Structural Control & Health Monitoring最新文献

{"title":"A Mechanics-Informed Data-Driven Model for Predicting the Lateral Deformation of Retaining Piles in Deep Excavations: Integrating Spatial Graph Convolutional Networks With Transformer-Augmented Temporal Kolmogorov–Arnold Networks","authors":"Qiang Wang,&nbsp;Jie Zhao","doi":"10.1155/stc/4482171","DOIUrl":"https://doi.org/10.1155/stc/4482171","url":null,"abstract":"<p>Accurate prediction of lateral deformation in retaining piles is critically important for ensuring safety in deep excavations. However, the deformation process of such piles involves complex spatiotemporal coupling effects and nonlinear dynamic characteristics, which are often inadequately captured by traditional methods, thereby limiting prediction accuracy. To address this gap, in this study, a GCN–Former–TKAN prediction framework with a hierarchical architecture is proposed for systematic spatiotemporal information integration. First, a recursive feature elimination (RFE) algorithm is adopted to screen the most deformation-sensitive influencing factors from multisource monitoring data, and parameters with clear physical driving significance are retained. Subsequently, graph convolutional networks (GCNs) are used to explicitly model the spatial topology of monitoring points, whose Spearman correlation-based graph structure implicitly reflects the propagation pattern of excavation disturbance. Next, a Transformer is introduced to conduct deep temporal modeling of spatial feature sequences output by the GCN, thereby capturing long-term dependencies and adaptively weighting features at different time steps via a self-attention mechanism. Finally, the temporal Kolmogorov–Arnold network (TKAN) serves as a nonlinear mapping module that fuses Transformer-enhanced spatiotemporal features with multisource time series to produce predictions. On the basis of a real-world engineering case, a time-window sensitivity analysis reveals that a 7-day historical data window provides the most stable overall performance in this case study. The results of ablation and comparative experiments indicate that the proposed model achieves higher prediction accuracy and more stable performance than the selected baseline models do. This study provides a practical data-driven reference framework for the deformation prediction and health monitoring of deep excavation retaining structures.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4482171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994151","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":"Crack Detection and Localization for Concrete Structures Using UAV Incorporating Dual-Camera Systems With Heterogeneous Lenses","authors":"Yujie Yan,&nbsp;Yihui Ding,&nbsp;Kongbiao Wei,&nbsp;Mida Cui,&nbsp;Dongming Feng,&nbsp;Gang Wu","doi":"10.1155/stc/2011149","DOIUrl":"https://doi.org/10.1155/stc/2011149","url":null,"abstract":"<p>Recent advances in unmanned aerial vehicles (UAVs) and optical imaging provide an alternative to traditional close-range human inspection of hard-to-reach areas in in-service bridges. Intelligent data acquisition and analysis techniques have been developed in recent studies to advance the level of automation and accuracy for such a UAV-based inspection approach. Nevertheless, simultaneous detection and 3D localization of microscale concrete cracks remains challenging due to the inherent trade-off between image resolution and field of view of optical cameras. This study proposes a UAV-based dual-camera inspection framework that integrates a telephoto camera for high-resolution crack imaging with a wide-angle camera for robust pose estimation and scene reconstruction. A two-stage crack segmentation network is developed by combining DeepLab v3+ with a point-based rendering (PointRend) refinement module to enhance crack boundary delineation in high-resolution imagery. In situ field experiments were conducted to validate the effectiveness of the proposed approach in crack delineation and localization, and the results demonstrate that an average crack localization error of 10.5 cm is achieved using a widely adopted DJI UAV platform and payload.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2011149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994150","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":"Incipient Buckling Detection Using Nonlinear System Identification Algorithms: A Comparative Study","authors":"Raymond Andrew Swartz,&nbsp;Padmanabh Shridhar Desai","doi":"10.1155/stc/5072566","DOIUrl":"https://doi.org/10.1155/stc/5072566","url":null,"abstract":"<p>Structural health monitoring detects and characterizes damage to predict failure, but failures like buckling, which lack signs of damage, are harder to detect. Direct load measurement is costly and complex, especially for dead loads requiring copious sensors and data acquisition hardware. Vibration-based detection is a good alternative because it infers global structural characteristics with fewer sensors. This paper presents a structural health monitoring approach for the detection of incipient buckling in structures due to excessive load conditions. Existing nonlinear modeling algorithms available in commercial software platforms, such as MATLAB and other open-source toolboxes, are leveraged to make this method potentially available to practitioners. The proposed method attempts to infer the presence of incipient buckling behavior from the linearity of relatively low-order system identification models fitted to global vibration signals measured at low levels of ambient-scale vibration. Three widely available nonlinear system identification models are investigated in this proof-of-concept study: (1) a nonlinear autoregressive with an exogenous input (NLARX) algorithm with