Damping ratio is a fundamental dynamic parameter of the structure. Recent monitoring of cable‐stayed bridges has shown that the damping ratio changes under different operating conditions. In general, the damping ratio of a structure can only be evaluated after the structure is built, and it is still challenging to theoretically calculate the damping ratio of a structure beforehand. To further understand the mechanism of the bridge damping ratio, this paper proposes an evaluation method based on the damping dissipation function. The effects of the initial and modal strain energy of the structure on the damping dissipation are considered. The damping dissipation function of substructures of a laboratory cable‐stayed bridge model was tested, and the structural system damping ratio was evaluated. Furthermore, the theoretical discussion and experimental verification of the effect of support friction on the damping ratio were conducted. The results indicate that the damping ratio of the substructure varies with both the vibration amplitude and the initial stress level. Consideration of the initial strain energy during testing the substructure damping dissipation function can lead to more accurate results in the evaluation of the damping ratio of the structural system. In addition, support friction also significantly influences the damping ratio of the longitudinal drift and vertical bending modes of the structure.

Structural damage identification (SDI) serves as an indirect approach that has the potential to meet real‐time monitoring of structures. However, the identification accuracy and efficiency of some methods need to be improved, especially when there are some uncertain interfering factors or noise. This paper presents a new optimization algorithm and an improved objective function for inverse problems of SDI, offering an effective solution for bridge damage identification under uncertain noise interference and incomplete modal data. In this study, by hybridizing the whale optimization algorithm and the sand cat swarm optimization, a novel whale‐sand cat swarm optimization (W‐SCSO) method is proposed for SDI. The cubic chaotic mapping is introduced for initialization of the W‐SCSO method, and then the lens opposition‐based learning and the stochastic differential mutation are employed to enhance the search capability and convergence accuracy of the proposed algorithm. Besides, the mode shape curvature, the frequency change ratio, and the L 1/2 sparse regularization are used to improve the objective function. Four other existing state‐of‐the‐art methods are used to verify the performance of the proposed W‐SCSO method by the CEC2017 benchmark functions and a simply supported beam finite model. The comparative analysis highlights the feasibility and effectiveness of the proposed method in the considered cases. Moreover, an aluminum alloy simply supported beam was conducted for the SDI experiment to further prove the effectiveness of the improved method in practice. Simulation and experimental results show that the proposed method effectively locates and quantifies stiffness reduction in bridge structures, which maintains high accuracy in damage identification despite potential modal incompleteness and uncertain measurement noise interference.

Replaceable systems, including the main structure and components, are the primary means of building a resilient city. However, due to the complex interaction relationships, the energy‐dissipation effect of such structural systems is not ideal. Accordingly, this study used a replaceable corrugated steel plate composite shear wall as an example, focusing on the seismic performance and energy‐dissipation ratio analyses of each component. We attempted to find a suitable stiffness ratio index to determine the interaction relationship of the replaceable corrugated steel plate shear wall. An energy‐dissipation evaluation method is proposed based on the stiffness ratio index, and the bearing‐capacity formula of the shear wall was modified. The results show that the revised lateral‐bearing‐capacity formula based on the energy dissipation evaluation exhibited ideal accuracy and could provide a reference for practical engineering applications.

The main girders of suspension bridges experience significant deformation due to temperature variations, wind dynamics, and vehicle loads, causing movement at the girder ends and friction among components such as bearings, expansion joints, and viscous dampers. Early warning of the component anomaly is vital for preventive maintenance. This paper develops a two‐stage framework for predicting girder end displacement to facilitate anomaly detection. First, a Bayesian neural network is employed to predict girder end cumulative displacement, accounting for uncertainties inherent in the prediction process. Second, an anomaly detection algorithm utilizing a Mahalanobis distance–based approach is implemented to provide warnings to operations based on both measured and predicted data. The effectiveness of the proposed approach is validated using data collected from multiple loads and displacement responses of a suspension bridge. The analysis reveals that the GEV distribution is highly proficient in capturing the underlying pattern of the cumulative displacement indicator, enabling the establishment of an appropriate threshold. This method proves successful in identifying anomalies in critical components such as viscous dampers, enhancing predictive and preventive maintenance practices and contributing to the longevity and safety of bridge infrastructure.

The deformation of concrete dams directly reflects their structural health and operational state, serving as a critical foundation for safety assessment and early risk warning. Accurately predicting dam deformation patterns is thus essential for ensuring long‐term structural safety and enabling scientific operation management. However, existing models remain limited in addressing high‐frequency noise in monitoring data, performing dynamic feature selection, and modeling complex spatiotemporal dependencies, which collectively constrain prediction accuracy. To overcome these challenges, this study proposes a dam deformation prediction model that integrates variational mode decomposition with wavelet thresholding (VMD–WT), a partial autocorrelation function (PACF)–based dynamic feature selection approach, and the ScaleGraph Block‐Mamba‐like linear attention (SGB–MLLA) –Transformer. The proposed model performs multiscale signal decomposition to suppress noise and extract dominant deformation trends, while dynamically selecting key influencing factors and incorporating spatial dependency modeling and lightweight attention mechanisms to enhance the representation of long sequence and multifactor coupled deformation features. To validate the model’s effectiveness, deformation data from monitoring points of a concrete dam in Jiangxi Province, China, were used for evaluation. Experimental results demonstrate that the proposed model achieves superior prediction performance across multiple monitoring points, achieving near‐perfect accuracy ( R 2 = 0.9993) with submillimeter error margins at GLD4, significantly outperforming existing models. These findings confirm that integrating frequency‐domain decomposition with adaptive feature selection and employing linear attention for efficient long sequence modeling can substantially improve deformation prediction accuracy. This research provides a novel methodological framework for dam health diagnosis and safety management, offering both theoretical and practical value for the development of intelligent dam monitoring systems.

