Structural Control & Health Monitoring最新文献

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.

A methodology utilizing vibration data and Mel-frequency cepstral coefficients (MFCCs) for wind turbine condition monitoring is developed to detect incipient faults in the wind turbine gearbox. This approach provides a more efficient and cost-effective solution compared to traditional condition monitoring techniques relying on physical inspections, which can be time-consuming and labor-intensive. The use of vibration data enables the identification of subtle changes in a wind turbine’s operating condition, providing early warning signs of potential issues. When the vibration data are analyzed, changes in frequency and amplitude can be detected, indicating the presence of a developing fault. Vibration-based condition monitoring systems (CMS) have already been widely used in the wind industry (mainly in new turbines). These systems utilize basic standard features, working in either the time or frequency domain, and are not optimized for nonstationary signals. In contrast, this work focuses on MFCCs, operating in both time and frequency domains, enabling the extraction of adequate information from nonstationary signals. The MFCCs are derived from vibration data signals, providing a compact representation for a more efficient analysis. These coefficients create a fingerprint of the wind turbine operating condition, compared to known healthy conditions, to identify anomalies. To underscore the practical value of this study, it is important to highlight the significant implications for the wind energy sector. The methodology developed offers an advanced, predictive tool for the early detection of gearbox faults, which is a critical aspect of optimizing the performance and longevity of wind turbines. By enabling earlier, more accurate fault detection, the proposed approach significantly reduces the likelihood of catastrophic failures and extensive downtime. This not only enhances the reliability and cost-effectiveness of wind energy systems but also contributes to sustainable energy practices by optimizing resource use and minimizing maintenance costs. The results strongly suggest that the proposed methodology is highly effective in detecting incipient faults in the wind turbine gearbox. By providing early warnings of damage, operators can address issues before significant downtime or damage occurs. The use of MFCCs offers additional benefits since data can be collected remotely, eliminating physical inspections. Analysis can be performed faster, even in real time, allowing more frequent monitoring. This provides a more complete and accurate picture of the health of the system. The approach is tested in the EISLAB dataset concerning vibration signals from six wind turbines in northern Sweden, all with three-stage gearboxes. All measurement data correspond to the axial direction of an accelerometer in the output shaft bearing housing of each turbine.

As the main structural component, the possibility of wind-induced vibration, especially vortex-induced vibrations (VIVs), is greatly increased due to the shape and structural characteristics of the long suspenders. To investigate the full-scale wind-induced vibration of the long suspenders of a long-span suspension bridge with a main span of 1418 m, the long-term vibration-based monitoring system was established. Based on the recorded structural health monitoring (SHM) data, the corresponding wind conditions and the vibration characteristics of long suspenders with different diameters and tensions are investigated. Furthermore, modal parameters including frequencies and damping ratios of long suspenders are identified and tracked during the VIV period. The relationship between the shedding frequency of long suspenders and the corresponding wind speed is studied. Results show that the VIVs with frequencies ranging from 8 Hz to 20 Hz were observed continuously across a wide range of wind speeds in both sets of long suspenders. Due to the relatively low modal damping, significant vortex characteristics and lock-in phenomena can be expected on the long suspenders. A new frequency-adjustable Stockbridge damper is employed to suppress multimodal VIVs in the long suspenders. The effectiveness of Stockbridge damper is verified through field application and comparative analysis. Finally, the effect of long suspender VIVs on local deck vibration is discussed, and it is clarified that the bridge deck vibration is mainly caused by multimodal VIVs of the long suspenders, rather than by external loads such as vehicles and wind. The study endeavors to provide a case to progress the identification, assessment, and control of long suspender VIVs in similar long-span bridges.

This paper experimentally assesses the efficacy of the indirect Structural Health Monitoring (iSHM) framework on a full-scale short-span bridge of nine meters long, using an instrumented vehicle with non-negligible mass with respect to the mass of the bridge. Emphasis is given to the dynamic identification of the two mechanical systems through Experimental Modal Analysis (EMA) on both the vehicle and the bridge. The EMA vehicle testing is among the main contributions of this paper, as such data become available in experimental iSHM implementations for the first time in the literature. Thus, new insights are brought on the vehicle’s dual role as a roving sensing unit and a vibrating mechanical system. A wireless sensor network is adopted that supports a dual monitoring system, i.e., an indirect system with accelerometers on the vehicle and a conventional system with fixed sensors on the bridge. Under a stationary vehicle’s position on the bridge, it is shown that a strong dynamic coupling occurs between the two systems due to their high mass ratio and the vehicle’s function as a Spring Mass Damper (SMD). In vehicle’s moving state, it is demonstrated that transfer of energy occurs between the vehicle and the bridge, which both oscillate under multiple modes of vibration that change over time. It is identified that four main parameters influence the quality of the extracted bridge natural frequencies from the vehicle-acquired data, i.e., (i) the filtering properties of the vehicle, (ii) the effective signals length in the presence of road discontinuities, (iii) the speed trade-offs, and (iv) the level of vehicle-induced bridge excitation and its transmissibility level. The careful consideration of those parameters determines the effectiveness of iSHM implementations in short-span bridges.