a wavelet function, (2) an NLARX algorithm with Tree Ensembles, and (3) a physics-informed dynamic mode decomposition for detecting incipient buckling. Synthesized ambient vibration data are obtained from numerical models of small-scale frame structures with varying gravity loads, incorporating nonlinearity via P-delta and large-displacement effects. For this proof-of-concept, variable control is vital, and using simulation data enables disabling material nonlinearity and connection slip effects to demonstrate the viability of nonlinear system identification for buckling alerts. Follow-up studies are planned to validate the results using physical models. The average norm of the nonlinear component of the output is chosen as the feature and compared at incremental loadings up to the incipient buckling load. Scaling issues, the effects of unmeasured disturbances, and unidentified sensor noise are also examined to highlight the limitations of the proposed methods in terms of excitation scale.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5072566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943271","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":"Attitude Control Mechanism of High-Speed Trains After Derailment by On-Board Protective Devices Based on Whole Process Experiments","authors":"Fengzhuang Tong,&nbsp;Jinjie Chen,&nbsp;Jianxi Wang,&nbsp;Pengfei Liu,&nbsp;Long Chen","doi":"10.1155/stc/6796511","DOIUrl":"https://doi.org/10.1155/stc/6796511","url":null,"abstract":"<p>Derailment accidents of high-speed trains (HSTs) occur occasionally. How to enable derailed vehicles to decelerate and come to a stop as much as possible along the track alignment is the last line of defense for mitigating the losses caused by HST derailment disasters. To address the complex collision and contact behaviors between vehicle and track components during the derailment process, a dynamic interaction model of the vehicle–track system was developed. Through HST derailment tests, the validity of the vehicle–track coupled dynamic model established in this study for the whole derailment process was verified. This paper studied the regulatory influence of on-board protective devices (OBPDs) on the running attitude of HSTs. It analyzed the effects of factors such as train speeds and the installation positions of OBPDs on the running attitude of HSTs after derailment and revealed the influence mechanism of OBPDs on the disaster mitigation effect of HSTs after derailment. The results indicate that the stability of the vehicle after derailment is optimal when the distance between the protective device and the outer side of the wheel is <i>l</i>_<i>p</i> = 140∼190 mm. However, even with protective devices installed, the risk of vehicle body rollover remains significant when the train speed exceeds 330 km/h. The findings provide a theoretical foundation for ensuring the safe operation of HSTs.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6796511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943076","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":"Enhancing Self-Powered Active Control Tolerance to Cable Tension Variations Using Neural Network–Based Identification","authors":"Heming Xing,&nbsp;Gang Li,&nbsp;Jin-Yang Li","doi":"10.1155/stc/6451025","DOIUrl":"https://doi.org/10.1155/stc/6451025","url":null,"abstract":"<p>The emergence of self-powered active control (SPAC) technology offers a promising solution for multimodal vibration control in cable structures. This technology retains the advantages of conventional active control in suppressing multiple vibration modes while significantly improving system stability by preventing excessive net energy injection. However, structural parameters such as cable tension may change significantly in harsh environments—such as large diurnal temperature variations or severe corrosion—or after long-term service. Such changes may degrade the accuracy of control gain calculations in SPAC systems, disrupt the self-powered mechanism, and eventually lead to system failure and loss of control over the cable structure. Therefore, it is necessary to continuously update the cable tension parameters in the SPAC controller using tension identification techniques. Existing vibration-based tension identification methods, however, are only suitable for passive control devices with fixed mechanical properties (mass, stiffness, and damping) and cannot be applied to active control systems that adjust control forces in real time. To address this gap, this paper presents a neural network–based method for identifying cable tension under active control conditions, designed to enhance the tolerance of SPAC to cable tension variations. Specifically, trained on a dataset generated via the finite difference method and LQG control theory, a neural network is integrated into the SPAC system to directly calibrate the cable tension parameters within the controller under specified conditions. Numerical simulations demonstrate that the neural network–based SPAC system with tension identification remains stable under moderate wind speeds and within a fluctuation range of 80%–120% of the initial cable tension. In contrast, the conventional SPAC system fails when the cable tension exceeds 92% or 106% of the initial value.