Structural Control & Health Monitoring最新文献_第3页

Unsupervised Multistate Anomaly Detection for Structural Health Monitoring Using Spatiotemporal Clustering 基于时空聚类的结构健康监测无监督多状态异常检测

IF 5 2区工程技术

Structural Control & Health Monitoring Pub Date : 2026-09-29 DOI: 10.1155/stc/7924571

Si-Yuan Sun, Zi-Xuan Zeng, Yan-Ke Tan, Yi-Qing Ni, Bin Yang, Qi-Lin Zhang

{"title":"Unsupervised Multistate Anomaly Detection for Structural Health Monitoring Using Spatiotemporal Clustering","authors":"Si-Yuan Sun, Zi-Xuan Zeng, Yan-Ke Tan, Yi-Qing Ni, Bin Yang, Qi-Lin Zhang","doi":"10.1155/stc/7924571","DOIUrl":"https://doi.org/10.1155/stc/7924571","url":null,"abstract":"Structural health monitoring (SHM) plays a critical role in ensuring the safety of major infrastructure, yet monitoring data inevitably encounters various anomalies, including structural damage, adverse environmental effects, and sensor faults. However, existing methods are typically designed for single anomaly types and either require prelabeled training data or depend heavily on expert knowledge to interpret and categorize detected anomalies. To address these limitations, this study proposes a spatiotemporal clustering-based anomaly detection algorithm to identify and interpret normal and anomalous structural states. The algorithm identifies multiple structural states without requiring prelabeled data or predefined anomaly metrics, given a user-specified number of clusters k. A method similar to expectation maximization (EM) is applied to compute the Gaussian inverse covariance matrix, capturing the spatiotemporal relationships of multisource monitored physical quantities and providing physically interpretable information to assist engineers in the preliminary identification of each structural state. The effectiveness of the algorithm is validated through a numerical example and further applied to the Shanghai Tower project. The results demonstrate that the algorithm not only detects anomalies effectively but also automatically discriminates between different structural states, with an inference time of less than one second enabling near-real-time anomaly detection.","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/7924571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146819","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}

引用次数: 0

Automated Crack and Feature Isolation in Heritage Masonry Using Sequential Geometric and Machine Learning Filters 使用顺序几何和机器学习滤波器的遗产砌体自动裂缝和特征隔离

IF 5 2区工程技术

Structural Control & Health Monitoring Pub Date : 2026-09-29 DOI: 10.1155/stc/6259403

Bara Alseid, Hyungjoon Seo, Ghazanfarah Hafeez

{"title":"Automated Crack and Feature Isolation in Heritage Masonry Using Sequential Geometric and Machine Learning Filters","authors":"Bara Alseid, Hyungjoon Seo, Ghazanfarah Hafeez","doi":"10.1155/stc/6259403","DOIUrl":"https://doi.org/10.1155/stc/6259403","url":null,"abstract":"This study presents a comprehensive evaluation of advanced multistage filtration workflows for automated crack detection and structural feature classification in dense 3D point clouds of historic masonry, using Liverpool Cathedral as a case study. The research systematically compares all logical filter sequences combining roughness, dip angle, and CANUPO machine learning classification, while implementing a novel, stagewise optimization of key parameters, including local neighbor radius (LNR) and dip angle thresholds. Unlike previous approaches that rely on fixed global values, this framework demonstrates that optimal filtration parameters are inherently context-dependent and must be dynamically adapted at each stage and for each targeted feature type. Six major workflow permutations were tested, and the dip angle–CANUPO–roughness (D–C–R) sequence consistently achieved the best compromise, preserving up to 36% of crack points while reducing bricks and joints to less than 10% of their original populations. Rigorous validation was performed on a large-scale, 100-million-point façade model, confirming both the scalability and the spatial precision of the optimized approach, with detected cracks aligning closely with ground-truth masonry imagery. The study also verifies that geometric filters alone, without a second CANUPO stage can deliver robust crack isolation, offering a viable solution for scenarios with limited annotated data or computational resources. Overall, this work establishes an empirical foundation for adaptive, multistage filtration workflows in heritage diagnostics and digital twin generation, providing practical guidelines for robust, automated defect detection in complex masonry structures.","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6259403","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146868","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}

