Structural Control & Health Monitoring最新文献

{"title":"Dynamic Horizontal Displacement Evaluation Method of Tunnel Shield Tunnel Based on MSD Method for Basement Side Tunnels","authors":"Gang Wei,&nbsp;Zhiyuan Mu,&nbsp;Yitong Li,&nbsp;Yongjie Qi,&nbsp;Guohui Feng","doi":"10.1155/stc/5170617","DOIUrl":"https://doi.org/10.1155/stc/5170617","url":null,"abstract":"<p>The impact of pit excavation on the surrounding environment is closely related to the deformation characteristics of the surrounding enclosure structure. However, most existing methods rely on calculating pit unloading stress based on the Mindlin solution, which does not adequately account for the dynamic deformation characteristics of the enclosure structure at different excavation stages and is difficult to apply for real-time assessment. This paper presents a new calculation method based on the mobilizable strength design (MSD) approach to dynamically predict the horizontal displacement of the shield tunnel adjacent to the excavation pit. By introducing dynamic evaluation of the horizontal displacement of the enclosure structure, the applicability of the traditional MSD method is enhanced. The paper compares and analyzes the differences between this method, the modified MSD (MMSD) method, the MSD method, and measured data from actual pit excavation cases. The results demonstrate that the proposed method more accurately reflects the deformation characteristics of the enclosure structure at different excavation stages and its dynamic impact on the horizontal displacement of the shield tunnel. The spatial distribution of horizontal displacement in the enclosure structure under zoned excavation is analyzed, revealing the coupling relationship between the deformation characteristics of the enclosure structure and the tunnel’s deformation response. The findings of this study provide valuable references for the safety assessment and protective measures of shield tunnels during pit excavation.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5170617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224213","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":"Coupling sPCA-Based Statistical Modeling With Deep Residual Networks Considering Thermal Effect for Deformation Forecasting in High Dams","authors":"Bo Liu,&nbsp;Fangfang Liu,&nbsp;Fei Song","doi":"10.1155/stc/6688960","DOIUrl":"https://doi.org/10.1155/stc/6688960","url":null,"abstract":"<p>Accurate prediction of deformation under thermal influences is critical for the safety assessment and long-term performance of high dams. This study proposes a novel two-stage prediction framework that integrates statistical modeling with deep learning to enhance the interpretability and accuracy of dam deformation forecasting. In the first stage, sparse principal component analysis (sPCA) is employed to extract dominant features from high-dimensional thermometer data. These features are then used to construct an interpretable dam deformation monitoring model using multiple linear regression (MLR), referred to as the HT_sPCAT-MLR model. In the second stage, the multilayer bidirectional gated recurrent unit (multi-Bi-GRU) network is developed to model the residuals of the HT_sPCAT-MLR framework, leveraging advanced gating mechanisms and bidirectional temporal learning to improve long-term prediction accuracy. Furthermore, the adaptive genetic algorithm (AGA) is utilized to optimize the hyperparameters of the multi-Bi-GRU model, enhancing the robustness and generalization of the residual correction module. The proposed methodology is validated using real-world monitoring data from an ultra-high arch dam. Quantitative evaluation at four representative measurement points shows that the proposed model consistently outperforms baseline methods across all key metrics. Specifically, it achieves <i>R</i>² values above 0.99, mean absolute error reductions of over 80% compared to traditional models, and the lowest sMAPE across all cases. The experimental results demonstrate model’s superior prediction accuracy, robustness, and practical applicability for dam deformation. The integrated framework offers a reliable and interpretable solution for thermal deformation forecasting in high dam structures.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6688960","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224544","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 Data-Driven Framework for Explainable Artificial Intelligence in Pavement Distress Analysis and Decision Support: Integrating Clustering Models and Principal Component Analysis","authors":"Xiaogang Guo","doi":"10.1155/stc/8852297","DOIUrl":"https://doi.org/10.1155/stc/8852297","url":null,"abstract":"<p>The increasing complexity of transportation infrastructure demands advanced, data-driven approaches for early pavement distress detection and maintenance decision-making. Traditional assessment methods often fail to provide reliable, interpretable, and proactive insights into pavement degradation. This study introduces an Explainable Artificial Intelligence (XAI) framework that integrates clustering algorithms with principal component analysis (PCA) to improve early-stage