Structural Control & Health Monitoring最新文献

Managing aging engineering structures requires damage identification, capacity reassessment, and prediction of remaining service life. Data from structural health monitoring (SHM) systems can be utilized to detect and characterize potential damage. However, environmental and operational variations impair the identification of damages from SHM data. Motivated by this, we introduce a Bayesian probabilistic framework for building models and identifying damage in monitored structures subject to environmental variability. The novelty of our work lies (a) in explicitly considering the effect of environmental influences and potential structural damages in the modeling to enable more accurate damage identification and (b) in proposing a methodological workflow for model-based structural health monitoring that leverages model class selection for model building and damage identification. The framework is applied to a progressively damaged reinforced concrete beam subject to temperature variations in a climate chamber. Based on deflections and inclinations measured during diagnostic load tests of the undamaged structure, the most appropriate modeling approach for describing the temperature-dependent behavior of the undamaged beam is identified. In the damaged state, damage is characterized based on the identified model parameters. The location and extent of the identified damage are consistent with the cracks observed in the laboratory. A numerical study with synthetic data is used to validate the parameter identification. The known true parameters lie within the 90% highest density intervals of the posterior distributions of the model parameters, suggesting that this approach is reliable for parameter identification. Our results indicate that the proposed framework can answer the question of damage identification under environmental variations. These findings show a way forward in integrating SHM data into the management of infrastructures.

Metallic yielding devices have been widely used for improving seismic performance of buildings. However, metallic dampers currently in use are often attached to structural systems through brace components, potentially causing conflicts with architectural requirements. In this study, a metallic damper that utilizes the angular deformation generated at the beam-column connection under lateral loads is proposed. The seismic input energy can be dissipated through inelastic deformations of hyperbolic-shaped steel bars. Firstly, this paper introduces the configuration and design concept of the newly proposed rotation-based metallic damper (RMD). Then, in order to investigate the hysteretic behavior and failure modes of the proposed devices, a total of twelve RMD specimens were fabricated, and quasistatic tests were conducted. Subsequently, the influences of physical characteristics associated with hyperbolic-shaped steel bars on the energy dissipation performance of RMD were studied. Finally, finite element analysis was conducted based on the detailed models of RMD specimens, and the results showed a good agreement with the experimental data. The results demonstrate that the RMD exhibits a sound energy dissipation capacity. It is replaceable and flexible in architectural arrangements due to its low space requirements, which is friendly in engineering practice.

Buried PVC water pipeline is prone to deformation due to the influence of unfavorable factors such as external load and soil collapse. Once the deformation exceeds the critical threshold, serious consequences such as leakage and pipeline burst may occur. In response to the crucial issue of monitoring PVC pipeline deformation, the feasibility of utilizing FBG (fiber Bragg grating) sensing technology to monitor the deformation of acrylate polymer blended with poly resin (ABR) pipe subjected to external pressure and soil collapse is explored in this study. To assess the monitoring performance of FBG on the ABR pipe’s surface, an analysis method for package failure and pasted failure of FBG is introduced, along with the calculation formula for strain attenuation based on the Goodman model and the method for calculating the minimum adhesion length. A total of 20 ABR pipes with two cross-sectional forms and different calibers are arranged with external pressure tests or soil collapse tests in this investigation. In the two tests, the circumferential strain measured by FBG is used to analyze the deformation of the ABR pipe and the bending strength. To validate the precision of FBG, a comparison between the strain curve measured by the strain gauge and that measured by the FBG sensors is conducted. The results of the two tests indicate that the deformation of the ABR pipe can be well monitored and the method can be applied to the field applications.

