Structural Control & Health Monitoring最新文献

{"title":"Phase-Based Real-Time Vibration Monitoring and Cable Force Estimation by Computer Vision","authors":"Feng-Liang Zhang,&nbsp;Junting Xu,&nbsp;Jian Zhou,&nbsp;Qingrui Yue,&nbsp;M. A. R. Hamid,&nbsp;Yan-Chun Ni,&nbsp;Zhongqi Shi","doi":"10.1155/stc/6330970","DOIUrl":"https://doi.org/10.1155/stc/6330970","url":null,"abstract":"<p>Cables are critical load-bearing components of cable-stayed bridges, and their force condition is essential for bridge safety and serviceability. Traditional contact-based cable force monitoring methods rely on dense sensor deployment, which faces challenges in reliability, durability, and maintenance. Therefore, computer vision (CV)-based noncontact cable force monitoring has become an important research direction. Although existing vision-based methods can achieve sufficient accuracy for microvibration measurement, their real-time applicability remains limited. This paper proposes a full-process phase-based real-time algorithm for cable vibration estimation and cable force identification. The proposed method uses a phase-based direct method (PDM) to optimize adaptive high-confidence regions and achieve subpixel motion tracking. A loopback constraint strategy is further introduced to effectively suppress cumulative drift errors during long-term tracking. Tests on simulated and handheld-captured videos demonstrate the reliability of the proposed framework. The method achieves accurate frequency estimation at extended distances, with minimum detectable amplitudes of 0.1–0.2 mm at 25 m, 0.3–0.8 mm at 50 m, and 1.0–1.8 mm at 100 m. In practical applications, the cable force identification errors are below 5% for most cables. Moreover, the single-frame processing time remains below 28 ms with a moderate window size, satisfying the real-time requirement of 30 FPS.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6330970","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synchro-Reassigning Transform: A Novel Approach to Earthquake Damage Identification in Structures","authors":"Roshan Kumar,&nbsp;Wei Zhao,&nbsp;Vikash Singh,&nbsp;Sudhakar Tummala,&nbsp;Shuai Kang,&nbsp;Surej Mouli","doi":"10.1155/stc/1790224","DOIUrl":"https://doi.org/10.1155/stc/1790224","url":null,"abstract":"<p>Structures may lose rigidity and exhibit a reduction in their fundamental natural frequency following a strong earthquake. Analysing the time–frequency representation of instrumented structure’s responses reveals key information about the changes in their frequency characteristics over time. The short-time Fourier transform (STFT), Wigner–Ville distribution, reassigned smoothed pseudo-Wigner–Ville distribution, wavelet transform, and Hilbert–Huang transform are among the well-known time–frequency techniques that are commonly employed for building damage detection. This is the first application of synchro-reassigning transform (SRT) for improved frequency-shift detection of structural damage after an earthquake, with greater robustness and resolution compared to other time–frequency transforms. This study thoroughly examines its performance by analysing two synthetic signals and three real earthquake signals: two from the 1994 Northridge earthquake and one from the 1971 San Fernando earthquake. The simulation results show that the SRT method provides improved time–frequency resolution and fewer distortions and is less sensitive to noise compared to well-established time–frequency techniques. Owing to these advantages, SRT could be more effective and promising in detecting frequency shifts for postearthquake structural assessment.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1790224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brake Fault Diagnosis Through Tree-Based Classifiers and Combination of Features","authors":"Vaishnavi Tripuraribhatla,&nbsp;Naveen Venkatesh Sridharan,&nbsp;Jegadeeshwaran Rakkiyannan,&nbsp;Sugumaran Vaithiyanathan","doi":"10.1155/stc/5519373","DOIUrl":"https://doi.org/10.1155/stc/5519373","url":null,"abstract":"<p>In the rapidly advancing world of automotive technology, ensuring safety and reliability is paramount. A vehicle’s braking system is crucial for preventing accidents and mechanical failures. Monitoring the braking system performance is vital, as any degradation can have severe consequences. This study focuses on identifying key features to evaluate braking system performance. Vibration signals were analyzed under various conditions including air in the brake fluid, mechanical fade, reservoir leak, and different brake pad wear states. The feature extraction process used statistical, histogram, and autoregressive moving average (ARMA) techniques. These features were then selected using the J48 decision tree to determine their significance. Each feature type was independently classified to assess its efficacy, achieving classification accuracies of 98.00%, 99.00%, and 97.00% for statistical, histogram, and ARMA features, respectively, using the random forest (RF) classifier. To improve accuracy, combinations of selected features were tested with histogram and statistical, ARMA and histogram, ARMA and statistical and histogram, and ARMA and statistical. The histogram and statistical features and the combination of all three features produced a classification accuracy of 100.00% with the RF classifier. Histogram and statistical feature combination was deemed most reliable, balancing high accuracy with reduced information and time complexity.