Structural Control and Health Monitoring
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Acceptance rate34%
Submission to final decision117 days
Acceptance to publication21 days
CiteScore9.200
Journal Citation Indicator1.160
Impact Factor5.4

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 Journal profile

Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. 

 Editor spotlight

Chief Editor, Professor Lucia Faravelli, is based at Zhejiang University, China. Her research interests include structural reliability, stochastic mechanics, and structural control.

 Society information

Structural Control and Health Monitoring is the official journal of the International Association for Structural Control and Monitoring and the European Association for the Control of Structures.

Latest Articles

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Research Article

Gust Factor Approach for Estimating Maximum Response and Control Force in High-Rise Base-Isolated Buildings with Active Structural Control

This paper devises a new method for estimating the maximum response and maximum control force for high-rise base-isolated buildings with active structural control (active base isolation) to simplify the conventional complex design procedure. While active base isolation has emerged as a prominent solution for achieving high control performance, its design process is inherently complex, particularly when applied to high-rise buildings where wind loads become prominent. To address this problem, we propose a streamlined method inspired by the gust factor methodology widely used in conventional passive wind-resistant designs. This method estimates the maximum response and maximum control force without the need for numerical simulations. We first construct an equivalent passive model of a multi-degree-of-freedom control system to theoretically compute the dynamics of the system. Based on the constructed equivalent passive model and then propose a method to calculate the mean displacement and mean control force using only the static equilibrium of this model. Furthermore, we extend the conventional gust factor approach to active base isolation to estimate the maximum displacement and maximum control force for active base isolation without the need for numerical simulations. We validate our methods through a series of numerical examples, incorporating key parameters such as feedback gain, aspect ratio of building, return period of wind force, and stiffness of isolation. Numerical verifications show that the mean response and mean control force are estimated by the static equilibrium of the proposed equivalent passive model. Moreover, the maximum response and maximum control force can be estimated by the proposed gust factors. Our methods can be applied for feedback control systems using a given feedback gain.

Research Article

Fast Force Estimation of Cable Structures Using Smartphone-Captured Video and Template Matching Algorithm

Cables are important components of long-span bridge structures, whose operation is significantly affected by cable force changes. Nowadays, cable force testing is performed by physical methods; that is, sensors are installed on the cable structure to monitor its force changes. Obviously, this strategy requires an extensive amount of time to achieve cable force calculation, which makes it impossible to monitor the force of the cable structure in real time. Meanwhile, smartphones have attracted extensive attention in the field of structural health monitoring (SHM) because of their higher cost-effectiveness than accelerometers, which include price and lifespan. Besides, many people own a smartphone, which leads to the possibility of a wider range of applications. Therefore, this paper presents a framework for the rapid estimation of the cable force of long-span bridges based on smartphones-captured video and a template matching algorithm. First, the empirical mode decomposition (EMD) method with wavelet decomposition (WD) method, that is, the EMDWD model, is constructed to extract the vibration signal of the bridge cable by eliminating the effects of smartphone vibration and environmental noise on the measured dynamic displacement, thus effectively improving the accuracy of data processing. In addition, the vibration identification model of bridge cable based on a template matching algorithm is established, and the deformation curve of cable is obtained. Finally, the frequency of bridge suspender is calculated by the Fourier transform method (FFT), and the cable force is estimated based on the smartphone-captured video.

Research Article

Fault Detection of In-Service Bridge Expansion Joint Based on Voiceprint Recognition

Bridge expansion joints (BEJs) in service are susceptible to damage from various factors such as fatigue, impact, and environmental conditions. While visual inspection is the most common approach for inspecting BEJs, it is subjective and labor-intensive. In this paper, we propose a novel methodology for detecting the fault status of BEJs, inspired by voiceprint recognition (VPR) based on audio signals. We establish an Artificial Neural Network to filter nonevent segments from low signal-to-noise ratio signals, achieving an AuC value of 0.981. We design and improve ConFormer VPR models with a multifeature aggregation strategy and cascade them to realize fault detection of BEJs. For three successive tasks in classifying environment sound types, vehicle impact types, and faults, the ConFormer VPR models achieve AuC values of 0.975, 0.925, and 0.886, respectively, demonstrating the feasibility of our methods for unmanned inspection of BEJs. In future research, the introduction of multiple types of damage and the implementation of benchmarking tests are planned to further enhance the capabilities of the system.

