Structural Control & Health Monitoring最新文献

The weight of existing buildings is a critical parameter in various structural engineering applications, including seismic assessment, uneven settlement evaluation, structural vibration control, building relocation, and demolition operations. While current practice typically estimates this value by multiplying floor area multiplied by an empirical unit weight coefficient. This approach faces limitations when the original design details are unavailable, making total floor area difficult to determine. To address this challenge, this study develops predictive models for estimating the weight of existing reinforce concrete (RC) buildings using easily accessible structural parameters, such as structural height, plan dimensions, number of stories, and fundamental period. A database comprising the weights and related design parameters of 732 RC buildings was developed through an extensive literature search. The maximum information coefficient and Kruskal–Wallis analysis of variance were used to identify factors that significantly influence building weight. Subsequently, regression formulas for building weight, incorporating structural height, plan dimensions of a standard floor, fundamental period, and structural type were established. These prediction formulas were applied to five building examples, and the results were compared with actual values. The comparison shows that the weight prediction formulas have good accuracy and can be used in state assessment of existing buildings and parametric modeling in disaster prevention analysis of urban buildings. Finally, the predictive models have been deployed on an online web page for the convenience of users.

With the development of bridge construction equipment and technologies, prefabricated girders have become larger and heavier, posing an increasingly higher challenge to the trial assembly quality control. Hence, more attention has been devoted to the virtual trial assembly (VTA) due to the success of laser techniques in bridge construction. Based on the Shenzhen–Zhongshan link, a world-class sea-crossing project, we develop a laser point cloud-driven VTA method for prefabricated girder preventive quality control, addressing challenges arising from the large volume, complex interface configuration, and diverse attachment noises. Specifically, the interface matching feature of the target bridge girder is first extracted from the raw 3D laser point cloud model. Then, the pose adjustment procedure is conducted for a precise virtual assembly, followed by the proposed assembly criteria, namely, the degree of matching (DoM), for quantitative evaluation. Our proposed VTA solution has been successfully applied in the whole construction process of the Shenzhen–Zhongshan link; besides, a comprehensive numerical study has also been conducted, inspired by this project, to prove the accuracy of our proposed VTA method. Results demonstrate that the extracted interface dimensional features are precise with error controlled under 1%. Then, assembly results between two interfaces show that 95% points of the interfaces can remain under 0.005 m in terms of DoM values, demonstrating the performance of the proposed method.

Prestressed bridges’ performance is strongly dependent on the health state of their prestressing cables, but unfortunately, these structural components are hidden and cannot be assessed through visual inspections. Moreover, conventional low-energy methods, like operational modal analysis, are inadequate due to their inability to detect the nonlinear effects of the prestressing force on the response under heavy travelling loads. In this paper, a methodology exploiting the Hilbert–Huang transform (HHT) is investigated in which the bridge’s nonlinear constitutive force–displacement relationship can be reconstructed by analysing the traffic-induced dynamic response, which has the features of a short-time nonstationary and potentially nonlinear signal. HHT, thanks to its adaptability to complex behaviours, is suitable for treating such type of signals and makes it possible to trace the response properties at each time instance, thus allowing to correlate instantaneous values of deformation with the simultaneous instantaneous (tangent) stiffness in a one-to-one relationship. Starting from a previous introductory study, and with the aim of making the proposed approach suitable for real structural health monitoring applications, a comprehensive investigation is performed considering a bridge with dynamical properties in the range of interest and realistic traffic scenarios adequately describing the time series of travelling loads and relevant internal actions. In particular, three main issues are considered: (i) development of a refined probabilistic response model (to be inferred from data collected under service loads) capable to overcome troubles induced by the nonhomogeneous distributions of data, generally consisting of frequent passages of light vehicles and rare passages of heavy vehicles; (ii) convergence analysis aimed at providing a relationship between the duration of the training period and the accuracy expected to infer the probabilistic model; and (iii) proposal and validation of a novel procedure to derive constitutive model of the bridge exploiting only deformation data recorded during vehicle passages and provide a tool for relating prestressing losses to variations in the dynamic response. The outcomes prove the potential of the proposed strategy paving the way for real-world experimental applications.

