Structural Control & Health Monitoring最新文献

{"title":"Variational Mode Extraction-Guided Automated Asynchronous Operational Modal Analysis for Bridges","authors":"Tao Chen,&nbsp;Xiao-Mei Yang,&nbsp;Shu-Han Yang,&nbsp;Xiao-Jun Yao,&nbsp;Yong-Xiang Zheng","doi":"10.1155/stc/4398316","DOIUrl":"https://doi.org/10.1155/stc/4398316","url":null,"abstract":"<div>\u0000 <p>Structural modal parameters are crucial for monitoring the condition of bridges. Operational modal analysis (OMA) has garnered great attention in vibration-based structural health monitoring of bridges because it only requires vibration measurements from multiple sensors. Slight asynchronization often occurs in these measurements during the monitoring process. Applying classical OMA methods, such as the natural excitation technique (NExT) combined with the eigensystem realization algorithm (ERA), to asynchronous vibration measurements can lead to significant errors in modal parameters. To address this issue, this study proposes a modal assurance criterion (MAC)-based time synchronization technique to generate reliable synchronous vibration measurements for modal identification. The MAC-based method takes advantage of the proportionality of modal components and is only capable of detecting nonsynchronized issues between single-degree-of-freedom (SDOF) signals. A variational mode extraction (VME) technique is employed to iteratively decompose bridge vibration measurements into SDOF components. The VME technique eliminates the need for artificially predefining the number of modes, which was required in many signal decomposition techniques. After time synchronization, the proposed method employs the NExT–ERA-based automatic OMA method for modal identification. The effectiveness of the proposed method is demonstrated using vibration measurements from both the finite element model of a highway bridge and field monitoring data from an actual bridge. The results show that the proposed method successfully synchronizes vibration signals and identifies mode shapes, even in the presence of modal node phenomena.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4398316","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244806","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":"Evaluating Measurement System Capability in Condition Monitoring: Framework and Illustration Using Gage Repeatability and Reproducibility","authors":"Haizhou Chen,&nbsp;Jing Lin,&nbsp;Weili Zhao,&nbsp;Hengtao Shu,&nbsp;Guanji Xu","doi":"10.1155/stc/3441846","DOIUrl":"https://doi.org/10.1155/stc/3441846","url":null,"abstract":"<div>\u0000 <p>In condition monitoring, the reliability of a predictive maintenance program is critically dependent on the precision of data obtained from measurement systems. With increased availability, a significant challenge is evaluating the capability of these measurement systems to ensure data precision, which is fundamental for informed system selection. To address this challenge, this study proposes a systematic framework for evaluating the capability of these measurement systems using Gage repeatability and reproducibility (Gage R&amp;R) technique, subsequently judging the acceptability level and guiding their selection to guarantee the data precision. Our study investigates the capability of these systems in terms of repeatability and reproducibility, quantifying the contributions of different sources to the systems’ capability and providing directions for measurement system correction and enhancement. Another distinctive innovation of our approach is the use of three-region graphs, incorporating metrics including percentage of Gage R&amp;R to total variation, precision-to-tolerance ratio, and signal-to-noise ratio, which presents a comprehensive overview of the systems’ capability within one single figure. Two comparative experiments in distinct application scenarios were conducted to validate the effectiveness of the proposed framework. The insights presented serve as a valuable reference to replace the commonly used experience-based system selection in condition monitoring. Through this framework, we present a promising data-based approach aimed at enhancing the widely employed time-based calibration strategies, ultimately contributing to the improvement of data quality and the overall success of condition monitoring initiatives.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3441846","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244316","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":"The Effects of Nonproportional Damping on the Identification of Offshore Wind Turbine Foundation Properties","authors":"Emily F. Anderson,&nbsp;Ross A. McAdam,&nbsp;Manolis N. Chatzis","doi":"10.1155/stc/2227997","DOIUrl":"https://doi.org/10.1155/stc/2227997","url":null,"abstract":"<div>\u0000 <p>There is a consistent discrepancy between the predicted and measured dynamic responses of in situ offshore wind turbine (OWT) structures. Underestimation of the foundation soil stiffness is thought to contribute significantly to this difference. Identification of the in situ foundation properties of OWT from monitoring data would reduce this uncertainty, providing critical feedback on foundation design methods and aiding lifetime reassessment. In this study, a system identification framework for estimating the in situ foundation stiffness of a parked OWT is presented using a model updating approach applied to simulated data. The results are shown to accurately replicate the behaviour of the true foundation. The study also demonstrates that the nonproportional nature of the aerodynamic damping causes the structure to exhibit mode shapes whose real parts do not correspond to those of the undamped system. A normalisation technique is applied that obtains a close approximation of the undamped mode shapes from the complex damped mode shapes. It is demonstrated that large errors are introduced in the identified foundation behaviour if this normalisation is not employed. Such errors can result in misleading interpretations of the foundation or superstructure properties of the OWT.