pavement distress analysis. The proposed framework leverages K-means, Gaussian mixture models (GMMs), and hierarchical clustering, applied to a customized dataset encompassing pavement performance metrics, geospatial information, and aggregate properties. By incorporating ground-truth validation, our approach not only differentiates between high-quality and deteriorating pavement sections but also reveals underlying factors contributing to distress, overcoming the opacity of traditional machine learning (ML) models. Results demonstrate that this transparent, interpretable AI-driven framework enhances infrastructure resilience by enabling data-informed decision-making for predictive maintenance. Beyond transportation engineering, the methodology establishes a scalable paradigm for explainable AI applications in civil infrastructure, advancing the intersection of ML, geospatial analysis, and material science.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8852297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224214","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 Data-Driven Approach for Multirate Transitioning Between Complex and Nonlinear Substructures in Real-Time Hybrid Simulation","authors":"Diego Mera,&nbsp;Gaston Fermandois,&nbsp;Fernando Gomez","doi":"10.1155/stc/5991335","DOIUrl":"https://doi.org/10.1155/stc/5991335","url":null,"abstract":"<p>This article proposes a new approach to interface both numerical and experimental substructures of a real-time hybrid simulation experiment running at different sampling rates. A regularized Wiener statistical finite impulse response filter is applied to the slow-rate sequence of interface target displacements at the numerical substructure to predict the next data point. Then, an interpolation rule using monomials is applied to obtain the sequence of interface target displacements at the experimental substructure running at a fast sampling rate. The Wiener filter is trained using offline simulations of the partitioned reference structure before the main experiment. The proposed scheme achieves good results for virtual simulations with linear and nonlinear structures, and it separates the task of determining simulation rates between substructures, ensuring both accuracy and stability in the experimental test.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5991335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224161","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":"Integrating Virtual Sensor Data Augmentation Into Machine Learning for Damage Quantification of Bolted Structures Under Assembly Uncertainty","authors":"J. S. Coelho,&nbsp;M. R. Machado,&nbsp;M. Dutkiewicz","doi":"10.1155/stc/8030303","DOIUrl":"https://doi.org/10.1155/stc/8030303","url":null,"abstract":"<p>Machine learning algorithms have significantly advanced structural monitoring by achieving accuracy levels outperforming traditional methods. These approaches facilitate uncertainty modeling and statistical pattern recognition analysis, supporting decision-making and manipulating broader data fusion. Efficient condition assessment of bolted structures, widely used in engineering systems and structural steel members, is crucial for maintaining stability, preventing unwanted loosening, and enabling scheduled maintenance. A critical issue in bolted systems, torque loosening, is often caused or aggravated by excessive vibrations, shocks, temperature variations, and improper usage, increasing the risk of structural faults. Predicting and monitoring bolt loosening remain a significant challenge, as it typically requires expensive inspections and operational controls. This work proposes an enhanced machine learning–based condition assessment model for estimating bolt torque loosening using the spectrum of raw vibration signals and data-driven augmentation strategies. The condition monitoring accounts for intrinsic variability introduced during the assembly process, with damage indexes derived from dynamic responses serving as feature extractors. The machine learning model utilizes data augmentation and fusion to enhance the dataset, relying solely on experimental data, thereby eliminating the need for numerical models. The results demonstrate significant enhancement in the model performance by adopting the integrated dataset, yielding improved torque estimation accuracy with lower error rates. In addition, the monitoring process incorporates uncertainty quantification associated with torque estimation, providing a more reliable assessment of the system’s condition. Furthermore, this study highlights the potential of data-driven machine learning damage assessment techniques in bolted joint monitoring, providing an effective and efficient method for detecting bolt torque loosening using raw vibration spectra. The proposed approach accelerates inspection and establishes a robust technique for monitoring bolted systems.