Structural Control & Health Monitoring最新文献

Damage identification (DI) methods using changes in static and modal flexibility (MF)–based deflections are effective tools to assess the damage in beam-like structures due to the explicit relationships between deflection change and stiffness reduction caused by damage. However, current methods developed for statically determinate beams require the calculation of mathematical scalar functions which do not exist in statically indeterminate beams and limit their application mainly to single-span bridges and cantilever structures. This paper presents an enhanced deflection-based damage identification (DBDI) method that can be applied to both statically determinate and indeterminate beams, including multispan girder bridges. The proposed method utilises the deflections obtained either from static tests or proportional defections extracted from output-only vibration tests. Specifically, general mathematical relationships between deflection change and relative deflection change with respect to the damage characteristics are established. From these, additional damage-locating criteria are proposed to help distinguish undamaged spans from the damaged ones and to identify the damage location within the damaged span. Notably, a span-similar virtual beam (SSVB) model concept is introduced to quantify the damage and make this task straightforward without the need to calculate complicated mathematical formulae. This model only requires information of the beam span length, which can be conveniently and accurately obtained from a real structure. The robustness of the method is tested through a series of case studies from a numerical two-span beam to a benchmark real slab-on-girder bridge as well as a complex large-scale box girder bridge (BGB). The results of these studies, including the minimal verification errors within five percent observed in the real bridge scenario, demonstrate that the proposed method is robust and can serve as a practical tool for structural health monitoring (SHM) of important highway bridges.

Existing dam displacement statistical methods simulate the thermal effects using simple harmonic functions ignoring the effects of ice periods, extreme heat, and seasonal weather. Moreover, existing data-driven methods usually utilize a separate modeling strategy, inevitably ignoring the spatiotemporal correlation of multiple displacement points in dams, resulting in poor predictive performance. To overcome these shortcomings, this study proposes a novel machine learning (ML)—aided multiple-point dam displacement predictive model considering the temperature hysteresis effect. Firstly, an improved hydraulic-Air_temperture_Time (HT_airT) statistical monitoring model is developed using the measured air temperature lagging monitoring data. On this basis, the multitask Gaussian process regression (multipoint GPR) algorithm with an improved kernel function to construct a multipoint deformation prediction model for ultrahigh arch dams. Then, the improved meta-heuristic physics-driven Frost algorithm is utilized to determine the optimal parameters of the multipoint GPR model. A high arch dam with a height of 305 m is used as the case study, and five displacement monitoring points are used for validation. Five advanced ML-based algorithms are used to comparatively evaluate and verify the performance of the proposed method in terms of forecast accuracy and interpretability. The HT_airT statistical model can better simulate the hysteresis effect of temperature on dam deformation. Moreover, the Frost-optimized dam multipoint displacement prediction model with the RQ kernel functions outperforms the other comparison methods in terms of R², mean absolute error (MAE), and root mean squared error (RMSE) evaluation indicators. This indicates the proposed method can mine the spatiotemporal correlation among multiple monitoring points of ultrahigh arch dams, further improving the overall deformation prediction and uncertainty estimation.

Passive vibration absorbers (PVAs) play a crucial role in mitigating excessive vibrations in engineering structures. Traditional PVA design typically begins with proposing a beneficial topological layout, incorporating stiffness, damping, and inertance elements, followed by optimal sizing of each element to minimise specific response of dynamically excited structures. An alternative approach involves first designing the impedance function of a PVA and then identifying a passive mechanical layout that replicates this impedance using network synthesis techniques. However, both methods struggle to identify the most efficient PVA layout using the minimum number of elements (referred to as “complexity”) for a given vibration suppression problem. To this end, this study introduces a graph-based methodology for designing optimal configurations (i.e., layout + sizing) of two-terminal spring-damper-inerter PVAs that achieve specified performance goals with minimum complexity. In this approach, a PVA is represented as a weighted coloured multigraph, enabling the application of a novel graph-based enumeration technique to generate the full set of potential layouts from any given number of mechanical elements. This enumeration is followed by a performance assessment of all layouts to pinpoint the optimal absorber configuration for the given problem. The methodology’s automation capability and versatility make it suitable for various civil and mechanical engineering applications. The effectiveness of the proposed methodology is demonstrated through two case studies: a vibration absorber design for a wind-excited tall building and a suspension design for a road vehicle. In both cases, the proposed methodology successfully identifies innovative PVA layouts that surpass traditional designs with minimum additional elements.

Bridge information models are essential for bridge inspection, assessment, and management. LiDAR technology, particularly UAV LiDAR, offers a cost-effective means to capture dense and accurate 3D coordinates of a bridge’s surface. However, the structure of large-scale bridges is complex, and existing commercial software still demands substantial manual effort to segment the components when constructing bridge information models for large-scale bridges. This study introduces a novel approach to automatically segment the components of a long-span rail-and-road cable-stayed bridge from the entire point cloud obtained through UAV LiDAR. In this proposed approach, the geometric and topological constraints of various bridge components are thoroughly examined, and a combination of the coarse-to-fine concept and top-down strategy is employed. The key structural elements, including piers, cable towers, wind fairing plate, stay-cable, main truss, railway surfaces, and deck surfaces, are identified and segmented. The proposed methodology achieves an average accuracy of over 96% at the point level validated using datasets acquired by UAV LiDAR.

