Structural Control & Health Monitoring最新文献

Long-span bridges face the significant challenge of deteriorating life cycles under fatigue loads. A new macroscopic damage mechanics model for rod hinge elements has been proposed to quantify the predamage of bridge beams subjected to high-cycle fatigue. This model introduces predamage variables to evaluate the damage evolution process prior to fatigue crack initiation, enabling the prediction of moderate deterioration in bridges that cannot be monitored during their service life. By comparing the fatigue test results and predamage simulation results of simply supported composite beams and continuous composite beams, it was found that the error between the model predictions and the test results is relatively small. This result confirms the reliability of the model. The predamage model has been implemented as a self-programming subroutine for numerical analysis. Taking the Daxi River Bridge as the engineering background, this predamage model was applied to practical engineering. Combined with typical traffic loads, a predamage assessment was conducted on its dangerous points. The dangerous beam segments of the bridge were taken and the damage values were calculated using a predamage subroutine model. The results obtained had a small error compared to the damage values of the corresponding beam segments in the full bridge simulation. The proposed high-cycle fatigue predamage subroutine model offers a valuable reference for predicting fatigue damage in bridges.

Pipeline damage in mission-critical systems, such as pipelines within naval ships, can result in substantial consequences. Compared to manual inspection of pipeline damage by crew members onboard, structural health monitoring of pipeline systems offers prompt identification of damage sites, enabling efficient damage mitigation. However, one challenge of this approach is deriving an optimal sensor placement (OSP) strategy, given the large and complex pipelines found in real-scale naval vessels. To address this issue, a search space reduction method is proposed for OSP suitable for the large and complex pipeline systems found in naval ships. In the proposed method, the original search space for sensor placement is reduced to a manageable scale using an explainable artificial intelligence (XAI) technique, namely, a gradient-weighted class activation map (Grad-CAM). Grad-CAM enables quantification and visualization of the contribution of individual pipeline nodes to classify damage scenarios. Noncritical sensor locations can be excluded from the candidate search space. Furthermore, a peak-finding algorithm is devised to select only a limited number of nodes with the highest Grad-CAM values; in this research, the algorithm is proven effective in reconstructing the search space. As a result, the original OSP problem—which has an extremely large search space—is reconstructed into a new OSP problem with a computationally manageable search space. The new OSP problem can be solved using either meta-heuristic methods or exhaustive search methods. The effectiveness of the proposed method is validated through a case study on a real-scale naval combat vessel, measuring 102 m in length and carrying a full load of 2300 tons. The results show that the proposed XAI-based search space reduction approach efficiently designs an optimal pipeline sensor network in real-scale naval combat vessels.

This paper proposes an innovative control method to reduce the seismic responses of nonlinear structures under the uncertainties of near- and far-field earthquakes. This method is crucial for controlling the seismic response and ensuring structural stability. For this purpose, the robust adaptive FOPID controller is combined with the interval Type 2 fuzzy neural network, whose parameters are optimized through the Levenberg–Marquardt algorithm. An MLP neural network trained using an error backpropagation algorithm is considered for structural system identification and plant estimation. The Jacobian of the estimated model is applied online to the controller. Also, an adaptive compensator, interval Type 2 fuzzy neural networks, is considered to increase the stability and robustness of the proposed controller against estimation error, seismic disturbances, and some unknown nonlinear functions. The extended Kalman filter with feedback error learning strategy is used to maintain the acceptable performance level in the compensator. The performance effectiveness of the proposed controller equipped with a compensator in reducing seismic responses was investigated on a 20-story benchmark building equipped with an active cable damper, and the evaluation criteria were compared with previous works. The results indicate that the IT2FNN-FOPID controller performs better than other controllers in mitigating the seismic responses of the structure during an earthquake and achieving the control objectives. Thus, the J₁ criterion in the El Centro earthquake with an intensity of 1.5 times has improved by about 70% of the ratio of the LQG controller, which is about 60% in the case of the Kobe earthquake.

