Structural Control & Health Monitoring最新文献

Wind turbines with larger capacities face bending deformation due to taller towers and longer blades, necessitating mitigation against extreme seismic loads. A vertically installed inerter-based damper, referred to as the tuned viscous mass damper (TVMD), is proposed alongside a closed-form design approach. First, the mechanical model and simulation approach for the TVMD and wind turbines are introduced, followed by the derivation of governing equations and frequency response solutions, considering the parked state. Second, a nacelle-hub assembly displacement–oriented design principle is formulated, providing mathematical design expressions and closed-form solutions based on the generalized fixed-point principle. Finally, the effectiveness of the proposed framework is validated through design cases and comparative investigation of theoretical approaches, under parked conditions with negligible aerodynamics and thus low effective damping, highlighting the advantages of the closed-form design formulas. The results indicate that the vertically installed TVMD offers superior performance compared to traditional damping design approaches in wind turbines, enabling the simultaneous control of multiple seismic responses. Furthermore, the nacelle-hub assembly displacement–oriented design principle and closed-form design formulas provide a quantitative framework for optimizing key design parameters of vertical TVMDs, facilitating rapid design implementation and deeper theoretical understanding. In addition, the proposed closed-form design formulas ensure enhanced energy dissipation and specific modal tuning capacity, offering robustness against parameter variations.

J. Lee, C. Lee, I. Yeo, and S. Jeong, “Multivision System for High-Resolution Strain Measurement of Continuously Welded Rail,” Structural Control and Health Monitoring 2025, no. 1 (2025): 1–16, https://doi.org/10.1155/stc/2447466.

In the article titled “Multivision System for High-Resolution Strain Measurement of Continuously Welded Rail,” there was an error in the funding grant code.

The correct funding statement should be as follows:

This research was supported by a grant from R&D Program (PK2501D4) of the Korea Railroad Research Institute.

We apologize for this error.

Under ambient excitations, the vibration response data of structures exhibit significant time-varying characteristics as time progresses. This time-varying data causes domain shift, which greatly hinders the application of neural networks in structural health monitoring (SHM). This paper proposes a one-dimensional spatiotemporal convolution-based domain adversarial network (SDAN) to address the issue of decreased damage identification (DI) accuracy in neural networks caused by the domain shift. In SDAN, to effectively utilize the spatial information from different sensors, we designed a one-dimensional spatiotemporal convolution that integrates temporal and spatial characteristics of the vibration response data. The spatiotemporal convolution proposed was advantageous for extracting fine-grained features with spatiotemporal characteristics to enhance the performance of the domain adversarial network. Domain adversarial training is then employed to extract domain-invariant features from the data, enabling the identification of damage features in structural response data under ambient excitations and improving the applicability of the network in time-varying data. The effectiveness of the proposed network is validated using vibration response data collected from two real-world bridges, old ADA bridge and KW51 bridge, under ambient excitations. The results show that SDAN significantly reduces the impact caused by the domain shift, achieving F1 scores of 95.8% and 99.6% on the old ADA bridge and KW51 bridge datasets, respectively. This represents an improvement of 21.2% and 12.1% compared to a network without domain adaptation (NoDA). Furthermore, SDAN was compared with a domain adaptation network based on global feature alignment using deep adaptation network (DAN) and a domain adaptation network based on subfeature alignment using deep subdomain adaptation network (DSAN). SDAN achieved the highest F1 scores on both examples, illustrating the effectiveness of domain adversarial training in addressing domain shift issues caused by time-varying ambient excitations. This provides a promising approach for utilizing ambient excitations in real-time structural DI.