引用次数: 0

Nondestructive Real-Time Monitoring of Cementitious Mortar Hydration and Fracture Behaviour Using an Embedded Passive Radio-Frequency Metamaterial Sensor 利用嵌入式无源射频超材料传感器对胶凝砂浆水化和断裂行为进行无损实时监测

IF 5 2区工程技术

Structural Control & Health Monitoring Pub Date : 2026-09-29 DOI: 10.1155/stc/8238877

Hamza Saeed, Tayfun Nesimoglu, Abdullah Ekinci, Cumali Sabah

{"title":"Nondestructive Real-Time Monitoring of Cementitious Mortar Hydration and Fracture Behaviour Using an Embedded Passive Radio-Frequency Metamaterial Sensor","authors":"Hamza Saeed, Tayfun Nesimoglu, Abdullah Ekinci, Cumali Sabah","doi":"10.1155/stc/8238877","DOIUrl":"https://doi.org/10.1155/stc/8238877","url":null,"abstract":"This study investigates the use of an embedded passive radio-frequency (RF) metamaterial sensor for real-time, nondestructive monitoring of cement mortar during early-age curing and uniaxial compression. The sensor was embedded centrally within a mortar cube and protected by a polyethylene terephthalate isolation layer, and then monitored with a calibrated vector network analyser (VNA) during a 7-day water-curing period, followed by mechanical loading. Key electromagnetic features, including resonance frequency (fr), quality factor (Q) and resonance-minimum magnitude (|S11,min|), showed time-dependent changes consistent with independently measured responses to setting, curing and mechanical loading. Logarithmic models were developed for Q(t) and |S11,min|, enabling the formulation of normalised indices for preliminary assessments, such as candidate demoulding readiness. During compression of the tested specimen, the RF response showed a gradual decrease in resonance depth within the elastic region, increased asymmetry during prepeak loading and significant broadening of the spectrum near peak stress. A tiered alert framework was implemented based on changes in ΔS magnitude and rate, providing advisory, warning and critical flags that aligned with mechanical events. The dominant resonance remained quantitatively trackable through peak and early postpeak loading, whereas the final fracture-stage response was interpreted qualitatively. These findings demonstrate the feasibility of using an embedded RF metamaterial resonator as a compact proof-of-concept approach for monitoring hydration-related dielectric evolution and load-associated spectral changes in cementitious materials.","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8238877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146924","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}

引用次数: 0

Multiview Consistent 3D Reconstruction of Concrete Defects via Automated Segmentation-Guided 2D Gaussian Splatting 基于自动分割引导的二维高斯飞溅的混凝土缺陷多视图一致三维重建

IF 5 2区工程技术

Structural Control & Health Monitoring Pub Date : 2026-09-28 DOI: 10.1155/stc/1642963

Dayou Duan, Zuocai Wang, Liang He

引用次数: 0

Buffeting Forecasting for a Long-Span Bridge Using Meteorological and on-Site Monitoring Data Based on Seq2Seq LSTM 基于Seq2Seq LSTM的大跨度桥梁抖振预报

IF 5 2区工程技术

Structural Control & Health Monitoring Pub Date : 2026-09-28 DOI: 10.1155/stc/7014618