When implementing structural model updating, whether the model is stochastic or deterministic, the ill-posed issue is a challenging problem. To effectively address this problem, this paper proposes a new static stochastic model updating method, which combines the homotopy method with the pre-estimation technique of solution domains of the updating quantities. Firstly, considering the uncertain static measurement displacements, the solution domains of updating factors in structural models such as bridges are derived in terms of the sensitivity of static strain energy. Then the homotopy method is used to transfer the stochastic static model updating equation into a series of deterministic recursive equations about the expansion coefficients of updating factors. Within the pre-estimated solution domains, the expansion coefficients of the updating factors can be solved by the L-curve method and the convex optimization. When the measurement positions do not contain the loading points, a model expansion strategy is provided. Two numerical examples demonstrate that the proposed method can offer stable updating results, which coincide very well with those assumed real values, in the cases of high-dimension and limited measurement points. And when the displacements at the loading points are not directly measured, compared with the Bayesian method with the finite element samples, the proposed method has higher computational efficiency with the equivalent accuracy. When updating a practical continuous box-girder bridge, the proposed method can efficiently update a large finite element model, and the statistics of updating results agree very well with those of the static measurement data.

Fluid viscous dampers (FVDs) in long-span bridges are prone to performance change, in which the gap effect caused by oil leakage and the parameter alteration induced by viscous material denaturation are two primary sources of change. These variations may negatively affect the safety of both the bridge and the damper, thus underlining the significance of performance assessment and abnormality detection. This study develops a Gap-Maxwell (G-M) model to simulate the restoring force characteristics of the FVD considering performance alteration and subsequently suggests identification methods for gap and parameter change to capture the condition variation of the damper. The G-M model contains a gap–hook element group and a Maxwell element, where the gap length of the gap element represents the leakage, and the parameter change is achieved by setting different parameter values for the Maxwell element. Its feasibility is verified by comparison with the cyclic test results. The simplified longitudinal movement pattern for the railway suspension bridge during the operational stage is suggested. Based on the G-M model and the movement pattern, the segmental gap identification (SGI) method is proposed to determine the gap length by segmenting the original data and identifying the gap in each segment. Numerical simulations illustrate its accuracy and robustness under different damper parameter settings and noise pollution. The G-M model parameter identification (GMPI) procedure is raised to capture the parameter change, which follows a procedure of preprocessing, clustering, fitting, and optimization. It is numerically proved to be effective in identifying the damping coefficient and velocity exponent of the FVD.

Steel structures are susceptible to fatigue cracking under cyclic loading, which can lead to catastrophic structural failure. In the incipient phase of crack propagation, the width of fatigue cracks typically measures less than 0.1 mm, making them difficult to detect using standard imaging techniques. This study presents a novel approach to crack detection on steel structures by tracking the displacement field on the structural surface derived from visual data. Initially, video or sequential images of the target structure under loading are captured and processed using an enhanced dense feature-matching model. The surface displacement field is then computed from the coordinate difference of the numerous matched feature points. By extracting discontinuities within the displacement field, fatigue cracks can be localized. Two case studies were conducted to validate the methodology: one involving a with a pre-existing crack and another steel plate with fatigue crack propagation. The findings indicate that the proposed method can be used to detect minuscule cracks, with crack widths as small as 5 μm. Factors potentially influcencing the method, including the texture of the steel surface, the region of interest (ROI) area ratio, the density of matching, and the resolution of input images, were discussed. Compared to traditional image-based semantic segmentation techniques, this approach is more convenient and precise, offering a promising avenue for the nondestructive evaluation of steel structures in civil engineering.

Steel–concrete composite structures are prevalent in civil engineering, however, due to the temperature variation and fatigue loading, the interface between the steel tube and the core concrete is susceptible to various types of damage, including cracking, delamination, and debonding. Accurate localization of interface damage is crucial to ensure the safety of steel–concrete composite structures. The time-reversal (TR) method is commonly used in nondestructive testing for localizing structural linear damage due to its temporal and spatial focusing characteristics. However, the damage in the steel-concrete interface exhibits complex mechanical behavior and results in localization errors with the traditional TR method. To address this challenge, combined with the advantages of the VAM method, this paper proposes a nonlinear ultrasonic TR method to improve the accuracy of the TR method. This novel approach involves simultaneously exciting low-frequency (LF) and high-frequency (HF) signals using only one lead zirconate titanate (PZT) transducer, extracting the first-order modulation sideband signal, reversing and reemitting this signal, and utilizing the focused signal image to determine the location of damage at the interface. To validate the effectiveness of the proposed method, experiments were conducted on a concrete-filled steel tube with prefabricated interface damage to check its localization accuracy. The results clearly demonstrate an improvement in localization accuracy when using the proposed method compared to the conventional TR method. Specifically, the relative error in the coordinates for damage determined by the conventional TR method was significantly reduced from (25.89%, 18.82%) to (3.53%, 7.06%) with the proposed method. These findings underscore the superior performance of the proposed nonlinear ultrasonic TR method in localizing damage at the steel-concrete interface.