Over the past 20 years, more than 200 major bridge-collapsed accidents have occurred during their service life. The durability of reinforced concrete (RC) beams is a serious threat to the safety performance of the structures. To accurately grasp the service performance of RC beams, a four-point bending loading experiment was conducted on RC rectangular beams, and magnetic field data were detected. The results show that during four-point bending loading, the damage modes of RC beams can be categorized into the elastic stress stage, stage of work with cracks, and yield stage. The change rule of the rebar tangential magnetic induction intensity (B_x) curves varies from overlapping each other to rotating counterclockwise, finally generating abrupt changes. The force-magnetic coupling model is optimized based on the magnetization angle. The “force-magnetic area parameter” K_σx is proposed to quantitatively analyze the rebar stress. Finally, the stress state assessment model of RC beam rebars is established. The relative error of the assessment results is near 6.61%. The nondestructive testing and assessment of the rebar stress state inside the RC beams are realized through the comparison and verification of the experimental phenomenon analysis and the force-magnetic coupling model. It lays a theoretical foundation for ensuring the safe operation of bridge structures and building structures during the service life.

In existing load models, the crowd bouncing load is often simplified as a single-point excitation; moreover, these models lack data support from crowd bouncing experiments. Inspired by the random field models widely adopted in seismic ground motion fields, a random field model for crowd bouncing loads was established in this research. The bouncing frequency, time lag, and amplitude of the coherence function were modeled to quantify the crowd synchronization; an auto-power spectral density (PSD) model from the author’s previous study was adopted for an individual bouncing load. The values of these parameters were obtained based on data from a crowd bouncing experiment involving 48 test subjects on the first day and 42 test subjects on the second day, in which the trajectories of reflective markers fixed at the clavicle of every test subjects were simultaneously recorded using three-dimensional motion capture system. Based on the PSD matrix of the crowd bouncing loads as simulated by the proposed random field model, the structural acceleration can be analyzed using random vibration analysis in the frequency domain. The established random field model and spectral analysis framework can be adopted to evaluate the vibrating performances of lightweight and high-strength structures. Moreover, the established load model is also the basis of structural vibration control.

This paper proposes a novel parameterized frequency-domain modal parameter identification method, called direct modal variational mode decomposition (DMVMD), based on the multivariate variational mode decomposition (MVMD) framework and the principle of modal superposition. Under the constraint of normalized mode shapes, this paper theoretically derives the relationship between multivariate variational mode decomposition and the natural frequencies and mode shapes of structural systems. The aim is to extract K response modes and their corresponding mode shapes from the excited C-dimensional vibration signals of the measured component’s response. First, the measured multichannel vibration signals are decomposed into IMFs aligned with K-order natural frequencies using multivariate variational mode decomposition (MVMD). Then, the Hilbert equations and mode shape normalization constraints are used to solve the structural natural frequencies and mode shapes. Furthermore, the proposed multimodal identification algorithm has been validated through numerical simulations and experimental examples, demonstrating its high accuracy and robustness in modal identification. Compared to the existing multimodal algorithms related to variational mode decomposition, the proposed method is more direct and elegant. This method has been successfully applied to the modal parameter identification of subway tunnel structures, enabling accurate determination of the location of tunnel damage through analysis of the identified modal parameters.

In response to the intricate installation challenges and the elevated cost of sensors for measuring base shear in large-scale structures, this paper proposes a noncontact measurement method integrating computer vision and model updating to invert structural base shear. The computer vision part measures physical displacement, while the nonlinear model updating section inverts base shear by refining the structural numerical model, thus achieving cost-effective, noncontact inverting measurements. In the computer vision component, a highly real-time and accurate optical flow estimation algorithm was selected and validated in actuator motion tracking tests, yielding a normalized root mean square error of less than 3% between displacement tracking and sensor measurable results. The model-updating section adopts the Bouc–Wen model, demonstrating through numerical simulations its ability to swiftly calibrate the numerical model within 7000 steps under various noise interference levels, accurately obtaining structural base shear. Moreover, the influence of different response combinations and sampling frequencies on parameter identification for model updating is discussed. Findings indicate that when considering both displacement and acceleration, along with a sampling frequency of 200 Hz, parameter identification meets accuracy requirements due to reduced susceptibility to measurement noise. In addition, a shake table test on a three-layer shear frame is conducted to further validate the proposed method’s feasibility. Test results demonstrate that the amplitude and fluctuation trend of the shake table test’s identification results mirror those of the numerical simulation results within the first 25 seconds, with a peak value error of 18.9%. While the error is relatively large, this paper provides a practical research framework for model updating and structural health monitoring. Simultaneously, it reduces the cost of acquiring structural response data during tests, thereby facilitating the application and promotion of computer vision technology in structural response monitoring.

