Structural Control & Health Monitoring最新文献

{"title":"Genetic Algorithm-Based Optimization of Graded-Yield Damper Systems: Mechanical Parameter Design and Energy Dissipation Performance Analysis","authors":"Yun Chen,&nbsp;Gan Guo,&nbsp;Yunlong Zheng,&nbsp;Rui Dai","doi":"10.1155/stc/5772311","DOIUrl":"https://doi.org/10.1155/stc/5772311","url":null,"abstract":"<div>\u0000 <p>This study proposes a novel mechanical parameter design methodology for graded-yield dampers based on an enhanced genetic algorithm framework, accompanied by comprehensive design procedures and algorithmic flow diagrams. The proposed approach employs genetic algorithm optimization to determine optimal yield displacement and yield bearing capacity parameters for single yield-point metallic dampers under three seismic intensity levels (small, moderate and large earthquakes). These optimized parameters are subsequently utilized to construct quadrilinear skeleton curves for three-stage graded-yield dampers. Distinct hysteretic models are developed according to the energy dissipation characteristics of two damper configurations: non-gap annular-type and reserved-gap-type graded-yield dampers. A comparative analysis of vibration control performance reveals that both damper configurations demonstrate significant energy dissipation capabilities. The reserved-gap configuration exhibits superior energy dissipation efficiency compared to its non-gap counterpart. Gap-type dampers achieve better interstory drift control across all seismic intensities, particular in frequent earthquakes. Acceleration response mitigation shows marked improvement in both graded-yield systems. These findings provide critical theoretical insights for application and research of different types of graded-yield dampers.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5772311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767714","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":"Signal Recognition and Prediction of Water-Bearing Concrete Under Axial Compression Using Acoustic Emission and Machine Learning","authors":"Aiping Yu,&nbsp;Tao Liu,&nbsp;Tianjiao Miao,&nbsp;Xuandong Chen,&nbsp;Xuelian Deng,&nbsp;Feng Fu","doi":"10.1155/stc/6633988","DOIUrl":"https://doi.org/10.1155/stc/6633988","url":null,"abstract":"<div>\u0000 <p>The presence of free water in the concrete slurry significantly influences the crack patterns of concrete. In this study, uniaxial compression tests were conducted on concrete specimens with varying moisture contents under acoustic emission (AE) monitoring. Through parametric analysis and machine learning, the cracking process of water-containing concrete was studied, signal patterns during the cracking process were identified, and the impact of moisture content on the damage evolution and fracture mechanism of concrete was understood. The results indicate that free water is capable of absorbing high-frequency signals. With the increase of moisture content, the AE signals decrease. The failure of concrete is mainly of the tensile type, while the shear-type accounts for a relatively small proportion. The presence of free water decreases the likelihood of diagonal shear failure in concrete structures. The unsupervised learning was used for various moisture content analyses. Three distinct AE signal patterns were identified during the concrete compression tests: frictional motion signals of the compression surface, fracture surface activity signals, and aggregate cracking signals. Based on the moisture content, this study analyzes the variations in signal responses across different modes. A predictive model was established utilizing the BP neural network to differentiate signals of various modes, achieving an accuracy rate of 99%.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6633988","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758516","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":"Transferring Self-Supervised Pretrained Models for SHM Data Anomaly Detection With Scarce Labeled Data","authors":"Mingyuan Zhou,&nbsp;Xudong Jian,&nbsp;Ye Xia,&nbsp;Zhilu Lai","doi":"10.1155/stc/2414195","DOIUrl":"https://doi.org/10.1155/stc/2414195","url":null,"abstract":"<div>\u0000 <p>Structural health monitoring (SHM) has experienced significant advancements in recent decades, accumulating massive monitoring data. Data anomalies inevitably exist in monitoring data, posing significant challenges to their effective utilization. Recently, deep learning has emerged as an efficient and effective approach for anomaly detection in bridge SHM. Despite its progress, many deep learning models require large amounts of labeled data for training. The process of labeling data, however, is labor-intensive, time-consuming, and often impractical for large-scale SHM datasets. To address these challenges, this work explores the use of self-supervised learning (SSL), an emerging paradigm that employs unsupervised pretraining. The SSL-based framework aims to learn from only a very small quantity of labeled data by fine-tuning, while making the best use of the vast amount of unlabeled SHM data by pretraining. Basic and representative models from generative, contrastive, and generative–contrastive SSL categories are employed. These SSL models are compared and validated on the acceleration data of two in-service bridges, which is one of the most widely utilized types of measurements in SHM. Comparative analysis demonstrates that SSL techniques boost data anomaly detection performance, achieving increased <i>F</i>₁ scores compared to conventional supervised training, especially given a very limited amount of labeled data.