Peng Liu, Wei Cui, Lin Zhao

{"title":"Buffeting Forecasting for a Long-Span Bridge Using Meteorological and on-Site Monitoring Data Based on Seq2Seq LSTM","authors":"Peng Liu, Wei Cui, Lin Zhao","doi":"10.1155/stc/7014618","DOIUrl":"https://doi.org/10.1155/stc/7014618","url":null,"abstract":"Buffeting of long-span bridges induced by strong winds has received attention recently due to traffic serviceability. An essential challenge is whether the bridge response can be estimated before the arrival of strong winds. In this work, a buffeting response early forecasting framework based on meteorological weather prediction is proposed. The initial input for the forecasting model is derived from the original low-time-resolution wind data at the meteorological station, as provided by the weather research and forecasting (WRF) system. High-time-resolution wind data at the bridge site are predicted using a combination of an LSTM neural network and a super-resolution method. Then, the buffeting response regression model is built based on the LSTM seq2seq method for different types of wind. The proposed framework introduces two key innovations. First, a mapping method is devised to connect large-scale wind speed prediction data with local wind conditions at the bridge site. Furthermore, a predictive model for the wind-induced response of bridges, incorporating a dropout layer, has been formulated to address various types of wind disasters. This established mapping model considers sequence-to-sequence relationships and estimated uncertainty. This integrated approach establishes a forecasting framework that spans from long-term future weather predictions to the prediction of wind-induced responses in significant bridge projects. The proposed method is of significant value for optimizing bridge operation and improving traffic control.","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/7014618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146521","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}

引用次数: 0

A Comparative Review of Robotic Structural Inspections for Civil Infrastructures: Platforms, Applications, and Future Challenges 民用基础设施机器人结构检测的比较综述：平台、应用和未来挑战

IF 5 2区工程技术

Structural Control & Health Monitoring Pub Date : 2026-09-24 DOI: 10.1155/stc/8468521

Zhifeng Xu, Jiakai Fan, Qiaoyun Wu, Hongping Zhu, Xuyong Chen

{"title":"A Comparative Review of Robotic Structural Inspections for Civil Infrastructures: Platforms, Applications, and Future Challenges","authors":"Zhifeng Xu, Jiakai Fan, Qiaoyun Wu, Hongping Zhu, Xuyong Chen","doi":"10.1155/stc/8468521","DOIUrl":"https://doi.org/10.1155/stc/8468521","url":null,"abstract":"The increasing complexity and safety demands of modern civil infrastructure have accelerated the adoption of robotic systems in structural inspection. Robotic platforms can improve the safety, accessibility, repeatability, and spatial consistency of inspection data acquisition; when inspections are repeated and spatially registered over time, the resulting datasets can further support condition comparison, deterioration trend analysis, and predictive maintenance. This paper presents a comparative review of robotic technologies for structural inspection of civil infrastructure, covering ground robots, climbing robots, marine robots, unmanned aerial vehicles, and multirobot systems. The review systematically classifies and compares these platforms across multiple dimensions: application domain, sensing modality, level of autonomy, endurance/payload, operational constraints, measurement accuracy, environmental sensitivity, field readiness, and deployment barriers. Quantitative examples of structural inspection data collected and acquisition methods are provided. Through synthesis of recent advances and five representative case studies, eight critical technological bottlenecks are identified: environmental adaptability, energy efficiency, autonomous navigation, multisensor fusion for long-term monitoring, real-time data transmission, multirobot collaboration, human–robot interaction, and digital twin (DT)-enabled structural inspection. Future research directions are outlined toward intelligent, autonomous, and resilient robotic structural inspection systems, with emphasis on DT integration, edge computing for on-board diagnosis, and standardized interfaces for structural inspection databases. This review offers a structured, comparative, and application-oriented reference for researchers and engineers implementing robotic structural inspections.","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8468521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048143","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}

引用次数: 0

Structural Health Monitoring in Jacket-Type Offshore Wind Turbines Through Generative Adversarial Networks: An Experimental Proof of Concept 基于生成对抗网络的导管式海上风力发电机结构健康监测：概念的实验验证

IF 5 2区工程技术

Structural Control & Health Monitoring Pub Date : 2026-09-23 DOI: 10.1155/stc/8854268