In this study, a novel damage detection framework for skeletal structures is presented. The introduced scheme is based on the optimization-based model updating method. A new multipopulation framework, namely, the Famine Algorithm, is introduced that hopes to reduce the number of objective function evaluations needed. Furthermore, using static displacement patterns, a damage-sensitive feature named pseudo-kinetic energy is presented. By exploiting the new feature, an efficient cost function is developed. Two mathematical benchmark problems and a two-membered truss for damage detection problem are depicted in 2D space to track the search behavior of the Famine Algorithm and show the changes in the search space when using the new feature. Four numerical examples, including three trusses and a frame structure, are used to evaluate the overall performance of the proposed damage detection methods. Moreover, an experimental shear frame is studied to test the performance of the suggested method in real-life problems. The obtained results of the examples reveal that the proposed method can identify and quantify the damaged elements accurately by only utilizing the first five vibrating modes, even in noise-contaminated conditions.

Modal parameter identification via ambient vibration is popular but faces challenges from uncertainties due to unknown inputs and low signal-to-noise ratio. Bayesian methods are gaining increasing attention for operational modal identification due to their ability to quantify uncertainties. However, improvements in computational efficiency are needed, particularly when addressing numerous modes and degrees of freedom. To address this challenge, this study proposes an innovative approach, termed the “Bayesian spectral decomposition” method (BSD), employing the decompose-and-conquer strategy. This novel method, operating within the frequency domain, identifies each mode individually by exploiting their inherent separated modal characteristics. For each mode, the response spectrum matrix undergoes an eigenvalue decomposition, yielding crucial eigenvalues (incorporating frequency and damping information) and eigenvectors (containing mode shape information). Subsequently, statistical properties of the eigenvalues and eigenvectors are utilized to establish likelihood functions for Bayesian parameter identification. By combining prior information, the posterior probability distribution functions of modal parameters are derived. The optimal solution is then obtained by resolving the maximum posterior probability distribution function problem. To further quantify the uncertainty of modal parameters, Gaussian distributions are employed to approximate the posterior probability distribution functions. The adoption of the decomposition approach circumvents the joint identification of all modal parameters, substantially reducing the parameter dimensions for optimization. Consequently, this strategy leads to decreased computational complexity and significantly improved computational stability. The effectiveness of the BSD is confirmed through simulated data generated from an 8-story shear building as well as measured data collected from both an experimental shear frame and the Canton Tower. The results demonstrate that the proposed method achieves high accuracy in identifying modal parameters, greatly improves computational efficiency, and effectively quantifies the uncertainties in modal parameters.

Structural health monitoring (SHM) faces a significant challenge in accurately detecting damage due to noise in acquired signals in composite plates, which can adversely affect reliability. Specific noise reduction techniques tailored to SHM signals are developed to tackle this issue. Gaussian smoothing proves effective in reducing noise and enhancing signal features, thereby facilitating the identification of damage-related information. Optimization algorithms play a crucial role in damage detection, especially when integrated with smoothing and fusion techniques, as they provide optimal solutions to SHM challenges. A model-updating-based optimization algorithm is proposed for detecting damage in structures using condensed frequency response functions (CFRFs), even in the presence of various types of noise and measurement errors. The CFRF signals are first smoothed using an optimized Gaussian smoothing technique as part of the proposed method. Then, the proposed methodology integrates diverse smoothed signals using a raw data fusion approach, including those from different excitations, frequency ranges, and sensor placements. Fused smoothed signals are then fed into a new objective function, incorporating mutual information (MI) and Gaussian smoothing to mitigate correlated coloured noise. The proposed objective function also introduces a hyperparameter tuning of Gaussian smoothing to enhance its performance. Optimization via the improved reptile search algorithm (IRSA) updates the objective function, optimizing damage and smoothing parameters. The hybrid method detects damage in numerical composite laminated plates with different layers and boundary conditions, demonstrating its effectiveness as an SHM technique. Comparative evaluations of other state-of-the-art methods show that the proposed method outperforms its counterparts, making it a promising damage detection approach to address the noise challenge in the SHM field.