Replaceable systems, including the main structure and components, are the primary means of building a resilient city. However, due to the complex interaction relationships, the energy-dissipation effect of such structural systems is not ideal. Accordingly, this study used a replaceable corrugated steel plate composite shear wall as an example, focusing on the seismic performance and energy-dissipation ratio analyses of each component. We attempted to find a suitable stiffness ratio index to determine the interaction relationship of the replaceable corrugated steel plate shear wall. An energy-dissipation evaluation method is proposed based on the stiffness ratio index, and the bearing-capacity formula of the shear wall was modified. The results show that the revised lateral-bearing-capacity formula based on the energy dissipation evaluation exhibited ideal accuracy and could provide a reference for practical engineering applications.

A continuous welded rail (CWR) is a critical component of modern rail systems, providing increased stability, improved passenger comfort, and reduced maintenance compared with jointed rails. However, the unique mechanical properties of CWR, particularly in the immovable zone where friction restricts longitudinal deformation, require accurate and continuous monitoring to prevent rail buckling or fractures. Despite the availability of various strain-monitoring technologies, including fiber Bragg grating sensors, strain gauges, and vision-based systems, these approaches have significant limitations in full-scale CWR applications. Challenges such as insufficient resolution for detecting minute strains and sensor-adhesion durability reduce the effectiveness of current strain-monitoring solutions. To address these limitations, we propose a high-resolution, vision-based biaxial strain measurement system specifically designed for CWRs. This system utilizes three microscopic cameras strategically positioned to capture detailed displacement data, allowing for accurate computation of biaxial strain through advanced image processing techniques. The proposed system was validated through both laboratory-scale and full-scale experiments and exhibited a minimum detectable strain of 1.5 µε under controlled loading conditions.

The dam piers undertake crucial tasks of structural support, surface overflow management, and dynamic gate operation under load. Any occurrence of cracks poses risks to their safe and efficient operation. Given the large-volume characteristics of dam pier concrete, controlling cracks is challenging. The existing analytical methods for dam pier concrete still have certain limitations in revealing the causes of cracks under complex environmental conditions. In particular, when accounting for the coupled effects of early-age temperature, humidity, and stress fields, further refinement of analytical models and methods is essential to develop more precise active crack control strategies. This study applied a hygro–thermo–mechanical coupling modeling method for early-age dam pier concrete. Comprehensive physical and mechanical experiments were conducted to calibrate the coupling model parameters to align with actual conditions. An experiment was conducted in a real dam pier to optimize the construction process to control cracks proactively, rather than applying remedial measures postcrack occurrence. The results show that the proposed method effectively analyzes the causes of cracks, and the proactive control measures targeting these causes are proven to be effective. This study provides a reference for proactive crack control of mass concrete structures.

This study introduces a novel semiactive rolling tuned mass damper (SARTMD) developed to mitigate structural vibrations more efficiently than conventional tuned mass dampers (TMDs), with a primary objective of significantly reducing absorber mass without compromising performance. The proposed device combines translational and rotational motions, featuring two key innovations: (i) an umbrella-like mechanism that dynamically adjusts the moment of inertia by varying the radius of a secondary mass, allowing real-time frequency tuning, and (ii) a planetary gearbox that transmits the rotation of a primary rolling mass to the secondary mass, increasing its angular velocity. This configuration enables substantial mass ratio reduction while maintaining high control effectiveness. A numerical model of a single-degree-of-freedom (SDOF) structure subjected to harmonic excitation from rotating machinery is developed to evaluate the system’s performance. A short-time Fourier transform (STFT)-based control algorithm is implemented to continuously match the SARTMD’s natural frequency with the dominant excitation frequency. Parametric studies are conducted to identify optimal ranges for the system’s parameters. Simulation results show that, compared to an uncontrolled system, the SARTMD reduces the peak displacement, velocity, and acceleration by 76.4%, 77.4%, and 77.9%, respectively, and lowers RMS responses by over 91%. Compared to a traditional TMD, it achieves 66% greater peak displacement reduction while using only 30% of the mass. Robustness analysis confirms that the system maintains effective performance under up to 30% frequency detuning. These results confirm that the SARTMD offers a lightweight, adaptive, and highly efficient alternative for structural vibration mitigation in applications with space or weight constraints.