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2227997","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244313","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":"Observability Analysis for Structural System Identification Based on Static-State Estimation","authors":"Ahmad Alahmad,&nbsp;Roberto Mínguez,&nbsp;Rocío Porras Soriano,&nbsp;Jose Antonio Lozano-Galant,&nbsp;Jose Turmo","doi":"10.1155/stc/8386282","DOIUrl":"https://doi.org/10.1155/stc/8386282","url":null,"abstract":"<div>\u0000 <p>The concept of observability analysis has garnered substantial attention in the field of structural system identification. Its primary aim is to identify a specific set of structural characteristics, such as Young’s modulus, area, inertia, and possibly their combinations (e.g., flexural or axial stiffness). These characteristics can be uniquely determined when provided with a suitable subset of deflections, forces, and/or moments at the nodes of the structure. This problem is particularly intricate within the realm of structural system identification, mainly due to the presence of nonlinear unknown variables, such as the product of vertical deflection and flexural stiffness, in accordance with modern methodologies. Consequently, the mechanical and geometrical properties of the structure are intricately linked with node deflections and/or rotations. The paper at hand serves a dual purpose: firstly, it introduces the concept of static-state estimation, especially tailored for the identification of structural systems; and secondly, it presents a novel observability analysis method grounded in static-state estimation principles, designed to overcome the aforementioned challenges. Computational experiments shed light on the algorithm’s potential for practical structural system identification applications, demonstrating significant advantages over the existing state-of-the-art methods found in the literature. It is noteworthy that these advantages could potentially be further amplified by addressing the static-state estimation principles problem, which constitutes a subject for future research. Solving this problem would help address the additional challenge of developing efficient techniques that can accommodate redundancy and uncertainty when estimating the current state of the structure.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8386282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220075","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":"Consistent Seismic Event Detection Using Multi-Input End-to-End Neural Networks for Structural Health Monitoring","authors":"Guangcai Qian,&nbsp;Zhiyi Tang,&nbsp;Jiaxing Guo,&nbsp;Xiaomin Huang,&nbsp;Changxing Zhang,&nbsp;Wei Xu","doi":"10.1155/stc/9966359","DOIUrl":"https://doi.org/10.1155/stc/9966359","url":null,"abstract":"<div>\u0000 <p>Seismic events pose a significant threat to the safety of bridge structures, potentially causing extensive structural damage or collapse. Structural health monitoring (SHM) systems for long-span bridges capture structural response information and generate substantial data but face issues like sensor faults, environmental noise, and data transmission problems that can degrade data quality and hinder accurate seismic response identification. To address the problem, a multi-input end-to-end deep learning method for seismic event detection is proposed. Vibration data of different directions are separately utilized, and the interference of multi-type anomalous data is considered. First, the segmented acceleration time series were transformed into time-domain, frequency-domain, and probability density curve images, respectively, to form three-channel images; then, images from three directions were input to the neural network in parallel. Back-end architectures are constructed based on two fusion strategies, i.e., decision fusion and feature fusion. Consistent detection results across three-dimensional image sets can be obtained by the end-to-end architecture. A global voting process is implemented to further fuse the detection results of different image sets at the same moment. The proposed method is verified using data from two actual seismic events of a cable-stayed bridge. The results show that the proposed method can consistently and accurately detect seismic events even with interference from anomalous data. Among them, the feature fusion method has higher seismic event detection accuracy, while the decision fusion method offers a certain level of interpretability.