Arch dam deformation has regional characteristics, and clustering is a common method of regional classification for arch dams. Traditional methods ignore the impact of dynamic changes in temperature and water level. Besides, the noise of deformation data is detrimental to mining potential information. The objective is to devise a dynamic cluster zoning method for arch dams, which considers the changing working conditions under the coupling of water level and temperature in this study. First, the deformation periods are classified by K-means clustering, and the arch dam deformation series are denoised using a sparrow search algorithm-optimized variational mode decomposition combined with wavelet threshold (SSA–VMD–WT) denoising method. The arch dam measuring points for different periods are then clustered. The engineering case study demonstrates that the SSA–VMD–WT denoising method improves the reliability of deformation data. The dynamic cluster zoning method reasonably describes the deformation regularity of the arch dam under different working conditions.

In experimental studies in geotechnical engineering, vibration with three degrees of freedom (DOFs), similar to that in an actual earthquake, needs to be reproduced in a centrifugal field. However, a suitable shaking table has not been developed. A general multi-DOF shaking table requires a complicated mechanism and a large installation space and is unsuitable for centrifugal fields. In this paper, the world’s first shaking table capable of three-dimensional motion in a centrifugal field was developed. The mechanical and control system requirements were defined, and the use of a Stewart platform mechanism consisting of six direct-acting hydraulic cylinders was proposed. An air spring was installed to offset the centrifugal force on the inertial mass, and a pressurized spherical bearing was used to withstand the excitation force of the actuator while maintaining more than two DOFs for the bearing. The shaking table could operate up to a maximum of 50 G and generate a maximum of 10 G in a single axis.

Conductor breakage with ice load is one of the major threats to the safe operation of transmission lines. The ice load increases the unbalanced longitudinal tension, leading to failure of tower members and even progressive collapse of the transmission line. This paper proposes an autotriggered anticollapse fusing hardware (AAFH), designed to reduce the unbalanced longitudinal tension caused by conductor breakage. When the longitudinal unbalanced tension of the transmission line exceeds the threshold of the AAFH, the fused part is destroyed, and the AAFH is elongated to reduce the longitudinal unbalanced tension. First, the construction and working mechanism of the device are introduced, and a numerical model of the transmission line–AAFH system is established to verify its effectiveness. Then, a parameter determination method for unbalanced tension in the tower-line system subjected to conductor breakage is proposed. In addition, the control performance of the device is studied. The results show that AAFH can effectively reduce the unbalanced tension induced by conductor breakage. The proposed method can predict the unbalanced tension of transmission lines, with an error within 10%. The greater the length of vertical/horizontal elongation, the better the protective effect. From a safety perspective, the AAFH should be designed according to the actual transmission line parameters to achieve an ideal control effect.

Machine learning (ML) methods have become increasingly prominent for predicting material and structural performance in civil engineering. However, these methods often require repetitive iterations and optimizations by professionals to obtain an optimal model, which are time-consuming and challenging for nonexpert users. In this paper, we propose an automated ML (Auto-ML) model using the tree-based pipeline optimization tool (TPOT) to address these limitations and streamline the performance prediction process. TPOT leverages genetic programming to optimize various ML models, including DT, RF, GBDT, LightGBM, and XGBoost, and to search possible models that fits a particular dataset, which cuts the most tedious parts of ML. To demonstrate the effectiveness of TPOT-based Auto-ML, two case studies are presented by using TPOT-based Auto-ML algorithms to construct prediction models for compressive strength of recycled micropowder mortar, and punching shear bearing capacity/failure mode of RC slab-column joints. To explain the “black box” of Auto-ML, Shapley Additive Explanation (SHAP) is introduced to interpret the best predictive models and rank the importance of influencing factors, providing a basis for material and structural design. Finally, a user interface (UI) for engineering applications is developed which enables end-to-end automation from data preprocessing to predictive results presentation.

The structural health monitoring system for offshore platforms exhibits anomalies in the collected monitoring data due to its prolonged service in complex and harsh environments. These anomalies significantly impede data analysis and early warning capabilities. In order to realize efficient and intelligent anomaly detection for the monitoring data, a method based on the vision transformer (ViT) model is proposed. Firstly, the monitoring data are transformed into image files by segmentation and visualization. Subsequently, the image features are analyzed to identify the anomaly patterns and construct an image database, so that the data anomaly detection problem is transformed into a classification problem based on the image features. Lastly, the ViT model combined with convolutional neural network (CNN) is constructed. The local perception ability of CNN is utilized to extract the underlying image features and smooth the image features inputted into the ViT model, which improves the accuracy of the model. Validation using actual monitoring data shows that the proposed method can efficiently detect multiple types of anomaly patterns in the monitoring data with an accuracy rate of 93.1%.

This study conducts a rigorous analysis of the moisture diffusion mechanism that undermines the adhesive layer of epoxy asphalt (EA) pavement on steel bridge decks, thereby fostering latent distresses. Furthermore, a novel and highly efficacious approach for detecting these concealed distresses is introduced. The findings of water vapor permeability tests conclusively reveal that the moisture diffusion coefficients of the upper and lower layers of the EA pavement stand at 0.1238 mm²/s and 0.0879 mm²/s, respectively, highlighting this disparity as the primary trigger for concealed issues like pavement delamination and swelling. Leveraging the combined capabilities of three-dimensional ground-penetrating radar (3D-GPR) and infrared thermography, this research reliably detects, identifies, and pinpoints concealed defects at three strategic locations on the steel bridge deck. The integration of these two technologies has exhibited remarkable proficiency in identifying concealed damages. Consequently, this study lays a substantial foundation for evaluating and detecting concealed distress in EA pavements atop steel bridge decks.