In the realm of structural health monitoring (SHM), understanding the expected behavior of a structure is vital for the timely identification of anomalous activities. Existing methods often model only the physical quantities of monitoring data, neglecting the corresponding temporal information. To address this, this paper presents an innovative deep learning framework that synergistically combines a BiLSTM model, fortified by a temporal pattern attention (TPA) mechanism, with time-encoded temperature and traffic-induced deflection-temporal patterns. The arithmetic optimization algorithm (AOA) is employed for optimal hyperparameter tuning, and incremental learning was implemented to enable real-time updates of the model. Based on the proposed framework, an anomaly detection method was subsequently developed. This method is bidirectional: it uses quantile loss to provide expected ranges for structural behavior, identifying isolated anomalies, while the windowed normalized mutual information (WNMI) based on multivariate kernel density estimation (MKDE) helps detect trend variability caused by decreases in structural stiffness. This framework and the anomaly detection method were validated using data from an operational cable-stayed bridge. The results demonstrate that the method effectively predicts structural behavior and detects anomalies, highlighting the critical role of temporal information in SHM.

Traditional manual inspection technology has the problems of high risk, low efficiency, and being time-consuming in bridge safety management. The unmanned aerial vehicle (UAV)-based detection technology is widely used in bridge structure safety monitoring. However, the existing deep learning-based concrete crack identification method has great limitations in dealing with complex background and tiny cracks in bridge structures. To address these problems, this study designs a crack pixel-level high-performance segmentation model for bridge concrete cracks that is suitable for UAV detection scenarios using machine vision (MV) and deep learning (DL) algorithms. First, considering the high requirements for the computing performance of the MV-based model for UAV-based detection, the ResNet-18-based lightweight convolutional neural network is used to represent the traditional large-scale backbone network of the pyramid scene parsing network (PSPNet) to develop a high-performance crack automatic identification model. Then, considering that bridge concrete cracks have the characteristics of subtle shapes and complex backgrounds, the spatial position self-attention module is inserted into the PSPNet to improve its detection accuracy. A concrete bridge is used for the case study, and a dataset of cracks in bridge concrete structures collected by UAVs is constructed and used for model training. The experimental results show that the loss function of the developed method in the training process results in a smooth decline, and the developed algorithm achieves the evaluation indicators of 0.9008 precision, 0.8750 recall, 0.8820 accuracy, and 0.9012 IOU on the bridge concrete crack dataset, which are significantly higher than other state-of-the-art baseline methods. In addition, four common UAV bridge detection scenarios, including low light, complex crack forms, high background roughness, and complex background scenes, are used to further test the crack detection ability of the developed crack identification model. The experimental results show that the proposed crack identification method can effectively overcome interference and real-size pixel-level segmentation of crack morphology. In addition, it also achieved a detection efficiency of 35.04 FPS, which shows that the real-time detection ability of the method has good applicability in the UAV detection scene.

This paper introduces a novel and comprehensive model for the analysis of dam deformation trends, integrating the variational mode decomposition (VMD) method, fractal theory, and the whale optimization algorithm (WOA) to refine the deep extreme learning machine (DELM) model. This integration allows for a meticulous denoising process through VMD, effectively isolating pertinent signal characteristics from noise and measurement interference. Following this, fractal theory is utilized to conduct an in-depth qualitative analysis of the denoised data, capturing intricate patterns within the deformation trends. The model further evolves with the application of WOA to optimize the DELM model, thereby facilitating an integrated approach that merges qualitative insights with quantitative analysis. The efficacy of this advanced model is demonstrated through a case study, highlighting its capability to deliver accurate and reliable predictions that are in harmony with practical engineering scenarios. This research not only offers a robust framework for analyzing dam deformation trends but also sets a new standard in the field, providing a new solution for assessing structural integrity in hydrological engineering.