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6451025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942559","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 Multirate Extended Kalman Filter for the Joint Identification of Structural State-Damage-Unknown Inputs","authors":"Lijun Liu,&nbsp;Wei Luo,&nbsp;Jiale Zhang,&nbsp;Shiyu Wang,&nbsp;Ying Lei,&nbsp;Nan Gong","doi":"10.1155/stc/6594136","DOIUrl":"https://doi.org/10.1155/stc/6594136","url":null,"abstract":"<p>Structural system identification provides a fundamental basis for the damage assessment of civil infrastructure. However, its practical implementation is challenged by inconsistent sensor sampling rates, unknown external excitations, and sparsely distributed structural damage. To this end, a multirate extended Kalman filter with unknown input (MREKF-UI) is proposed for the joint identification of structural states, sparse damage, and external loads. The proposed MREKF-UI constructs a dynamic observation model by fusing multirate acceleration and strain response data acquired synchronously or asynchronously. An <i>l</i>₁-norm regularization term is incorporated as a sparsity constraint on damage and embedded into the MREKF-UI filter update step via a pseudomeasurement strategy. The proposed framework formulates full-field response, sparse damage identification, and unknown load estimation within a unified filtering scheme. Numerical simulations on an overhanging beam and a truss structure are conducted to validate the proposed method under synchronous and asynchronous multirate sampling conditions. The results demonstrate accurate damage localization and quantification, reliable identification of unmeasured displacement and velocity responses, and effective estimation of unknown external excitations. Therefore, the proposed MREKF-UI provides a feasible framework for intelligent structural diagnosis using multisource heterogeneous sensor data under unmeasured excitation conditions.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6594136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942681","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":"Stochastic Model Updating for Nonlinear Isolation Structures via Constrained Unscented Kalman Inversion","authors":"Jiancheng Gu,&nbsp;Yu Lin,&nbsp;Dan Li,&nbsp;Xinhao He","doi":"10.1155/stc/9983512","DOIUrl":"https://doi.org/10.1155/stc/9983512","url":null,"abstract":"<p>In earthquake engineering, motivated by the extensive use of isolation systems, the pronounced nonlinear behavior of structures, and the pressing need to account for pervasive uncertainties, the exploration of model updating for structural performance assessment with limited sensor data has increasingly expanded to nonlinear and probabilistic scenarios. These challenges are particularly evident because nonlinear behaviors hinder the use of linear methods that rely on periods and mode shapes as indicators. Although stochastic sampling methods that generate large ensembles to approximate probability distributions have been widely adopted in batch or offline Bayesian inference, this study introduces constrained unscented Kalman inversion (UKI) as an efficient alternative. UKI achieves a balance between computational efficiency and accuracy by employing deterministic sigma points, generated through the unscented transform, to construct a sequence of intermediate probability distributions transitioning from the prior to the posterior. To further constrain the solution space and ensure physical rationality and numerical stability, two constraint-imposition schemes are incorporated into both forward uncertainty propagation and inverse calibration steps. These schemes impose general constraints while preserving the first two moments of parameters, thereby forming the constrained UKI. The effectiveness of UKI is demonstrated through three illustrative examples: steel dampers and high-damping rubber bearings subjected to uniaxial and biaxial static loadings, respectively, and an isolation building subjected to biaxial dynamic excitation. The validation encompasses a range of model complexities, including strength-hardening, strength-degrading and pinching hysteresis, and evaluates probabilistic parameter estimation, prediction of unmeasured responses, convergence behavior, robustness analysis and comparisons with existing methods.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/9983512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942609","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":"Research on Partial Label–Driven Multitask Deep Learning Model in the Field of Building Damage Recognition","authors":"Weichao Lyu,&nbsp;Dezhang Sun,&nbsp;Junwu Dai","doi":"10.1155/stc/2372206","DOIUrl":"https://doi.org/10.1155/stc/2372206","url":null,"abstract":"<p>Despite numerous previous studies proposing various deep learning methods for identifying building damage–related tasks using diverse image datasets and models, the application and advantages of partial labels in multitask recognition within civil engineering remain rarely discussed, and such deep learning–based recognition tasks are inherently challenging. To address this issue, this paper proposes a partial label–driven multitask deep learning model. The method first integrates image data from multiple damage tasks through hash mapping to construct a partially annotated multitask dataset for efficient utilization of sparse annotation information and then establishes a multitask learning (MTL) framework with EfficientNet-B0 as the shared feature extraction backbone and task-specific output heads for different damage tasks. During training, the model calculates losses only using the available labels of each image and learns multiple damage recognition tasks simultaneously through joint optimization. Based on the public ϕ-Net benchmark dataset, controlled comparative experiments are conducted on a full-label intersection multitask model, single-task models, and ablation tests with fixed training sample sizes. Results show that the proposed multitask model trained on the partial-label union dataset significantly outperforms the full-label intersection multitask model and achieves superior performance over task-specific single-task models in most tasks. When the training sample size is fixed to the scale of the four-task full-label intersection (2779 images), the masked partial-label multitask model still performs better than the full-label intersection model across all tasks, and ablation studies verify that computing losses only at locations with valid labels is one of the main contributors to performance gains. Compared with per-task single-task models under the same sampling strategy in the union dataset, the benefits of MTL are task dependent. This paper contributes a novel visual recognition framework that integrates partial labels and MTL and systematically demonstrates its performance advantages and applicable boundaries, which can support the rapid batch analysis of postdisaster building images.