Structural Control & Health Monitoring最新文献

Friction dampers are widely used due to their simple structure, remarkable energy dissipation capacity, and frequency independence. However, existing friction dampers are prone to relaxing the preload force during long-term service, which can lead to cold bonding or cold solidification. To overcome this critical shortcoming, a novel non-preload variable friction damper (NVFD) was firstly proposed. The construction of the proposed NVFD is provided in detail. Furthermore, restoring the force model through the amplification factors of friction force and inertial mass was derived based on the principle of the proposed NVFD. Then, pseudo-static tests with various parameters were conducted. Finally, a single-degree-of-freedom (SDOF) structure was employed to compare the effectiveness of this paper’s new NVFD with a conventional friction damper (FD) under various earthquake levels. The results show that non-preload characteristics avoided the problems of large preloads by traditional friction dampers; thus, the NVFD had stable and reliable variable friction performance, which can effectively adapt to different hazard levels.

Data-driven structural health monitoring (SHM) is an approach which relies on the information contained in the data and through signal analysis techniques captures the features, variations, and uncertainties that data contain. This paper presents the response of shaking table tests of a full-scale, 3-story building with sliding slabs connected by horizontal buckling-restrained braces for energy dissipation. First, the global dynamic characteristics of the structure were identified from a series of the building response data under different intensity level of base excitations. The variation of the identified modal parameters, such as the mode frequencies and modal shapes, was discovered. The influence of sliding slabs on the dynamic characteristics of the frame was also investigated through the measured response and the equation of motion with six degree of freedom systems. Comparison on the achieved interstory stiffness due to the implementation of sliding slabs and the fixed (locked up) slab was examined. The mechanism and dynamic characteristics of sliding slabs, including energy dissipation of the friction force, BRB hysteresis behavior, and unintended damping force during strong base excitation were analyzed directly using the ARX/recursive model. The extracted unintended damping force performed like a friction hysteretic response, which needs to be considered for frame modeling in shaking table tests. The findings through the data analysis have clarified the important aspects of sliding slabs and demonstrated the benefits and applicability of sliding slabs on reducing the frame response.

Cracks exist in the majority of components of ancient Chinese timber structures and have led to serious mechanical property degradation problems, threatening the safety of the whole structures and making the cracks’ detection for wooden components a necessity. With the rapid development of intelligent protection technology of cultural buildings, it is important to establish a scientific identification and quantification method for cracks in wooden components to replace traditional manual detection techniques. Deep learning is precisely such an advanced technology. In this study, images of cracked wooden components were first collected from the Yingxian wooden pagoda and the crack characteristics were analyzed. A dataset for crack segmentation was established using a total of 501 images of cracked wooden components, including a training dataset of 450 images and a validation dataset of 51 images. Based on the mathematical principles of deep learning and the fully convolutional neural networks (FCNN), a deep fully convolutional neural network (d-FCNN) model was constructed based on encoding and decoding methodology. Four model indicators, pixel accuracy (PA), average pixel accuracy (mPA), mean intersection over union (mIoU), and F1-score were analyzed to train the model and determine the optimal model parameters, including learning rate, batch size, and epoch. It concluded that the optimal initial learning rate takes the value of 10⁻⁴, batch size of 6, and epoch of 100, achieving the average accuracy of 78.8%. Further, based on the pixel’s accumulation principle, a quantitative calculation method for crack length and maximum width was proposed. Two cracked wooden columns were prepared, and crack image identification and quantification experiments were conducted to verify the correctness of the constructed d-FCNN model and the proposed crack quantification method. The results show that the model is suitable for crack intelligence detection, identification, and quantification of cracked wooden components.

The economic impact of Structural Health Monitoring Systems based on optical fibre sensors is assessed in the development of composite helicopter rotor blades. Hence, the focus of this analysis is on the helicopter’s Beginning Of Life stage. Two applications of the Structural Health Monitoring System are considered in the development of composite blades: curing cycle development and accomplishment of laboratory and flight certification tests. Optical fibre sensors measure the temperature field during the curing cycle and strain field during the laboratory tests and allow load identification during the load survey activity. It was found that Structural Health Monitoring Systems can potentially lead to economic benefits during the development of the blade provide that a reduction in the number of curing cycles and number of blades tested is achieved as a consequence of the improvement of the temperature and strain field quality. Moreover, an economic benefit could be achieved during the load survey activity, needed to complete the certification of the composite blade, avoiding the periodical maintenance of the applied strain gauges acquiring the strains during the flight.

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).