Structural Control & Health Monitoring最新文献

This paper proposes a method for the evaluation of the combined system of heavy port cranes and the rails on which they run and demonstrates its success. Using techniques from railroad track health monitoring, we record the guided waves created in the rails from the movement of the wheels using laser-based vibrometry. In our novel approach, the signal is processed using discrete wavelet decomposition and dynamic wavelet fingerprints. This allows anomalies in the wheel or the rail to be found. The field measurements are verified using elastodynamic finite integration technique simulations. This methodology allows quick and safe evaluation without impacting cargo flow. We were able to identify tracks with corrugation damage.

Timely and efficient real-time surface damage detection is essential for maintaining the healthy operation of concrete bridges and has become a critical research focus. However, existing deep learning–based damage detection methods still face challenges such as low detection accuracy, poor adaptability, and limited applicability to diverse scenarios. To address these issues and enhance surface damage detection performance in complex environments, this study proposes an improved YOLODF model based on You Only Look Once, Version 5 (YOLOv5). The improvements include replacing the C3 module with the C2f structure with depthwise separable convolutions and inverted bottlenecks (DSIBC2f) module to build a new backbone network, DSIBCSPDarknet, which strengthens feature extraction capabilities. The SPPFCSPC structure is introduced to replace the spatial pyramid pooling fast (SPPF) module, enabling more effective multiscale feature fusion. Furthermore, the Enhanced Multidimensional Collaborative Attention (EMCA) is combined with the DSIBC2f module to construct a fused neck, FNeck, further optimizing feature fusion. Experimental results show that YOLODF significantly outperforms YOLOv5 in terms of precision, recall, F1 score, and mAP_0.5 and also surpasses the latest YOLOv12. Additionally, it demonstrates excellent damage detection capabilities in challenging scenarios, such as adverse weather, noise interference, and color variations. Despite a slight increase in computational load, YOLODF achieves a detection speed of 118 frames per second, demonstrating its high practicality for surface damage detection on bridges in complex environments.

This paper proposes a theoretical model correlating cable tension and frequency, incorporating the influence of intermediate transverse constraints. A theoretical vibration equation, considering these constraints, was derived to map the relationship between cable tension and frequency. Theoretical and numerical solutions for this equation were developed and validated. The impact of intermediate constraints on the cable tension–frequency relationship was subsequently analyzed. Results indicate that the theoretical numerical solutions provide accurate and efficient predictions for both single and multiple intermediate constraints, while the theoretical analytical solution is limited to single-constraint scenarios. Factors such as stiffness, position, and quantity of intermediate constraints significantly influenced the cable tension–frequency relationship, with these factors exhibiting coupled effects. At low constraint stiffness, the squared first-order frequency exhibited a linear correlation with cable tension, irrespective of constraint quantity or position. As stiffness increased, this relationship transitioned from linear to nonlinear, characterized by an initial convex upward curve before stabilizing into a linear segment for varying intermediate constraint configurations.

With the rapid development of structural health monitoring systems and sensor technologies for civil structures, various methods have been developed for condition monitoring and performance assessment based on measured structural response data. However, structural responses under working conditions are influenced by environmental and operational factors. Some of these factors are strongly correlated with structural responses, while others show weak or even negligible associations. This inconsistency often leads to data redundancy and limits the effectiveness of structural performance assessment methods. To address this issue, this paper proposes a unified Bayesian sparse Gaussian process (BSGP) model that integrates adaptive feature selection directly within a nonlinear regression framework. The proposed method can predict the structural response under different environmental conditions and prunes irrelevant environmental features simultaneously. The effectiveness of the proposed BSGP model is validated by both synthetic data and a year-long monitoring data of a full-scale bridge. The model successfully pruned all five redundant channels in the simulation and demonstrated higher prediction accuracy than a standard Gaussian process model in the bridge case study. Furthermore, the analysis reveals that the sensitivity of influencing factors such as wind speed and humidity is time dependent, showcasing the capability of the proposed method to support adaptive monitoring strategies. The proposed framework provides a robust tool for intelligent data analysis in SHM, enhancing model interpretability and offering guidance for optimizing monitoring system costs.

Tall-pier bridges are commonly employed in mountainous or deep-water regions. Ensuring the postearthquake serviceability is crucial for the seismic design of tall-pier bridges. Buckling-restrained braces (BRBs), serving as prevalent replaceable energy-dissipating components, can effectively enhance the seismic performance of such bridges. For the seismic design of tall-pier bridges, the objective is typically to minimize their structural responses under small to moderate earthquakes to maintain serviceability. Under rare or extremely rare earthquakes, efforts should be made to reduce seismic damage to the piers, thereby ensuring their postearthquake repairability. This study proposes a novel capacity-adjustable BRB (CABRB) to improve the seismic performance of double-column tall-pier bridges. The mechanical behavior of the CABRB is initially investigated through experimental studies and numerical simulations. Building upon these, a representative tall-pier bridge is selected as the research object. Comparative analyses of its seismic performance are conducted under three distinct pier configurations: prototype pier, BRB pier, and CABRB pier. The results indicate that the installation of CABRBs effectively reduces the displacement response at the pier top and the curvature response at the pier base, significantly enhancing the seismic performance of tall-pier bridges.