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5519373","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Intelligent Decision-Support Framework Using Linguistic Assessment and Probabilistic Neural Networks for Rapid Prioritization of Urban Building Stocks","authors":"Burak Oz,&nbsp;Memduh Karalar","doi":"10.1155/stc/1127824","DOIUrl":"https://doi.org/10.1155/stc/1127824","url":null,"abstract":"<p>Rapid and reliable tools are essential for urban regeneration and disaster risk mitigation in earthquake-prone regions. This study proposes a hybrid decision-support framework that integrates linguistic expert assessments with a probabilistic neural network (PNN) for rapid prioritization of urban building stocks. Eleven seismic vulnerability parameters were evaluated using a five-level linguistic scale and applied to 194 buildings affected by the 2023 Kahramanmaraş earthquake in Türkiye. The model achieved 100% training accuracy and 95% testing accuracy, with misclassifications limited to adjacent damage classes. The proposed framework can support municipalities in rapidly identifying high-risk buildings during urban regeneration planning. PCA-based visualization confirmed clear class separation and consistent generalization. A post hoc sensitivity analysis further quantified decision-boundary uncertainty, showing that near-boundary transitions were concentrated in a small number of influential parameters. Class-level stability indices indicated higher robustness for severe-damage predictions. Overall, the proposed framework offers a practical and operationally efficient tool for first-level seismic vulnerability screening and evidence-based urban regeneration prioritization.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1127824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multisensor Data Fusion Framework for Real-Time Profile Estimation of Large-Diameter Shield Tunnels","authors":"Kun Zeng,&nbsp;Yusheng Zhu,&nbsp;Yuliang Zhou,&nbsp;Qifeng Yang,&nbsp;Genji Tang","doi":"10.1155/stc/4511210","DOIUrl":"https://doi.org/10.1155/stc/4511210","url":null,"abstract":"<p>Ensuring the quality and safety of large-diameter tunnels requires accurate, real-time deformation monitoring. However, existing methods often lack the precision needed for larger shield tunnel structures, are difficult to implement during construction, and rarely provide real-time data. This study presents a novel approach for estimating the deformation profile of large-diameter shield tunnels using sensor technology. Sensors are installed immediately after tunnel segment assembly to monitor key parameters, specifically segment rotation and seam openings. A finite element method (FEM) model establishes a theoretical relationship between these measurements and the overall tunnel profile, allowing the actual deformation to be estimated based on real-time sensor data. To enhance the robustness of the results and reduce errors from individual sensors, a Kalman filter is employed to fuse the two estimated profiles. The study’s findings indicate that deformations estimated from segment rotation were less severe than those derived from seam openings. The Kalman filter–based fusion reduced the influence of individual sensor-derived errors and provided more stable tunnel profile estimates. Compared with total-station measurements, the fused profiles achieved MAE values of 2.07–3.30 mm and RMSE values of 2.70–4.16 mm during the validation period, with average MAE and RMSE of 2.70 and 3.41 mm, respectively. These results demonstrate the feasibility of the proposed framework for real-time deformation monitoring of large-diameter shield tunnels.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4511210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Track Inspection Data-Driven Identification and Localization of Interlayer Debonding in Slab Tracks Using a Tile-Coding Neural Network","authors":"Xiao-Zhou Liu,&nbsp;Liu-Yan Zhu,&nbsp;Tian-Ci Gao,&nbsp;Zai-Wei Li","doi":"10.1155/stc/9952249","DOIUrl":"https://doi.org/10.1155/stc/9952249","url":null,"abstract":"<p>Interlayer damage is a critical issue affecting the structural integrity and service performance of high-speed railway slab tracks. Traditional track inspection data-driven identification methods often rely on manual feature extraction and are limited by insufficient utilization of temporal and multisource inspection data, resulting in limited accuracy and robustness. To address these challenges, this study proposes a tile-coding neural network (TC-NN) for the identification and localization of interlayer debonding. The model integrates tile coding with a convolutional neural network to automatically extract spatiotemporal