BIM is increasingly crucial for the building construction, yet deviations from actual construction limit its construction monitoring applications. Therefore, this paper proposes a BIM-construction interaction method that integrates parametric modeling, point cloud processing, and parameter optimization and fitting to enhance the construction monitoring. Proposed parametric modeling methods equip BIM elements with the ability of the pose adjustment and deformation modification, addressing potential deviations during construction. Developed component feature extraction algorithms efficiently capture pose and deformation features from the point cloud model that sufficiently and accurately reflect the actual state of the structural components. The proposed parameter optimization and fitting approach targets model parameters for optimization and aims to match pose and deformation features for fitting. By constructing objective functions that quantify the deviation between the BIM and point cloud models, the process is driven by the RBFOpt optimization algorithm and Opossum optimization solver. This approach enables the automatic updating of the design BIM into the as-built BIM and generates deviation data between the two models, providing a basis for comprehensive construction monitoring results. The BIM-construction interaction method was applied to the core area construction of the Shanghai Grand Opera House, where it reduced the root mean square error (RMSE) between the parametric BIM and point cloud models of the core column, concrete thick shells, and cantilever beams from 0.0352 m, 0.0411 m, and 0.0323 m to 0.0082 m, 0.0323 m, and 0.0053 m, respectively, significantly reducing deviations between BIM and the actual construction. Comprehensive and quantitative construction monitoring data, including pose deviations and structural deformations, were obtained to assess the precision and safety of the core area construction. The results demonstrate that the BIM-construction interaction method effectively supports the interaction between BIM and construction, enabling monitoring and evaluation based on point cloud data.

Regular monitoring of cable forces is critical to ensuring the long-term safety and performance of cable-stayed bridges. While vision-based methods offer noncontact, cost-effective alternatives to traditional vibration-based methods, most existing studies adopt an offline workflow in which videos are recorded and processed afterward. This study develops an embedded vision-based sensing system for cable force monitoring. Unlike offline vision approaches, the system performs on-site video acquisition, processing, and force estimation on-site, enabling real-time monitoring without external video transfer. First, an efficient and accurate visual object tracking (VOT) algorithm is proposed for real-time displacement extraction from video sequences. We benchmark the algorithm’s accuracy and computational efficiency on a Jetson Orin Nano using a public shaking table test dataset. The results show that the algorithm achieves a good balance between accuracy and computational efficiency, making it suitable for deployment on edge computing devices. Subsequently, the cable vibration experiment indicates that the embedded vision-based sensing system achieves maximum errors of 2.61% in cable frequency measurement and 5.68% in cable force estimation. In addition, the camera position did not materially affect system accuracy. Future work will enhance robustness under diverse field conditions and validate the system on full-scale bridges.

Industrial machinery plays a vital role as essential mechanical equipment across industries, such as aviation, transportation, and smart manufacturing. However, these machines are prone to various failures caused by complex and dynamic operating conditions, which can disrupt entire industrial systems, lead to significant financial losses, and pose serious safety hazards. This emphasizes the importance of fault diagnosis in these machines to improve system reliability and safety. Recently, artificial intelligence (AI)–based techniques have gained significant attention due to their reliability, superior performance, and adaptability in diagnosing faults. However, a comprehensive review of recent advancements in intelligent fault diagnosis (IFD) is still lacking, and clear future research paths for further advancement are not well-defined. In addition, choosing the appropriate fault diagnosis methods for specific fault types remains a challenge. To address these gaps, this paper provides an in-depth review of the latest advancements in AI techniques applied to fault diagnosis in industrial machinery. The review paper starts by introducing the basic concepts of AI methods and then delves into a detailed examination of their applications in IFD for industrial machinery. In addition, the review discusses the strengths and weaknesses of different variants of AI methods, including traditional machine learning, deep learning, and transfer learning, within the field. Based on the review results, existing research challenges and prospects are discussed to guide future directions, followed by conclusions. Thus, this review serves as an essential resource for professionals, researchers, and stakeholders involved in the research field.