A continuous welded rail (CWR) is a critical component of modern rail systems, providing increased stability, improved passenger comfort, and reduced maintenance compared with jointed rails. However, the unique mechanical properties of CWR, particularly in the immovable zone where friction restricts longitudinal deformation, require accurate and continuous monitoring to prevent rail buckling or fractures. Despite the availability of various strain‐monitoring technologies, including fiber Bragg grating sensors, strain gauges, and vision‐based systems, these approaches have significant limitations in full‐scale CWR applications. Challenges such as insufficient resolution for detecting minute strains and sensor‐adhesion durability reduce the effectiveness of current strain‐monitoring solutions. To address these limitations, we propose a high‐resolution, vision‐based biaxial strain measurement system specifically designed for CWRs. This system utilizes three microscopic cameras strategically positioned to capture detailed displacement data, allowing for accurate computation of biaxial strain through advanced image processing techniques. The proposed system was validated through both laboratory‐scale and full‐scale experiments and exhibited a minimum detectable strain of 1.5 µε under controlled loading conditions.

In this study, an automated transmissibility‐based procedure for damage detection is developed for output‐only systems. The application of transmissibility has been previously investigated for damage detection. Despite the advancements, current techniques are not applicable in a general way, as vast experience or expert knowledge is needed to achieve accurate results, particularly for the frequency range selection. Moreover, the extent of noise influence still needs to be adequately addressed. A novel procedure has been developed to resolve these issues. First, the frequency range is determined by applying modal coherence using the first singular value of the cross‐power spectral density (CPSD). Then, the transmissibility functions are calculated from the CPSD and smoothed using a moving mean approach to reduce the influence of noise. Afterward, the threshold is obtained from the transmissibility damage indicator values of the system’s healthy state. Finally, damage detection can be performed continuously for each subsequent dataset. The procedure is compared to damage detection based on eigenfrequencies and mode shapes using simulated data, demonstrating higher sensitivity to minor damages at low noise levels. Furthermore, the procedure is validated on experimental data from a steel cantilever beam, where various noise scenarios, damage severities, and damage positions are considered, and on field data from a lattice tower, showing high damage detection accuracy across three damage scenarios. The proposed procedure can be automated, demonstrating sensitivity to minor damages when high signal‐to‐noise ratio is available.

Due to its simple and beautiful architectural appearance, the stress‐ribbon bridge (SRB) has been gradually built around the world as a pedestrian or traffic bridge. However, as characterized by low bending stiffness and low damping ratio features, SRB is prone to the dynamic effects of external excitations, such as pedestrians, vehicles, and/or winds. To control the vertical vibration of the SRB, a rail‐damper system is proposed in this study. In the proposed scheme, the rotation of the handrails triggered by the flexural deformation of the SRB is utilized to drive the viscous dampers installed between the adjacent handrails. The governing equations of the proposed control system are established. The key design parameters and their influences on the dynamic properties of the control system are systematically investigated. The control performances of the proposed rail‐damper system are further investigated through an SRB numerical model subjected to pedestrian excitations. It is discovered that the rail‐damper system can offer considerable supplemental damping to the structural modes through reasonable design, achieving satisfactory control performances. To gain the excellent effect of the proposed rail‐damper system in real applications, a nondimensional rail stiffness of no less than 1000 is recommended, and the stiffness of the damper should be controlled as small as possible.

BIM is increasingly crucial for the building construction, yet deviations from actual construction limit its construction monitoring applications. Therefore, this paper proposes a BIM‐construction interaction method that integrates parametric modeling, point cloud processing, and parameter optimization and fitting to enhance the construction monitoring. Proposed parametric modeling methods equip BIM elements with the ability of the pose adjustment and deformation modification, addressing potential deviations during construction. Developed component feature extraction algorithms efficiently capture pose and deformation features from the point cloud model that sufficiently and accurately reflect the actual state of the structural components. The proposed parameter optimization and fitting approach targets model parameters for optimization and aims to match pose and deformation features for fitting. By constructing objective functions that quantify the deviation between the BIM and point cloud models, the process is driven by the RBFOpt optimization algorithm and Opossum optimization solver. This approach enables the automatic updating of the design BIM into the as‐built BIM and generates deviation data between the two models, providing a basis for comprehensive construction monitoring results. The BIM‐construction interaction method was applied to the core area construction of the Shanghai Grand Opera House, where it reduced the root mean square error (RMSE) between the parametric BIM and point cloud models of the core column, concrete thick shells, and cantilever beams from 0.0352 m, 0.0411 m, and 0.0323 m to 0.0082 m, 0.0323 m, and 0.0053 m, respectively, significantly reducing deviations between BIM and the actual construction. Comprehensive and quantitative construction monitoring data, including pose deviations and structural deformations, were obtained to assess the precision and safety of the core area construction. The results demonstrate that the BIM‐construction interaction method effectively supports the interaction between BIM and construction, enabling monitoring and evaluation based on point cloud data.

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.