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2372206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942713","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":"Probabilistic Corrosion Detection of Reinforced Concrete Structures via Magnetic Flux Leakage Signal and Bayesian Neural Network","authors":"Xin-Rui Ma,&nbsp;Shi-Zhi Chen,&nbsp;Ye-Peng Chen,&nbsp;Xiang-Yu Wang","doi":"10.1155/stc/2884415","DOIUrl":"https://doi.org/10.1155/stc/2884415","url":null,"abstract":"<p>Internal reinforcement corrosion threatens the durability and safety of concrete structures, yet conventional nondestructive testing (NDT) methods remain largely qualitative and struggle to detect early-stage corrosion concealed within the concrete cover. Magnetic flux leakage (MFL) is a promising NDT approach for reinforcement corrosion detection. However, variations in corrosion morphology and reinforcement layout induce substantial signal fluctuations and intrinsic uncertainties that deterministic models cannot represent. To address this, this study proposes a probabilistic framework that integrates MFL signals with a Monte Carlo Dropout Uncertainty-Aware Neural Network (MCD-UANN) for quantitative corrosion detection. An automated indoor platform acquires 640 MFL samples from reinforced concrete specimens covering 32 corrosion states and four duct positions, each scanned at four angular orientations and summarized by an averaged waveform. The trained MCD-UANN achieves high accuracy (<i>R</i>² = 0.932) and reliable uncertainty quantification, with 92.9% of the measured corrosion degrees falling within two standard deviations <span></span><math></math> of the mean prediction <span></span><math></math>. Benchmarked against well-optimized Multilayer Perceptron, eXtreme Gradient Boosting, and Random Forest models, it attains equal or better accuracy while additionally separating the predictive uncertainty into aleatoric and epistemic components. Interpretability analysis confirms that the model captures physically meaningful MFL features consistent with magnetic dipole theory, bridging data-driven learning and domain knowledge. The quantified uncertainty is further shown to support inspection planning and maintenance prioritization so that the framework offers a robust, uncertainty-aware solution for reinforcement corrosion assessment.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2884415","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942438","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":"Control Algorithm for Distributed Multiple Semiactive Tuned Mass Dampers in Base-Isolated Buildings Under Earthquake Excitations","authors":"Setegn A. Kore,&nbsp;Daniel H. Zelleke,&nbsp;Saverio Spadea,&nbsp;Vasant A. Matsagar","doi":"10.1155/stc/3407994","DOIUrl":"https://doi.org/10.1155/stc/3407994","url":null,"abstract":"<p>This study presents a generalized mathematical formulation and performance evaluation of an energy-based predictive (EBP) control algorithm for mitigating the seismic response of base-isolated (BI) multistory buildings equipped with distributed multiple semiactive tuned mass dampers (DM-SATMDs). Although base isolation effectively limits superstructure accelerations, it often results in large isolator displacements, particularly under strong or near-fault ground motions. To address this limitation, the EBP algorithm—originally developed for a single semiactive TMD—is extended herein to an arbitrary number of semiactive units distributed across multiple floor levels. The coupled equations of motion for BI buildings with DM-SATMDs are derived, and a comprehensive parametric study is performed considering different isolation periods, TMD parameters, and number of stories under real earthquake records. Four configurations are examined: BI without TMDs, BI with a single SATMD (S-SATMD), BI with multiple SATMDs (M-SATMDs) placed on the same floor, and BI with DM-SATMDs. The proposed strategy is further evaluated through floor response spectra (FRS), comparisons with existing passive and semiactive control strategies, robustness analyses, and a nonlinear lead–rubber bearing model accounting for heating-induced lead-core strength degradation. The results show that the proposed control algorithm is generally more effective for taller BI structures across all evaluated parameters. Notably, the BI + DM-SATMDs configuration promotes an effective redistribution of seismic energy between the isolation system and the auxiliary dampers. Further, the proposed EBP-controlled DM-SATMDs largely outperform S-SATMD and conventional DM-PTMDs in mitigating FRS across all considered earthquake records, enhancing floor acceleration control and secondary systems performance. Overall, the proposed EBP-based DM-SATMDs framework offers an efficient and adaptable strategy for improving the seismic resilience of BI structures.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3407994","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942423","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}