Structural modal parameters are crucial for monitoring the condition of bridges. Operational modal analysis (OMA) has garnered great attention in vibration-based structural health monitoring of bridges because it only requires vibration measurements from multiple sensors. Slight asynchronization often occurs in these measurements during the monitoring process. Applying classical OMA methods, such as the natural excitation technique (NExT) combined with the eigensystem realization algorithm (ERA), to asynchronous vibration measurements can lead to significant errors in modal parameters. To address this issue, this study proposes a modal assurance criterion (MAC)-based time synchronization technique to generate reliable synchronous vibration measurements for modal identification. The MAC-based method takes advantage of the proportionality of modal components and is only capable of detecting nonsynchronized issues between single-degree-of-freedom (SDOF) signals. A variational mode extraction (VME) technique is employed to iteratively decompose bridge vibration measurements into SDOF components. The VME technique eliminates the need for artificially predefining the number of modes, which was required in many signal decomposition techniques. After time synchronization, the proposed method employs the NExT–ERA-based automatic OMA method for modal identification. The effectiveness of the proposed method is demonstrated using vibration measurements from both the finite element model of a highway bridge and field monitoring data from an actual bridge. The results show that the proposed method successfully synchronizes vibration signals and identifies mode shapes, even in the presence of modal node phenomena.

In condition monitoring, the reliability of a predictive maintenance program is critically dependent on the precision of data obtained from measurement systems. With increased availability, a significant challenge is evaluating the capability of these measurement systems to ensure data precision, which is fundamental for informed system selection. To address this challenge, this study proposes a systematic framework for evaluating the capability of these measurement systems using Gage repeatability and reproducibility (Gage R&R) technique, subsequently judging the acceptability level and guiding their selection to guarantee the data precision. Our study investigates the capability of these systems in terms of repeatability and reproducibility, quantifying the contributions of different sources to the systems’ capability and providing directions for measurement system correction and enhancement. Another distinctive innovation of our approach is the use of three-region graphs, incorporating metrics including percentage of Gage R&R to total variation, precision-to-tolerance ratio, and signal-to-noise ratio, which presents a comprehensive overview of the systems’ capability within one single figure. Two comparative experiments in distinct application scenarios were conducted to validate the effectiveness of the proposed framework. The insights presented serve as a valuable reference to replace the commonly used experience-based system selection in condition monitoring. Through this framework, we present a promising data-based approach aimed at enhancing the widely employed time-based calibration strategies, ultimately contributing to the improvement of data quality and the overall success of condition monitoring initiatives.

There is a consistent discrepancy between the predicted and measured dynamic responses of in situ offshore wind turbine (OWT) structures. Underestimation of the foundation soil stiffness is thought to contribute significantly to this difference. Identification of the in situ foundation properties of OWT from monitoring data would reduce this uncertainty, providing critical feedback on foundation design methods and aiding lifetime reassessment. In this study, a system identification framework for estimating the in situ foundation stiffness of a parked OWT is presented using a model updating approach applied to simulated data. The results are shown to accurately replicate the behaviour of the true foundation. The study also demonstrates that the nonproportional nature of the aerodynamic damping causes the structure to exhibit mode shapes whose real parts do not correspond to those of the undamped system. A normalisation technique is applied that obtains a close approximation of the undamped mode shapes from the complex damped mode shapes. It is demonstrated that large errors are introduced in the identified foundation behaviour if this normalisation is not employed. Such errors can result in misleading interpretations of the foundation or superstructure properties of the OWT.