Distributed tuned mass dampers (DTMDs) have been widely employed for multimodal seismic response control of structures with closely spaced natural frequencies. However, the traditional mode-by-mode design approach neglects the coupling effects between modes and the synergistic effects among tuned mass dampers (TMDs), making it difficult to effectively control the multimodal seismic responses of these structures. This study proposes a global optimization method for the design of DTMDs to address this challenge. A typical three-degree-of-freedom (3DOF) structure with closely spaced frequencies is used to establish the governing equations of the 3DOF-DTMDs coupled system. The optimal parameters of the DTMDs are determined via the global optimization method and compared with the mode-by-mode approach. Subsequently, complex modal analysis is conducted to investigate the influence of DTMDs on the structural modal characteristics. Finally, the effectiveness of the proposed method is validated in both the frequency and time domains, and the effects of mass ratio and the interactions among individual TMDs on the control performance are further investigated. The results show that DTMDs designed using the global optimization method achieve superior vibration suppression compared to the mode-by-mode design approach, without altering the frequencies, damping ratios, or mode shapes of the uncontrolled modes. It is worth noting that there exists an interaction among the DTMDs, and a rational allocation of mass ratios is crucial for achieving optimal control.

The detection and segmentation of dam cracks are crucial for water conservancy project maintenance and safety assessment. However, underwater image often suffers from poor lighting and noise, causing blurred edges and low contrast. This increases the difficulty of segmentation. Additionally, cracks have complex shapes and varying scales, making it hard for existing methods to capture fine details. To address these issues, we propose a new crack instance segmentation framework. First, in the feature extraction stage, we propose a multiscale optimization module (MSOM). This module adjusts the receptive field’s shape and size based on the input image, improving the extraction of crack features at different scales. Then, we introduce a spatial feature pyramid aggregation FPN (SFPAFPN) to enhance multiscale feature fusion with a redesigned fusion strategy. We also design a dynamic perception convolution module (DPC), which optimizes sampling point distribution through a flexible sampling mechanism, improving the segmentation of crack edges and fine details. Experimental results show that our method performs well in underwater dam crack segmentation and underwater object detection, with AP₅₀ in mask reaching 52.9% and 41.5%.

Following an earthquake, roads must be opened as soon as possible for emergency transportation. Therefore, the damages to road bridges must be quickly and accurately assessed and their structural safety and serviceability must be confirmed. For this purpose, current post-earthquake inspections are first conducted by vehicle patrols, followed by hands-on inspections in case of irregularities on the road surface. However, this inspection method is time-consuming. Despite this, in post-earthquake inspections, the safety of structures must be accurately diagnosed and fatal damages must not be overlooked. Therefore, inspection methods must be highly reliable. To clarify the scope of application where the quality of inspection results is guaranteed, this study examined the correspondence between measurement conditions and quality and proposed a concept for evaluating this relationship. Based on this concept, we present a method to evaluate the extent of damage based on images captured by unmanned aerial vehicles (UAVs) that are utilized for post-earthquake inspections. Experiments were conducted to verify the deformation detection accuracy of UAVs. The results indicated that at a distance of 2 m, cracks ≥ 0.3 mm, deformation angles ≥ 1°, and movements ≥ 2 mm can be confirmed. This conformity evaluation, which was also confirmed by cross-checking with the actual damages, demonstrates the potential of this technology for practical applications.

Accurate estimation of structural displacement is crucial for assessing the damage state of structures. However, displacement sensors are rarely deployed on practical structures. Moreover, the traditional integration method based on acceleration data entails sophisticated processing steps, such as multiple filtering and baseline correction procedures. Therefore, an end-to-end displacement prediction method based on the long short-term memory (LSTM) model integrated with an attention mechanism is presented herein to mitigate the limitations. The proposed model can predict structural displacement using noisy acceleration data acquired from accelerometer sensors mounted on structures. The overlapping window technique was employed to augment the dataset and enhance training efficiency. Furthermore, an attention mechanism was incorporated to improve prediction accuracy. When compared with the traditional integration method and the LSTM model without the attention mechanism, the proposed Attention-LSTM model exhibited superior performance on the testing dataset. Additionally, the effects of key parameters were systematically investigated, such as the normalization method, cropping window size, number of LSTM layers, and number of units per layer. It is demonstrated that the proposed Attention-LSTM model possesses robust generalization capability and antinoise performance, which contributes to the reliable estimation of structural displacement in engineering structures.

Slow-moving landslides along reservoir banks often act as precursors to catastrophic failures, which could lead to significant risks to human lives and critical infrastructures. Fortunately, interferometric synthetic aperture radar (InSAR), with its wide-area, lightweight, and all-weather monitoring capabilities, provides a promising method for effectively forecasting such events. Small baseline subset (SBAS) InSAR, utilizing a combination of multiple master images and short baselines, efficiently obtains adequate coherent points from the site surface. This study measured surface deformation in Suijiang County using a total of 202 ascending and 199 descending Sentinel-1 images, spanning the period from 2014 to 2022. The SBAS method with the Generic Atmospheric Correction Online Service (GACOS) data is used to analyze the time-series deformation in Suijiang County, and the results are interpreted by integrating the monitoring data of ascending and descending orbits. The monitoring results indicate significant deformation in the study area, primarily occurring before the implementation of the geotechnical treatment project. In the procedure of geological treatment, the deformation rate of the site tends to converge. It is found that both precipitation and high reservoir water levels were the triggers of surface deformation. Furthermore, the spatiotemporal evolution of the deformation zone was examined using historical data. Finally, the structural damage level is assessed by analyzing the deformation field of the building. The results demonstrate that accurate building safety evaluations necessitate integration of prior information. This study provides an important case reference for the analysis, identification, and prevention of slow-moving landslides and subsequent disasters on reservoir banks and similar infrastructures.