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/9966359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214064","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":"Spatial Dynamic Early Warning of Different Positions in Underwater Tunnel Driven by Real-Time Monitoring Data","authors":"Xu-Yan Tan,&nbsp;Weizhong Chen,&nbsp;Lixiang Fan,&nbsp;Junchen Ye,&nbsp;Bowen Du","doi":"10.1155/stc/5397749","DOIUrl":"https://doi.org/10.1155/stc/5397749","url":null,"abstract":"<div>\u0000 <p>Research on early warning of tunnel anomalies is fundamental for achieving intelligent management. However, most current methods for determining early-warning values of tunnel mechanics indicators are different to couple the nonlinear variation property and spatial positional difference. Therefore, this research presents a novel approach to tunnel early warning based on deep autoregressive learning method (DL-AR) that considers the spatiotemporal correlations of structural mechanics responses, specifically tailored to dynamically determine the warning thresholds at different spatial positions. The methodology introduces a framework for predictive modeling and instantiates it on a typical underwater shield tunnel. After thoroughly learning the temporal and spatial correlations of structural mechanical responses, accurate predictions are made for the evolving trends of structural behaviors and the probabilities to the reasonable fluctuation range. Based on these predictions, spatially varying alert thresholds for structural behaviors are proposed. To ensure the reliability of the proposed model, a series of discussions and validation experiments are conducted. Results indicate that the proposed model effectively captured the spatiotemporal characteristics of structural evolution and identified alert ranges, defining permissible variations in structural trends. The prediction results showed near to 99% consistency with actual data, a 5% enhancement compared to classical models. Any deviation beyond this range triggers an early warning, demonstrating the efficacy of model in anticipating and responding to potential structural issues.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5397749","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214063","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":"Quantitative Analysis of Excavation-Damaged Zones for Effective TBM Tunnel Support Design","authors":"Fanjie Yang,&nbsp;Muhammad Usman Azhar,&nbsp;Hui Zhou,&nbsp;Chuanqing Zhang,&nbsp;Fudong Chi,&nbsp;Jingjing Lu,&nbsp;Tofeeq Ahmad,&nbsp;Hasan Arman,&nbsp;Alaa Ahmed","doi":"10.1155/stc/5029697","DOIUrl":"https://doi.org/10.1155/stc/5029697","url":null,"abstract":"<div>\u0000 <p>The mechanical characteristics of the excavation-damaged zone (EDZ) are essential in tunnel engineering for scientific design, safe construction, stability evaluation, and support optimization. Due to the lack of quantitative research on the mechanical characteristics of the EDZ and their impact on engineering support design, this paper proposes a quantitative investigation of the EDZ and a method for tunnel support optimization based on field monitoring studies. Then, the Gaoligong Mountains tunnel was analyzed using this method to quantify the mechanical characteristics of the EDZ during the tunnel boring machine (TBM) excavation and its impact on engineering support design. Firstly, the quantitative investigation of EDZ and engineering support optimization method was proposed based on the zonal crack density statistics in the EDZ, the evaluation of the zonal equivalent mechanical parameters using the Hoek–Brown criterion, stability analysis of surrounding rock considering EDZ zonal deterioration, and the engineering support design method. Secondly, the evaluation of the EDZ depth, rock mass wave velocity, and crack propagation in the surrounding rock mass during TBM excavation was analyzed based on field monitoring tests (the ultrasonic test, acoustic CT test, and digital borehole camera test) result of the Gaoligong Mountain tunnel. Thirdly, using the above method, the zonal equivalent mechanical parameters of the rock mass in the EDZ were calculated, and stability analysis of the surrounding rock considering EDZ zonal deterioration was carried out. Finally, the anchor design parameters (the length, pitch, and row spacing of the anchor) of the surrounding rock for the Gaoligong Mountain tunnel were analyzed and optimized. A second stability analysis of the surrounding rock was also carried out to validate the new approach using the conventional rock mass mechanical parameter equivalent. The depth of the plastic zone (7.4 m) in the proposed method was more in agreement with the field monitoring data (6∼8 m) as compared to the traditional method (9.2 m). Hence, this method could provide a stronger foundation for assessing and optimizing the tunnel design scheme and supporting measures.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5029697","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191097","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":"Study on Antiseepage Measures for Earth-Rock Dam Reservoir in Super-Intense Karst Area: Research on Curtain Grouting Scheme for Comprehensive Seepage Prevention","authors":"Mingming Wang,&nbsp;Chunying Shen,&nbsp;Jihong Duan,&nbsp;Ming Ye,&nbsp;Qiang Xu","doi":"10.1155/stc/1319986","DOIUrl":"https://doi.org/10.1155/stc/1319986","url":null,"abstract":"<div>\u0000 <p>Constructing reservoirs in karst regions is a challenge that dam engineers around the world are very unwilling to face. The antiseepage of reservoirs is one of the urgent problems to be solved in the construction of reservoirs in karst regions. Adopting the field test method combined with advanced geological exploration instruments and survey technologies, the antiseepage measures are studied in the construction of a reservoir located within a China’s super karst region. Through a detailed hydrogeological survey, the reservoir water seeps out through the downstream primary and secondary fracture points along the limestone dissolution erosion zone through the upstream sinkholes. According to the topography, lithology, submerged area, and seepage form and direction in the reservoir area, a vertical grouting curtain is proposed to block the seepage of reservoir water to the downstream. Based on the normal water level, impermeable layer (slate) distribution, and the lowest discharge datum plane, the left, right, and bottom boundaries of the vertical curtain grouting are determined, and the maximum grouting depth reaches 131.75 m. The double curtain grouting method is proposed to reduce the construction difficulty of the super-deep grouting curtain in the intense karst area, and alongside a method is put forward to integrate the upper and lower curtains into a cohesive unit. Practical validation through the Yundong Reservoir project demonstrates the efficacy of the proposed treatment scheme, ensuring seepage-free performance for 6 years under normal water levels. The findings lay the groundwork for further studies exploring specific challenges encountered during curtain grouting construction in karst environments, which include underground karst caves, strong corrosion zones, and underground large flow and high-speed pipeline inflow.