Structural Control & Health Monitoring最新文献

In addressing the problems of delayed detection, inefficient identification, and coverage blind spots in optical fiber-based pipeline leakage detection within pipe galleries, this study proposes a leakage detection strategy utilizing distributed optical fiber temperature measurement technology. Finite element method is employed to analyze the temperature influence radius around the pipeline leakage hole and a model test is conducted as a validation. The results show that the temperature field image at the leakage site is elliptical, influenced by temperature differences between ambient and liquid. An increase in this temperature difference accelerates changes in the temperature field’s range. Adjustments to the optical fiber’s winding angle and pitch demonstrated that an optimal pitch is 1/24 of the pipeline’s length, with a 45° winding angle. This configuration maximizes the optical fiber’s distribution in detection while maintaining its cost-effectiveness. When the leakage site is constant, and only the winding mode is altered, it is observed that when the ambient temperature exceeds the liquid temperature in the pipeline, the temperature of the escaping liquid impacts the temperature-measuring fiber due to gravity, registering approximately 2°C higher than the temperature measured directly at the leakage site. The temperature anomaly from field diffusion is significantly less than that caused by the water flow from the leakage impacting the fiber due to gravity. Conversely, when the ambient temperature is lower than the pipeline’s liquid temperature, the opposite occurs. These research findings offer a novel approach for distributed detection in water supply and drainage pipeline leakage.

Recent destructions of structures due to insufficient isolator deformation capacity have led to demands for greater seismic redundancy in seismic isolation design. For a friction pendulum system (FPS), the effect of bidirectional behavior of earthquakes on the maximum response and its effect on friction heating, temperature, and in turn on the maximum response can be significant. However, the extent of these effects under different FPS design parameters and different types of ground motions (GMs) is still not clear. In this study, an analytical model of double concave FPS considering the coupling effect of friction heating and bidirectional behavior was proposed and validated by bidirectional earthquake response orbits, which reflect the characteristics of both GMs and FPSs. Then, the effects of bidirectional GM and corresponding bidirectional temperature change on the response were investigated under different types of strong GMs. Finally, a performance-based design method with a bidirectional-effect-compensation mechanism was proposed. For double concave friction pendulum bearings with PTFE-related layers, it was found that the bidirectional behavior of earthquakes will amplify the maximum isolator displacement by an average of 110–210% (60 MPa) and the maximum superstructure acceleration by an average of 100–140% (60 MPa) under strong GMs (PGV-C1 > 0.2 m/s) and optimum design parameters. The amplification ratio is not only influenced by GM characteristics but also highly related to the design parameters and friction-heating effect of DCFPS.

For vision-based structural displacement measurement and health monitoring, the scale factor (SF) calculation plays a pivotal role in converting the pixel displacement into the actual one. On the other hand, for the current SF calculation methods, the object distance, between the to-be-measured points on the object plane and the optical center of the shooting instrument, has to be measured in advance due to the existence of the pitch and horizontal angle under off-axis measurement. Unfortunately, it is usually inefficient, difficult, and even impossible to obtain the object distances for all the to-be-measured points, especially for the full-field displacement monitoring on huge scale structures. In this paper, a novel SF calculation method was proposed to calculate the full-field scale factor of the host structure under off-axis measurement by combining with the similarity relation of the camera imaging model. With the help of this method, all the scale factors of the to-be-measured points can be calculated accurately and highly efficiently, as long as the object distance of any one point or the geometric dimensions of any feature object on the object surface is provided. In addition, to quantitatively assess the SF calculation accuracy of the method, a static vision-based measurement investigation was firstly conducted, and then, two experimental investigations about the multipoint and full-field structural displacement measurement on a cable-stayed bridge model and a simply supported beam model were conducted to validate the effectiveness and feasibility of the proposed method. Finally, all the results demonstrated that the proposed method exhibits an excellent performance on the SF calculation under off-axis measurement and provides a great potential to be utilized for the vision-based structural multipoint and full-field displacement measurement and health monitoring.

The presence of missing values is common in real-world datasets, so modeling and uncertainty quantification (UQ) of incomplete datasets have gained increasing attention in various research areas, including structural health monitoring (SHM). However, modeling and UQ utilizing incomplete datasets are nontrivial tasks. On the other hand, prediction based on a set of incomplete measured input variables is also an important task, but most existing methods, which are discriminative models, do not possess this capability. Aiming to tackle these two challenges, we propose the two-stage analytical Bayesian copula-based uncertainty quantification (A-BASIC-UQ) using incomplete SHM data. In the modeling stage, the copula-based multivariate joint probability density function (PDF) is modeled directly according to an incomplete dataset without imputation or disposal of any data points. For the univariate marginal PDF, using the measured (nonmissing) values of the corresponding random variable (RV), Bayesian model class selection is conducted to select the most suitable model class. For the Gaussian copula PDF, using the bivariate complete data points of entry-by-entry pairwise data, the optimal parameter vector is obtained from the estimation of the Pearson correlation coefficient. In the prediction stage, the analytical expressions of the predictive PDF, the predicted value and the credible region of the output variables are derived according to a set of incomplete measured input variables. The analytical expression of the predictive PDF is obtained based on the analytical operations on the auxiliary RVs and that of the predicted value and the credible region are obtained based on the analysis of multivariate Gaussian distribution. Therefore, the proposed method does not require numerical integration nor Monte Carlo simulation and does not suffer from computational burden even when there are many variables (say 4 or above). Examples using simulated data and real SHM data are presented to illustrate the capability of the proposed A-BASIC-UQ.

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.