features from multiperiod track inspection data while enabling the fusion of track geometry and car-body vibration measurements. Comprehensive experiments using real-world data from two high-speed railway lines demonstrate that the TC-NN achieves an identification accuracy of 87.60% using only track irregularity data, and further improves to 89.09% when synchronized multisource inputs are used. The acceleration-only model shows limited standalone discriminability, whereas the shuffled-acceleration control experiment indicates that the synchronized relationship between track geometry and car-body dynamic response provides auxiliary information for the fused model. With the optimized tile-coding configuration, the proposed framework supports defect localization along the track, achieving a localization recall of 96.00% on a continuous section under a ± 6.45 m tolerance, approximately corresponding to one standard CRTS-II slab length. These results suggest that the TC-NN provides a geometry-dominant and acceleration-assisted framework for engineering-oriented screening of interlayer debonding in slab tracks.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/9952249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Tuned Mass Amplified Inertance Damper for Vibration Control of Building Structures","authors":"Lili Zhou,&nbsp;Yung-Tsang Chen,&nbsp;Chien-Liang Lee,&nbsp;Jing Bie,&nbsp;Byung Gyoo Kang","doi":"10.1155/stc/4229601","DOIUrl":"https://doi.org/10.1155/stc/4229601","url":null,"abstract":"<p>In vibration control of building structures, the application of classical tuned mass dampers (TMDs) often faces challenges, particularly the need for a large mass to be effective. While alternative devices like the tuned mass damper inerter (TMDI) offer improved performance over the TMD with a reduced physical mass, existing TMDI configurations generally require the inerter to be either grounded or spanned across several stories, hindering their practical implementation. To overcome these limitations, a novel tuned mass amplified inertance damper (TMAID) is proposed, integrating a TMD with an inerter and a lever amplification device. The TMAID is installed on the penultimate floor, with its lever connected to the top floor. This configuration avoids grounding or spanning the inerter, while the lever mechanism enhances performance by amplifying the relative displacement across it. A single-story building equipped with a TMAID is first examined, and closed-form solutions are derived for this case. The model is subsequently extended to multistory buildings. Results from a simple three-story structural model indicate that, when designed optimally with proper leverage amplification, the TMAID performs comparably to the grounded TMDI but without the need to connect the inerter to the ground. Results from a ten-story structure show that the TMAID delivers seismic performance comparable to a classical TMD (1% mass ratio) but with only a fraction of the physical mass (0.1% mass ratio). By removing the constraints associated with the TMDI, the proposed TMAID offers a high-performance, lightweight, and readily implementable alternative for mitigating vibrations in building structures.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4229601","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Digital Twin With Material Characterisation and LLM as High-Level Orchestrator via Few-Shot In-Context Learning for Closed-Loop Control in Robotic Adaptive 3DCP","authors":"Giancarlo Di Marco","doi":"10.1155/stc/8514242","DOIUrl":"https://doi.org/10.1155/stc/8514242","url":null,"abstract":"<p>The industrialisation of 3D concrete printing (3DCP) is impeded by the lack of universal standards, a situation exacerbated by the process’s sensitivity to multiple interdependent parameters and environmental conditions, which can introduce structural defects and performance inconsistencies. Starting from the definition of 3DCP as a cyberphysical production system (CPPS), this exploratory study proposes adopting a hybrid standardisation method that optimises structural performance, or concrete grade. By following the DSR/ASTM V&amp;V research framework and based on the preliminary characterisation of materials, a computational digital twin is implemented, including a compressive strength predictive model connected to the simulation of the adaptive 3DCP process. Accounting for the anisotropic behaviour of 3D-printed concrete (3DPC), the predictive model determines the 3DPC grade based on preliminary material characterisation results and considering different process factors that determine the mechanical performance: By optimising process parameters, the model maximises parallel compression strength, prioritising safety. Uncertainty propagation is embedded in the computational model, enabling statistical validation through extensive virtual testing. The optimised configuration of process parameters will serve as a benchmark for the adaptive 3DCP process, in which a pretrained large language model acts as a high-level orchestrator for closed-loop control via few-shot