The displacement evolution of concrete dams serves as a key indicator of their structural safety. Establishing an accurate and reliable model for predicting displacement is essential for effective dam monitoring. Nevertheless, current multi-point forecasting approaches often overlook the interdependencies among deformation drivers and lack robust validation techniques to assess generalization capability and stability. This shortcoming hinders the accurate representation of deformation behavior under complex loading scenarios. To overcome these issues, this research introduces a spatiotemporal prediction model for concrete dams that combines hybrid clustering with adaptive decomposition and optimization strategies. Initially, the SOM-K-means method is employed to clusters monitoring points, followed by spatial correlation analysis to uncover interpoint relationships. Clustering performance is quantitatively evaluated using a composite assessment technique. During model development, hydrostatic pressures are derived through finite element simulation, and sensitivity analysis is applied to gauge the influence of environmental variables on deformation. Furthermore, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Mean Impact Value (MIV) techniques are employed to decompose and select deformation features. Tests show that the proposed model achieves superior predictive accuracy within clustered zones (R² > 0.98, compared to Transformer: 0.9673 and CNN-BiLSTM: 0.9501). Validation across multiple dam types further confirms the framework’s broad applicability and resilience. By incorporating spatiotemporal analysis, this method enables regionalized health monitoring and integrates data fusion under physical constraints, thereby significantly improving noise resistance and establishing a new benchmark for health prediction in high concrete dams.

This paper proposes a method for the evaluation of the combined system of heavy port cranes and the rails on which they run and demonstrates its success. Using techniques from railroad track health monitoring, we record the guided waves created in the rails from the movement of the wheels using laser-based vibrometry. In our novel approach, the signal is processed using discrete wavelet decomposition and dynamic wavelet fingerprints. This allows anomalies in the wheel or the rail to be found. The field measurements are verified using elastodynamic finite integration technique simulations. This methodology allows quick and safe evaluation without impacting cargo flow. We were able to identify tracks with corrugation damage.

Timely and efficient real-time surface damage detection is essential for maintaining the healthy operation of concrete bridges and has become a critical research focus. However, existing deep learning–based damage detection methods still face challenges such as low detection accuracy, poor adaptability, and limited applicability to diverse scenarios. To address these issues and enhance surface damage detection performance in complex environments, this study proposes an improved YOLODF model based on You Only Look Once, Version 5 (YOLOv5). The improvements include replacing the C3 module with the C2f structure with depthwise separable convolutions and inverted bottlenecks (DSIBC2f) module to build a new backbone network, DSIBCSPDarknet, which strengthens feature extraction capabilities. The SPPFCSPC structure is introduced to replace the spatial pyramid pooling fast (SPPF) module, enabling more effective multiscale feature fusion. Furthermore, the Enhanced Multidimensional Collaborative Attention (EMCA) is combined with the DSIBC2f module to construct a fused neck, FNeck, further optimizing feature fusion. Experimental results show that YOLODF significantly outperforms YOLOv5 in terms of precision, recall, F1 score, and mAP_0.5 and also surpasses the latest YOLOv12. Additionally, it demonstrates excellent damage detection capabilities in challenging scenarios, such as adverse weather, noise interference, and color variations. Despite a slight increase in computational load, YOLODF achieves a detection speed of 118 frames per second, demonstrating its high practicality for surface damage detection on bridges in complex environments.

This paper proposes a theoretical model correlating cable tension and frequency, incorporating the influence of intermediate transverse constraints. A theoretical vibration equation, considering these constraints, was derived to map the relationship between cable tension and frequency. Theoretical and numerical solutions for this equation were developed and validated. The impact of intermediate constraints on the cable tension–frequency relationship was subsequently analyzed. Results indicate that the theoretical numerical solutions provide accurate and efficient predictions for both single and multiple intermediate constraints, while the theoretical analytical solution is limited to single-constraint scenarios. Factors such as stiffness, position, and quantity of intermediate constraints significantly influenced the cable tension–frequency relationship, with these factors exhibiting coupled effects. At low constraint stiffness, the squared first-order frequency exhibited a linear correlation with cable tension, irrespective of constraint quantity or position. As stiffness increased, this relationship transitioned from linear to nonlinear, characterized by an initial convex upward curve before stabilizing into a linear segment for varying intermediate constraint configurations.