Time series–related methods in structural damage detection have gained increasing recognition due to their effectiveness, yet they face limitations in accuracy and efficiency for data processing, particularly in damage localization. In this study, we propose a novel method that utilizes a mutative-scale symbolic matrix, which extracts the second-largest eigenvalue as a damage indicator, to address the difficult problems of damage detection under random excitation. Unlike the conventional symbolized time series method, the mutative-scale symbolic matrix method selects data from the virtual impulse response function series at specific intervals, based on the Pearson correlation coefficient, and uses these data with the intervals to construct the mutative-scale symbolic matrix through joint occurrence entropy. The second-largest eigenvalue of the matrix is identified as an effective damage indicator which significantly magnifies the variations in structural characteristics. Damage localization is achieved by exploring damage occurrence between different reference and measurement points, and the flexibility in selecting these points enables a more precise determination of the damaged area according to the technology process based on dichotomy. A 10-DOF numerical model subjected to random Gaussian white noise is initially employed to validate the accuracy of the damage indicator for damage identification and localization. Subsequently, upon experimental application to a testbed structure, the proposed method exhibited super robustness in data selection under different damage types, with higher computational efficiency than conventional methods.

This study was conducted to examine the vibration control performance of the active isolation platform (AIP) implemented on the cart table (CT) of a moving front opening unified pod (FOUP) transporting cart to prevent damage to fragile silicon wafers during transportation across different buildings in semiconductor fabs. Additionally, the equation of motion for the proposed AIP–cart system simulated by a full vehicle model under raised floor irregularities was derived. Moreover, the direct output feedback control algorithm was used to determine the optimal feedback gain matrix for calculating the active control forces of the AIP. Furthermore, the dynamic time histories of the proposed model under raised floor irregularities were analyzed by the discrete–time state–space procedure (SSP), and the numerical simulation results revealed that AIP effectively suppressed the bouncing (or vertical) acceleration with a reduction of > 90% at FOUP locations to 2.37 m/s² (< 9.81 m/s² or 1.0 g, the bouncing acceleration threshold) to prevent FOUPs (or fragile silicon wafers) from bouncing away from the CT without AIP, causing damages to the wafers via collisions. Moreover, AIP greatly reduced the pitching angular rotation with a reduction of > 65% to prevent the sliding of FOUP-stored wafers from the supporting slots inside FOUPs when the FOUP-transporting cart traversed through a larger bump between the expansion joints. The flexible AIP that demanded less control force (27.08 N) significantly isolated the high-frequency response transmitted from the CT and effectively enhanced its damping ratio to suppress the resonance low-frequency response induced by intermittent perforated floor irregularities or bumps. From a practical point of view, the proposed AIP scheme implemented on CT can be adopted for protecting jumping- or sliding-induced collision damages to wafers (or similar fragile products) transported by carts to reduce huge economic losses in industry.

Vibration-based damage detection relies on changes in structural dynamic features. However, environmental and operational variations (EOVs) can cause changes in dynamic features that mask those caused by damage. In addition, the EOV effects on dynamic features are often nonlinear, which limits the application of many linear damage detection methods. A novel nonlinear output-only method is proposed to address this. This method leverages variational mode decomposition (VMD) as a preprocessing step to remove seasonal patterns and noise from the modal frequencies. The first modes of the decomposition results (IMF1 signals) are then used to calculate the Euclidean distance based on the residual obtained by the principal component analysis (PCA) method. To eliminate the nonlinear EOV effects and provide normalized damage features for reliable continuous dynamic monitoring, a Gaussian process regression (GPR) model is trained to learn the underlying calculation rule of the PCA Euclidean distance. Due to the linear nature of PCA, the nonlinear EOV effects are still retained in both the PCA Euclidean distance and the GPR–predicted value. Through a subtraction process, their common nonlinear environmental effects can be removed, and the resulting prediction error can serve as a normalized feature sensitive to structural damage. The proposed method is validated through a simulated 7-DOF example and real data from the Z24 bridge, with several comparisons highlighting its effectiveness.