in-context learning. The proposed architecture relies on the performance predictive model, sensor readings, and an LLM that interprets the process status and suggests corrective actions. The primary novel contribution of this work is a computational performance predictive model with uncertainty propagation; the adaptive 3DCP is described as completing the digital twin definition and demonstrating its use in a production setting. Starting from preliminary materials characterisation and considering the specificities of our robotic cell and environmental conditions, the performance predictive model reduced the variance in parallel structural strength by 25%, ensuring a 90% confidence level in achieving the M20 grade, with a significant reduction in anisotropic behaviour. Additionally, while maximising 3DPC performance, the digital twin enables benchmarking of production parameters across any production setting, making it a valid tool for standardising the technology. This research proposes a critical transition from deterministic to flexible standardisation, and from training-for-control to a more versatile prompting-for-control AI methodology, offering a viable, data-efficient pathway towards standardised, reliable, and industrial-scale 3DCP.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8514242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A ResNet–TadGAN Hybrid Framework for Robust Detection of Imbalanced and Subtle Anomalies in Structural Health Monitoring","authors":"Mohammad Hossein Abbasi,&nbsp;Fatemeh A. Mehrabadi,&nbsp;Maryam Bitaraf","doi":"10.1155/stc/3434617","DOIUrl":"https://doi.org/10.1155/stc/3434617","url":null,"abstract":"<p>Addressing anomalies caused by sensor malfunctions, environmental influences, and operational variability is crucial for developing a reliable structural health monitoring (SHM) system. This study proposes a hybrid methodology that integrates a ResNet-18 architecture with a time-series anomaly detection generative adversarial network (TadGAN) to detect six types of imbalanced anomalies, namely, missing, minor, outlier, square, trend, and drift, in a cable-stayed bridge. After preprocessing, the acceleration response signals are converted into grayscale images and fed into ResNet-18 for initial classification. Segments, classified as normal, are subsequently re-evaluated to detect potential misclassified anomalies. To this end, modification filters, including TadGAN, are applied to refine the detection process. The framework is validated using long-term acceleration data from the Sutong Bridge, one of the world’s longest cable-stayed bridges, achieving an overall detection accuracy of 98.1% and 90.94% <i>F</i>1-score for drift anomalies, substantially outperforming previous studies. The results demonstrate that the proposed approach is particularly effective in identifying subtle anomaly types, such as drift, and highlight its potential as a robust and scalable tool for real-time SHM and improved structural condition assessment.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3434617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Structural Health Assessment of a Cascade Dam Group Utilizing Monitoring Data and Ordered Entropy","authors":"Jiayi Wang,&nbsp;Qiming Zhong,&nbsp;Wolfgang Nowak,&nbsp;Denghua Li,&nbsp;Hao Gu,&nbsp;Yibo Shan","doi":"10.1155/stc/4274587","DOIUrl":"https://doi.org/10.1155/stc/4274587","url":null,"abstract":"<p>The safe operation of cascade dam groups is critical to water resource management and disaster prevention. However, existing methods are ill-suited for real-time structural health monitoring (SHM) and system-level health assessment due to their static nature and inability to model dynamic interdependencies. To bridge this gap, this study proposes ordered entropy as a novel, systems-theoretic metric that quantifies the dynamic health state of an entire dam group. We develop an integrated, monitoring-data-to-health-assessment framework that systematically transforms raw monitoring data through robust preprocessing (Apriori–DBSCAN), spatial field reconstruction (Co-Kriging), and multisource information fusion (order degree with PCA). By integrating the maximum entropy principle and extreme value theory with the derived entropy sequence, we establish a novel method for estimating the real-time failure probability as a health state quantification output that serves SHM of hydraulic systems. Applied to the Y River cascade dam group, the framework yields a service-state failure probability of 1.33 × 10⁻⁶–1.47 × 10⁻⁶ per year, demonstrating its rationality and practicality, with the temporal variation of failure probability showing a dominant correlation with reservoir water level—consistent with engineering mechanics principles and operational observations. This work provides both a new theoretical paradigm and a practical toolchain for advancing data-driven SHM, prognostic health management, and operation state awareness of hydraulic infrastructures.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4274587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}