference for bridge engineers to evaluate and repair seismic-induced bridge damage, which can significantly lower the cost of disaster reconnaissance.

The identification and classification of railway turnout faults are essential for guaranteeing train safety. Traditional diagnostic methods for these faults face challenges due to limited accuracy, stemming from the scarcity of fault samples, and often fail to provide detailed fault classification. In response to these issues, we introduce an advanced two-stage model for the classification of railway turnout faults, utilizing the FastDTW algorithm, known for its efficient approximation of DTW (dynamic time warping) with linear time and space complexity. In the first stage, we employ a Shapelets feature extraction algorithm, based on a greedy strategy, to efficiently identify the most representative segments from long sequence action curves. Progressing to the second stage, the model tackles the inherent singularities in the FastDTW algorithm by incorporating a novel curve segmentation technique, also rooted in a greedy strategy. This technique fine-tunes the fault classification process, leading to more accurate outcomes. The effectiveness and precision of our proposed model were validated empirically using a dataset of 540 faulty curves from a specific high-speed railway station, achieving an impressive classification accuracy of 97%. This substantial accuracy in fault curve classification underscores the potential of our model to significantly enhance the safety and efficiency of railway operations, marking a notable advancement in the field of railway turnout fault classification.