Ricardo Prieto-Galarza, Christian Tutivén, Yolanda Vidal

{"title":"Structural Health Monitoring in Jacket-Type Offshore Wind Turbines Through Generative Adversarial Networks: An Experimental Proof of Concept","authors":"Ricardo Prieto-Galarza, Christian Tutivén, Yolanda Vidal","doi":"10.1155/stc/8854268","DOIUrl":"https://doi.org/10.1155/stc/8854268","url":null,"abstract":"Early detection of damage in critical infrastructure, such as offshore wind turbines, enables predictive maintenance strategies that improve safety, reliability, and cost effectiveness. This work presents an experimental proof of concept for a damage detection approach for jacket-type supports in offshore wind turbines. The proposed methodology integrates generative adversarial networks and autoencoders with time-frequency feature extraction using the Wigner–Ville distribution. The approach is tested under varying excitation, using different types of damage at four different locations, and using vibration-only-response data. Experimental data is acquired from a laboratory downscale replica of an offshore jacket-type wind turbine under controlled conditions. The methodology requires only healthy data for development, extending its applicability by eliminating the need for damage data. The results show that the model effectively detects structural anomalies, reaching an average accuracy of 91.49%, with a recall of 87.25%, precision of 87.50%, F1-score of 87.37%, and specificity of 100%. Although the present study is limited to a controlled experimental setting, it provides a proof of concept for future validation in more realistic operational environments and for the further development of deep learning–based structural health monitoring strategies.","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8854268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048078","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}

引用次数: 0

Dynamic Deformation Prediction and Quantification of Long-Span Bridges Under Environmental Loads 环境荷载作用下大跨度桥梁动力变形预测与量化

IF 5 2区工程技术

Structural Control & Health Monitoring Pub Date : 2026-09-22 DOI: 10.1155/stc/2156454

Liangliang Hu, Xiaolin Meng, Xiangdong An, Yilin Xie, Yan Bao, Fan Zhang, Jinpei Li

{"title":"Dynamic Deformation Prediction and Quantification of Long-Span Bridges Under Environmental Loads","authors":"Liangliang Hu, Xiaolin Meng, Xiangdong An, Yilin Xie, Yan Bao, Fan Zhang, Jinpei Li","doi":"10.1155/stc/2156454","DOIUrl":"https://doi.org/10.1155/stc/2156454","url":null,"abstract":"Long-span bridges exhibit nonstationary deformation under simultaneous environmental loads, yet existing monitoring approaches commonly predict the overall structural response without adaptively separating load-associated components. This study develops a framework combining eXtreme Gradient Boosting (XGBoost) and optimal sparsity and reconstruction–based variational mode decomposition (OSR-VMD) for real-time deformation prediction and adaptive quantification of load-associated deformation components. XGBoost is first used to map multisource environmental observations to lateral and vertical bridge deformations. A physically interpretable OSR objective then evaluates within-mode bandwidth, mode-location separation, spectral overlap, and reconstruction fidelity, and a genetic algorithm adaptively selects the VMD parameters α and K. The framework is evaluated using 10 months of heterogeneous monitoring data from the Forth Road Bridge, UK. The results show that XGBoost reliably reproduces the measured deformation patterns, while OSR-VMD separates the predicted responses into frequency-specific modes associated with wind and temperature variations. By integrating nonlinear prediction with adaptive time-frequency decomposition, the framework provides an interpretable approach for identifying dominant environmental–load associations in bridge deformation and supports structural condition assessment under coupled environmental forcing.","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2156454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048135","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}

引用次数: 0

MPCA-Based Domain Adaptation for Transfer Learning in Ultrasonic Guided Waves 基于mpca的超声导波迁移学习领域自适应

IF 5 2区工程技术

Structural Control & Health Monitoring Pub Date : 2026-09-22 DOI: 10.1155/stc/4747489