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8030303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146399","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":"Development and Application of a Dynamic Theoretical Model for the Eddy Current Dampers Based on Mechanical Experiment","authors":"Hui-Juan Liu,&nbsp;Xing Fu,&nbsp;Hong-Nan Li,&nbsp;Fu-Shun Liu","doi":"10.1155/stc/1063991","DOIUrl":"https://doi.org/10.1155/stc/1063991","url":null,"abstract":"<p>Eddy current damper (ECD) has emerged as a highly desirable solution for vibration control due to its exceptional damping performance and durability. However, the inherent nonlinearity of the ECD poses significant challenges in research and engineering implementations. Traditional views attribute the nonlinearity of the ECD solely to variation in velocity. However, experimental results reveal that nonlinearity still exists even at a constant velocity. The nonlinearity at a constant velocity has not been sufficiently emphasized and quantitatively modeled. This study addresses the issue by developing a dynamic theoretical model with clear physical meaning and a simple mathematical form. A comprehensive study of the nonlinear characteristics of the ECD has been carried out using a combination of experimental and theoretical analysis. Firstly, the basic construction and working mechanism of a velocity-amplified hamburger-shaped eddy current damper (VHECD) are described in detail. Subsequently, a prototype experiment is conducted to explore the mechanical performance of the VHECD. Most importantly, a nonlinear phenomenon at a constant velocity is revealed and a dynamic theoretical model is developed. Finally, the dynamic theoretical model is validated through the experimental results of the VHECD and numerical simulation of a single-degree-of-freedom (SDOF) system. The proposed dynamical theoretical model generalizes the nonlinear phenomenon at a constant velocity. Both the coefficient of determination of force and the mean absolute percentage error of energy dissipation show that the dynamic theoretical model performs exceptionally well. The numerical simulation of the SDOF system demonstrates that the proposed dynamic theoretical model can more accurately predict the damping performance of ECD than the Wouterse model. This dynamic theoretical model is useful for the physical understanding of the ECD and the engineering application.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1063991","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145102112","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":"Vision-Based Monitoring of Absolute and Relative Displacements in Multistory Buildings During Full-Scale Shake-Table Tests","authors":"Laura Gioiella,&nbsp;Fabio Micozzi,&nbsp;Morgan McBain,&nbsp;Michele Morici,&nbsp;Alessandro Zona,&nbsp;Andrea Dall’Asta,&nbsp;Barbara G. Simpson,&nbsp;Andre R. Barbosa","doi":"10.1155/stc/2618220","DOIUrl":"https://doi.org/10.1155/stc/2618220","url":null,"abstract":"<p>Displacements are among the most important engineering response parameters to be monitored during shake-table testing, with experiments playing a key role in studying the seismic behavior of structures. However, their accurate measurement is not a trivial task when using contact sensors. Computer vision is an attractive alternative for monitoring absolute and relative displacements, and this study presents a new configuration to fully exploit its potential. The proposed solution combines internal and external video cameras. The former is installed on the roof and points downwards to simultaneously acquire the displacements of targets located throughout the height of the building. The latter was installed outside the shake-table platen and tracked the roof displacements to provide redundant measures for control and noise compensation. In this way, the movements of the buildings can be reconstructed with high robustness and precision using a limited number of video cameras. The proposed configuration was applied for the first time during shake-table testing of a full-scale six-story building on the outdoor shake table at the University of California, San Diego. The measurements obtained up to strong dynamic inputs showed the capacity of the proposed approach in real-world environmental conditions and were used for a critical comparison with conventional contact sensors.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2618220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145102058","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":"Explainable AI-Driven Optimal Feature Selection for the Identification of Structural Damage","authors":"Xinwei Wang,&nbsp;Zheng Wei,&nbsp;Zhihao Wang,&nbsp;Shuaiqiang Wei,&nbsp;Yanchun Li,&nbsp;Muhammad Moman Shahzad","doi":"10.1155/stc/7253150","DOIUrl":"https://doi.org/10.1155/stc/7253150","url":null,"abstract":"<p>The existing scholarly investigations into intelligent structural damage recognition predominantly emphasize the enhancement of the precision and efficacy of damage detection. Nonetheless, the opaque “black box” characteristic inherent to deep learning frameworks constrains users’ comprehension of the underlying decision-making mechanisms, which significantly obstructs their practical progression and execution. Consequently, this manuscript employs the interpretative framework known as Shapley Additive exPlanation (SHAP) to elucidate and scrutinize the attributes of a convolutional neural