Research Article

A Novel SMFL-Based Assessment Method for Corrosion Nonuniformity of Rebar and Its Application in Reliability Analysis of Corroded RC Beam

The reliability of corroded reinforced concrete (RC) structures relies on the accurate minimum cross-sectional area of corroded rebar. In this study, the accurate morphologies and self-magnetic flux leakage (SMFL) field strengths of twenty-eight non-uniformly corroded rebars were obtained using 3D structural light scanning and micromagnetic detection technologies, based on which three indices of the SMFL field variation ratio dH, the corrosion non-uniformity degree , and the cross-sectional area ratio K0.25 are proposed. The statistical results show that the probability densities of and K0.25 obey the Weibull distribution and Gamma distribution at the 95% confidence level, respectively, and their distribution parameters are linearly or inversely proportional to dH. The probability density distribution of the minimum cross-sectional area of corroded rebar can be determined using indices and K0.25, based on which a feasible SMFL-based reliability assessment method of corroded RC structures is proposed. The case study of a real specific corroded RC beam shows that the reliability assessment error of the SMFL-based method is only 1.2%, which is much lower than the 20.7% error of the existing method. This SMFL-based method provides a novel idea that can automatically and accurately assess the effect of rebars’ corrosion non-uniformity on the reliability of specific in-serviceRC structures.

Research Article

Transformer-Enhanced Traffic Load Simulation for Wear Evaluation of Bridge Expansion Joint

Timely wear evaluation is crucial in maintaining the functionality of bridge expansion joints (BEJs), ultimately ensuring the safety of bridges. Despite the significance of traffic load simulation (TLS) in simulation-based evaluation methods, existing TLS approaches face challenges in accurately modeling in situ traffic flow at a high fidelity. This paper presents a novel methodology and its application for evaluating the wear performance of BEJs, employing a Transformer-enhanced TLS approach. Initially, a tailored dataset is crafted for data-driven car-following modeling, leveraging an established spatial-temporal traffic load monitoring system. High-fidelity TLS with a mean absolute error (MAE) of 0.1738 m/s is then achieved using Transformer modules equipped with an attention mechanism. To evaluate the final wear life of BEJs, transient dynamic analysis and a calibrated finite element model of the bridge are employed to extract cumulative displacement. Additionally, a surrogate model is developed to depict the relationship between the hourly traffic weight on the entire bridge deck and the cumulative displacement of BEJs, yielding an impressive R-squared value of 0.96619. Comparative results demonstrate the superior performance of our proposed TLS approach over other data-driven approaches, with the linear model derived from our TLS approach outperforming the model generated by the conventional Monte Carlo-based TLS approach. To conclude, our proposed TLS emerges as a comprehensive and precise methodology for the wear evaluation of BEJs.

Research Article

Identification Uncertainties of Bending Modes of an Onshore Wind Turbine for Vibration-Based Monitoring

This study considers the identification uncertainties of closely spaced bending modes of an operating onshore concrete-steel hybrid wind turbine tower. The knowledge gained contributes to making mode shapes applicable to wind turbine tower monitoring rather than just mode tracking. One reason is that closely spaced modes make it difficult to determine reliable mode shapes for them. For example, the well-known covariance-driven stochastic subspace identification (SSI-COV) yields complex mode shapes with multiple mean phases in the complex plane, which does not allow error-free transformation to the real space. In contrast, the Bayesian Operational Modal Analysis (BAYOMA) allows the determination of real mode shapes. The application of BAYOMA presents a further challenge when quantifying the associated uncertainties, as the typical assumption of a linear, time-invariant system is violated. Therefore, validity is not self-evident and a comprehensive investigation and comparison of results is required. It has already been shown in a previous study that the significant part of the uncertainty in the mode shapes corresponds to their orientation in the mode subspace (MSS). Despite all the challenges mentioned, there is still a great need to develop reliable monitoring parameters (MPs) for Structural Health Monitoring (SHM). This study contributes to this by analysing metrics for comparing mode shapes. In addition to the well-known Modal Assurance Criteria (MAC), the Second-Order MAC (S2MAC) is also used to eliminate the alignment uncertainty by comparing the mode shape with a MSS. In addition, the mode shape identification uncertainties of BAYOMA are also considered. Including uncertainties is also essential for the typically used natural frequencies and damping ratios, which can be more appropriately used if the identification uncertainty is known.

Structural Control and Health Monitoring
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
See full report
Acceptance rate34%
Submission to final decision117 days
Acceptance to publication21 days
CiteScore9.200
Journal Citation Indicator1.160
Impact Factor5.4
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