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2414195","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740555","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":"Experimental Study of Online Structural Health Monitoring Using the Recursive Subspace Approach","authors":"Shieh-Kung Huang,&nbsp;Chung-Hsien Lee,&nbsp;Jin-Quan Chen,&nbsp;Chung-Han Yu","doi":"10.1155/stc/3304372","DOIUrl":"https://doi.org/10.1155/stc/3304372","url":null,"abstract":"<div>\u0000 <p>As a pivotal component in advancing the sustainable development goals (SDGs), structural health monitoring (SHM) has garnered increasing attention in the field of civil engineering. Considering the various approaches, model-based SHM is the most prevalent and remains highly effective due to its theoretical framework and nondestructive nature, creating a robust framework for effective SHM, enabling early detection of issues, and supporting informed maintenance strategies. Through decades, stochastic subspace identification (SSI) has been proven, and recursive SSI (RSSI) has been consequently applied for model-based SHM due to its ability to track modal parameters and generate accurate models. However, online validation through structural experiments has yet to be conducted with large-scale specimens. In this study, a shaking table experiment is conducted to validate the online implementation of RSSI for tracking time-varying modal parameters in real time. The full-scale specimen, experimental setup, and test framework are first described with great detail, and a numerical model is developed through a pretest using the shaking table system located in Taiwan. Subsequently, the simulation study provides numerous suggestions for experimental implementation. The experimental study then demonstrates that the proposed approach not only enables an online identification but also produces an accurate dynamic model. Besides, practical measures are recommended to fulfill online processing through the comprehensive simulation and experiential studies, especially those related to the user-defined parameters and ambient excitations. The results evidence that the SHM systems based on RSSI can effectively track the changes of dynamic characteristics under ambient excitations, ultimately facilitating the assessment and maintenance of structures.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3304372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725516","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":"LBN-YOLO: A Lightweight Road Damage Detection Model Based on Multiscale Contextual Feature Extraction and Fusion","authors":"Guizhen Niu,&nbsp;Guangming Li,&nbsp;Chengyou Wang,&nbsp;Kaixuan Hui","doi":"10.1155/stc/5595809","DOIUrl":"https://doi.org/10.1155/stc/5595809","url":null,"abstract":"<div>\u0000 <p>Detecting and classifying road damage are crucial for road maintenance. To address the limitations of existing road damage detection methods, including insufficient fine-grained contextual feature extraction and complex models unsuitable for deployment, this paper proposes a lightweight backbone and neck road damage detection model named LBN-YOLO. First, the backbone and neck of the original model are improved to be lightweight, and the C2f-dilation wise residual (C2f-DWR) module is integrated in the backbone to extract multiscale contextual information. Second, a simplified bidirectional feature pyramid network is employed in the neck structure to optimize the feature fusion network, reducing the number of parameters and simplifying the model complexity. Finally, a dynamic head with self-attention is introduced to enhance the sensing capability of the detection head, thus improving the precision of detecting occluded small objects. The proposed model’s detection ability is evaluated using a custom road damage dataset. The experimental results demonstrate that our proposed LBN-YOLO model achieves superior performance compared with the YOLOv8n model, with an increase of 4.1% in [email protected] and a 5.2% enhancement in precision, outperforming other detection models. In addition, the model is evaluated on two public datasets, showing improved detection performance compared with the original model, demonstrating strong generalization capabilities. Code and dataset are available at https://github.com/gzNiuadc/Road-crack-dataset.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5595809","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144712125","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":"Combining GPR, Passive Seismic, and Load Testing With Computational Models in the Assessment of Historical Bridges: The Case Study of the Comboa Bridge","authors":"Vega Perez-Gracia,&nbsp;Mercedes Solla,&nbsp;Simona Fontul,&nbsp;Oriol Caselles,&nbsp;Jesús Balado,&nbsp;Rodrigo Alva,&nbsp;Juan Luis Rodríguez-Somoza,&nbsp;Ramón González-Drigo","doi":"10.1155/stc/5309473","DOIUrl":"https://doi.org/10.1155/stc/5309473","url":null,"abstract":"<div>\u0000 <p>The preservation of historical bridges usually requires extensive structural evaluations for possible damage detection. Therefore, effective techniques are essential for diagnosis and, consequently, proper maintenance and rehabilitation actions. The combination of techniques provides complementary data that support decision making. A complete assessment was applied in the study of the Comboa Bridge, a medieval masonry