Lucio Pinello, Francesco Cadini, Luca Lomazzi

{"title":"MPCA-Based Domain Adaptation for Transfer Learning in Ultrasonic Guided Waves","authors":"Lucio Pinello, Francesco Cadini, Luca Lomazzi","doi":"10.1155/stc/4747489","DOIUrl":"https://doi.org/10.1155/stc/4747489","url":null,"abstract":"Ultrasonic guided waves (UGWs) represent a promising diagnostic tool for structural health monitoring (SHM) in thin-walled structures, and their integration with machine learning (ML) algorithms is increasingly being adopted to enable real-time monitoring capabilities. However, the large-scale deployment of UGW-based ML methods is constrained by data scarcity and limited generalisation across different materials and sensor configurations. To address these limitations, this work proposes a novel transfer learning (TL) framework based on multilinear principal component analysis (MPCA). First, a convolutional neural network (CNN) for regression is trained to perform damage localisation for a plated structure. Then, MPCA and fine-tuning are combined to have the CNN work for a different plate. By jointly applying MPCA to the source and target domains, the method extracts shared latent features, enabling effective domain adaptation without requiring prior assumptions about dimensionality. Following MPCA, fine-tuning enables adaptation of the pretrained CNN to a new domain without requiring a large training dataset. The proposed MPCA-based TL method was tested against 12 case studies involving different composite materials and sensor arrays, using both pseudo-damage and impact-induced damage. Statistical metrics were used to assess domain alignment both before and after MPCA, and the results demonstrate a substantial reduction in localisation error compared to CORAL and standard FT techniques. Hence, the proposed approach emerges as a robust, data-efficient and statistically-based TL framework for UGW-based SHM.","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4747489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048137","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}

引用次数: 0

Assessing Modal Consistency in Operational Modal Analysis via Multialgorithm Identification and Consensus Metrics 通过多算法识别和一致性度量评估操作模态分析中的模态一致性

IF 5 2区工程技术

Structural Control & Health Monitoring Pub Date : 2026-09-21 DOI: 10.1155/stc/7665600

Dag Pasquale Pasca, Matteo Verzeroli, Marco Civera, Angelo Aloisio, Bernardino Chiaia

{"title":"Assessing Modal Consistency in Operational Modal Analysis via Multialgorithm Identification and Consensus Metrics","authors":"Dag Pasquale Pasca, Matteo Verzeroli, Marco Civera, Angelo Aloisio, Bernardino Chiaia","doi":"10.1155/stc/7665600","DOIUrl":"https://doi.org/10.1155/stc/7665600","url":null,"abstract":"The increasing number of ageing civil structures requiring continuous monitoring has highlighted the limitations of fully automated approaches to operational modal analysis (OMA), particularly in the presence of algorithm-dependent variability and ambiguous stabilisation diagrams. In this context, the present paper proposes a modular, multialgorithm framework for assessing modal consistency and supporting decision-making in OMA. Rather than relying on a single identification technique, the framework integrates multiple clustering strategies applied to stabilisation diagrams obtained via covariance-driven stochastic subspace identification (Cov-SSI). The approach explicitly acknowledges that different structural typologies, ranging from slender high-rise buildings to massive civil and monumental structures, exhibit markedly different dynamic behaviours, for which no single algorithm can be expected to provide universally reliable results. To address this issue, clustering outputs from multiple algorithms are mapped into unified modal estimates, and a set of consensus metrics is employed to quantify the robustness and agreement of the identified modal parameters, including natural frequencies, damping ratios and mode shapes. The proposed methodology does not aim to replace expert judgement, but rather to provide a systematic, transparent and algorithm-agnostic assessment of modal reliability, highlighting consistent modes as well as ambiguous or uncertain identifications. The framework is demonstrated through both a numerical benchmark and an experimental case study based on real accelerometric data from the Heritage Court Tower.","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/7665600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149047869","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}

引用次数: 0