network–based intelligent structural damage recognition model, while also proposing a methodology for the optimization of features pertinent to structural damage recognition. In particular, this inquiry clarifies the foundational principles that govern the output results of damage assessment and identifies the prospective optimal characteristics of structural damage identification signals. In assessing the contribution of various features to the results of damage recognition and the interrelations among these features, both global and local perspectives of the damage signal were taken into account. The interpretation and analysis of damage recognition signal characteristics can facilitate the selection of structural damage recognition features, thereby aiding deep learning models in the extraction of high-dimensional features and markedly enhancing the recognition accuracy of structural damage identification. The efficacy of the proposed algorithm was corroborated through two experimental scenarios, with results indicating that the accuracy of the structural damage identification algorithm delineated in this study surpassed 95%. This research offers thorough guidance for the implementation of SHAP analysis within intelligent structural damage models, and the findings hold significant implications for augmenting the interpretability of intelligent damage identification algorithms.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/7253150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012635","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":"Influence of Bridge Bearings on Mitigating Nonlinear Seismic Responses of Straddle-Type Monorail Trains","authors":"Junyong Zhou,&nbsp;Tang Tang,&nbsp;Xiaohui Wang,&nbsp;Cheng Huang,&nbsp;Jianxu Su,&nbsp;Jiang Yi,&nbsp;Jin Guo","doi":"10.1155/stc/6724029","DOIUrl":"https://doi.org/10.1155/stc/6724029","url":null,"abstract":"<p>Straddle-type monorail systems (STMSs) are increasingly adopted as medium-capacity transit solutions to alleviate urban traffic congestion. However, their operational resilience during earthquakes is challenged by the low lateral stiffness of track beams and single-column piers, with critical components like bearings and piers vulnerable to elastoplastic behavior under seismic loads. To this end, this study proposes a MATLAB + OpenSees co-simulation framework to investigate nonlinear vehicle–bridge interaction (VBI) dynamics in STMSs subjected to seismic excitations. Validation shows high consistency with literature results, with Pearson correlations of &gt; 0.81 and &gt; 0.98 for bridge and train responses and relative errors of maximal values &lt; 4%. Three types of bridge bearings—regular spherical steel bearing, lead rubber bearing (LRB), and friction pendulum bearing (FPS)—are compared to assess their influence on mitigating vibration responses. Both LRB and FPS effectively reduce lateral vibrations of the track beam and train, with maximum reduction rates reaching up to 60%. The shear forces and bending moments at the bottom of the piers are also substantially reduced by the isolation bearings, with reduction rates up to 50%. The proposed approach can be extended for nonlinear VBI analysis of STMSs under severe nonlinear excitations such as strong earthquakes, high winds, or collision loads.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6724029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998959","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":"Damage Identification of an Offshore Wind Turbine Support Structure Using VMD and Deep Transfer Learning","authors":"Jianda Lv,&nbsp;Yansong Diao,&nbsp;Yi Zhang,&nbsp;Jingru Hou,&nbsp;Yijian Ren,&nbsp;Yun Liu,&nbsp;Xiuli Liu,&nbsp;Chenhui Zhang","doi":"10.1155/stc/1699730","DOIUrl":"https://doi.org/10.1155/stc/1699730","url":null,"abstract":"<p>When identifying damage to an offshore wind turbine (OWT) support structure, the influence of harmonic components in vibration response and the difficulty of acquiring data in the damaged state will be encountered. Therefore, the current paper employs the variational mode decomposition (VMD) and sim-to-real deep transfer learning (TL) to identify the damage to an OWT support structure. To eliminate the effect of harmonic components, the vibration response is decomposed using VMD, and the modal response’s reconstructed signal (only containing the structure’s natural frequency) is selected for damage identification. The numerical simulation data and the model test’s measured data are utilized as the source domain (SD) and target domain (TD), respectively. The source model is established by training a convolutional neural network (CNN) with the SD data. The source model’s network structure and weight are frozen to the TD network’s corresponding position. The measured data are utilized to fine-tune the parameters to establish a target model, which is tested to attain the damage identification outcomes. The presented method is validated using the model test data of an OWT support structure.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1699730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998960","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}