structure in river Verdugo, in Galicia (Spain). It has three irregular arches, and the first visual inspection denotes the existence of important cracking and vegetation in the stonework. One of the most representative nondestructive testing (NDT) techniques for in situ evaluation is ground-penetrating radar (GPR) that offers detailed insights into subsurface conditions, revealing information about materials, voids, and deterioration, while loading tests and passive seismic methods provide dynamic responses that are related to type of structure and possible damage. This method was combined with loading tests to obtain deflections of the bridge deck and passive seismic for analyzing the dynamic behavior. Moreover, 3D models of the structure were set up using light detection and ranging (LiDAR), performed with terrestrial laser scanning, and unmanned aerial vehicle (UAV) surveying. By combining 3D models with NDT techniques, the results provide comprehensive information that enhances the understanding of a bridge’s condition and safety. These results are used for calibrating the dynamic computational model of the structure in order to obtain the vibration modes. Each technique used in the study presents limitations, which are addressed and discussed herein. Furthermore, the site conditions can also affect the results, as the effectiveness of these methods can vary greatly, depending on the materials and structures, which influences the electromagnetic and mechanical wave propagation. Additionally, the frequency of the waves may not effectively mark all relevant structural features or smaller damage. When used together, the NDT methods can complement each other’s strengths, but challenges remain. Overall, while these techniques are valuable tools for assessing historical bridges, awareness of their limitations is crucial for accurate interpretation and effective decision making in preservation efforts. The results obtained in the Comboa Bridge demonstrate improved accuracy in identifying structural anomalies. Additionally, recommendations to overcome some of these challenges in case of historical bridge assessment and also for the continuous monitoring and adequate maintenance actions to preserve the bridge integrity and safety are presented.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5309473","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705565","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":"Integrated Floating Slab Dynamic Vibration Absorber Based on Tuned Liquid Particle Damping: Theory, Modeling, and Experimentation","authors":"Duojia Shi,&nbsp;Pengzhan Liu,&nbsp;Tao Lu,&nbsp;Yi Qiu,&nbsp;Linlin Xie,&nbsp;Bing Feng Ng,&nbsp;Caiyou Zhao,&nbsp;Ping Wang","doi":"10.1155/stc/5050421","DOIUrl":"https://doi.org/10.1155/stc/5050421","url":null,"abstract":"<div>\u0000 <p>As subway train-induced low-frequency vibrations continue to rise, there is an increasing need for more effective vibration control strategies. Although current low-frequency vibration reduction methods offer some solutions, further progress is necessary. This paper introduces a novel tuned liquid particle damper-dynamic vibration absorber (TLPD-DVA), which merges the principles of tuned liquid dampers (TLDs) and particle dampers (PDs). By capitalizing on the low-frequency damping capabilities of TLDs, this approach incorporates particles suspended within the liquid to create a hybrid damping device capable of effectively attenuating vibrations across a wide low-frequency range (10 to 80 Hz). A discrete element method-computational fluid dynamics (DEM-CFD) model for multiphase flow is employed to explore the damping mechanism, optimize system parameters, and develop a frequency-dependent nonlinear damping device. The TLPD-DVA is then applied to floating slab track systems to control low-frequency vibrations, and a dynamic interaction model involving the coupled vehicle-TLPD-DVA-floating slab track-tunnel system is established to assess the system’s response. Harmonic response analysis of a floating slab track fitted with TLPD-DVAs, along with dynamic mass and mass ratio indices, clarifies the vibration reduction mechanism. Additionally, field tests demonstrate that the TLPD-DVA reduces vertical acceleration on the floating slab by up to 8 dB and on the tunnel wall by up to 10 dB within the low-frequency range, surpassing the performance of tuned DVAs. The proposed TLPD-DVA offers significant potential for vibration control in various civil engineering applications, including transportation infrastructure, building foundations, and vibration-sensitive facilities.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5050421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695768","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 Bridge Deflection Missing Data Repair Model Based on Two-Stage Modal Decomposition and Deep Learning","authors":"Zhijun Li,&nbsp;Jinrui Yang,&nbsp;Xuehong Li,&nbsp;Xiuli Xu","doi":"10.1155/stc/5458862","DOIUrl":"https://doi.org/10.1155/stc/5458862","url":null,"abstract":"<div>\u0000 <p>The bridge structural health monitoring (SHM) system will inevitably experience missing data. To ensure the integrity and practicability of the bridge SHM system, it is essential to repair the missing data. The existing data recovery methods mainly use the spatial correlation with other monitoring data but cannot adequately capture the time dependence of the raw monitoring data. This paper uses historical monitoring data to predict future data and complete the task of repairing missing data. A hybrid prediction model based on the gated recurrent unit (GRU) neural network, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and variational mode decomposition (VMD) is proposed. By decomposing the raw monitoring data, the input of the GRU model is optimized, resulting in improved accuracy of prediction and enabling the model to operate independently from other sensors. The accuracy of the method is verified based on the SHM data of a cable-stayed bridge. The prediction results of the proposed model are stable and reliable, with a prediction accuracy reaching 95%, indicating that the CEEMDAN-VMD-GRU model is suitable for repairing missing deflection data in bridge SHM systems.