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1319986","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140787","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 Concrete Resistivity Method Based on a Simple Measuring Cell for Onsite Corrosion Monitoring: Study on Concrete Under Varying Conditions","authors":"J. E. Ramón,&nbsp;I. Martínez,&nbsp;J. M. Gandía-Romero,&nbsp;A. Castillo,&nbsp;M. Valcuende","doi":"10.1155/stc/5522124","DOIUrl":"https://doi.org/10.1155/stc/5522124","url":null,"abstract":"<div>\u0000 <p>Concrete resistivity (<i>ρ</i>) is commonly monitored in situ using sensors based on the rebar-disc (RDM) or four-electrode (FEM) methods. This study validates, for the first time in reinforced concrete, an innovative corrosion sensor approach (CSA) previously tested only in simulated pore solutions. The CSA uses a single embedded two-electrode sensor that also allows the corrosion rate, offering a significant advantage for structural health monitoring. CSA resistivity values were broadly consistent with those from established reference methods: 2.9% higher than the RDM and 20% lower than the two-electrode method. Larger differences were observed with the FEM, decreasing when a finite-element cell factor (103%) was applied instead of one for semi-infinite elements (208%). This trend aligns with expected differences between FEM surface resistivity and bulk values. Additionally, a simple correction factor is proposed to normalise <i>ρ</i> to the reference temperature (<i>T</i>) of 20°C, expressed as <i>1</i>/(<i>a·</i>exp((<i>b</i>)·<i>T</i>)), with <i>a</i> and <i>b</i> equal to 1.7251 and 0.027 for low-resistivity concretes and 2.4851 and 0.046 for medium- to high-resistivity concretes. A general model for the full resistivity range yielded <i>a</i> = 2.0687 and <i>b</i> = 0.036. While further research is needed to explore wider corrosion scenarios, the results highlight the potential of the CSA as a practical tool for both laboratory and in situ corrosion assessment.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5522124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135611","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":"Robust Damage Detection and Localization Under Varying Environmental Conditions Using Neural Networks and Input-Residual Correlations","authors":"Niklas Römgens,&nbsp;Abderrahim Abbassi,&nbsp;Florian Fürll,&nbsp;Tanja Grießmann,&nbsp;Raimund Rolfes,&nbsp;Steffen Marx","doi":"10.1155/stc/3451930","DOIUrl":"https://doi.org/10.1155/stc/3451930","url":null,"abstract":"<div>\u0000 <p>This study aims to evaluate sequences of raw time series using an autoencoder structure for unsupervised damage detection and localization under varying environmental conditions (ECs). When it comes to structural health monitoring (SHM) for real-world applications, data-driven models need to improve sensitivity and robustness toward damage due to the EC-dependent variance. For systems situated outdoors, changing ECs affects the stiffness properties without causing permanent alterations to the structure. Applying data normalization strategies to consider these natural variations is not easy to conduct and is unfavorable for sensitivity regarding damage. To address these challenges, the model’s input variables are non-standardized to avoid input-related modifications and to feature a higher sensitivity toward structural changes. The autoencoder’s ability to capture structural variations caused by ECs and to handle non-standardized time series data makes it favorable for real-world applications. By quantifying the input-residual correlations, sensitivity, and robustness can be improved; no adjustments to the model have to be made. The autoencoder’s black-box nature is inspected by analyzing a linear dynamic 8DOF system and the Leibniz University Structure for Monitoring (LUMO). The neural network’s structure is identified by tracking the residual correlation. Here, a common test statistic of a whiteness test is used to find an optimal choice of the bottleneck dimension. Significantly increased robustness and sensitivity toward damage when evaluating the input-residual correlations instead of the reconstruction error is observed. To capture the temperature-dependent structural response for experimental validation, 10-min data sets of different structural temperatures are given to the neural network during training. It was derived that for damage detection, an amplitude-related normalization is inevitable due to the different excitation intensities in real life, which was carried out using input-residual correlations quantified by a Pearson coefficient. Considering the results obtained, autoencoders with non-standardized time series and input-residual correlations demonstrate a potent tool for vibration-based damage identification.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3451930","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118232","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}