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5458862","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681205","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":"Coupled Thermal and Mechanical Behavior of Lead–Rubber Bearings: Full-Scale Testing and Numerical Modeling Methodology","authors":"Bin Xue,&nbsp;Wensheng Lu,&nbsp;Xiangxiang Ren,&nbsp;Wenlu Wen","doi":"10.1155/stc/8186890","DOIUrl":"https://doi.org/10.1155/stc/8186890","url":null,"abstract":"<div>\u0000 <p>Self-heating effect of the lead core in lead–rubber bearings (LRBs) under cyclic loading causes degradation of mechanical properties of LRBs, which in turn affects their self-heating effect. This study conducts full-scale tests and proposes a numerical modeling methodology to investigate the coupled thermal and mechanical behavior of LRBs. The methodology integrates mechanical modeling, thermal modeling, temperature-dependent material properties, and thermal-mechanical modeling. Experimental results reveal significant mechanical degradation under high-speed cyclic loading (0.25 Hz, 100% shear strain), with a temperature rise of 90°C in the lead core and a 22°C increase observed in adjacent rubber layers after 10 cycles. The numerical model demonstrates a good agreement with test data, accurately capturing force-displacement loops and temperature within the lead core. Numerical results show that the thermal–mechanical behavior of LRBs is sensitive to loading frequency and shear strain: increasing the frequency from 0.25 Hz to 0.5 Hz amplifies energy dissipation rates by 38%, while a 50% increase in shear strain (100%–150%) increases peak temperatures by 27%. A case study under nonharmonic motion shows that conventional mechanical models overestimate energy dissipation by 37% compared to the coupled thermal–mechanical model. The proposed modeling methodology provides a usable tool for investigating the coupled thermal and mechanical behavior of LRBs under various seismic conditions.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8186890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672841","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":"Theoretical Calculation and the Design Method of Tall Dual-Column Bents With Shear Beams Validated by Simulations and Tests","authors":"Wen Xie,&nbsp;Chongjie Jin,&nbsp;Yangfan Hong,&nbsp;Limin Sun","doi":"10.1155/stc/7736709","DOIUrl":"https://doi.org/10.1155/stc/7736709","url":null,"abstract":"<div>\u0000 <p>Dual-column bents with energy dissipation components represent one structural type of seismic resilient bridge bents. However, there have been few dynamic analyses and shaking table tests to validate the theoretical formulas, design methodology, and the seismic effects of the energy dissipation elements on dual-column bents, particularly for the tall dual-column bents. Thus, the study aims to derive theoretical formulas for calculating the yield strength, yield displacement, and elastic stiffness of tall dual-column bents with and without shear beams (SBs). This enables a more comprehensive understanding of structural performance and allows for more accurate predictions of the seismic behavior of these novel bents during seismic events compared to existing studies. A structural fuse-based design methodology for tall dual-column bents was developed and validated through verified finite element models and shaking table tests. Both numerical and experimental analyses were conducted to evaluate the effectiveness of SBs in mitigating seismic damage and responses in tall dual-column bents. The displacements and curvatures of the tall dual-column bents with and without SBs were analyzed. The results show that SBs enhance the seismic resilience and decrease the seismic responses of tall dual-column bents by strategically yielding first to dissipate energy, achieving up to 69.5% displacement reduction under E1-level earthquakes (PGA = 0.40 g) and 77.6% curvature reduction under E2-level earthquakes (PGA = 0.68 g) compared to tall dual-column bents without SBs. Crucially, this prioritized yielding mechanism enables SBs to function as structural fuses, suppressing structural responses below critical yield thresholds and safeguarding columns. Consequently, the tall dual-column bent without SBs undergoes seismic damage under E1-level earthquakes, while the tall dual-column bent with SBs does not suffer any damage. SBs enable the tall dual-column bent to meet performance targets. This suggests that SBs notably enhance the seismic resilience of tall dual-column bents, and the proposed design method can be used to design actual engineering structures.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/7736709","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647713","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}