Structural Control & Health Monitoring最新文献

A smart concrete girder bridge usually has various sensors, based on which several physical properties can be measured, and hence, the health condition can be evaluated. Cracks are always observed on a smart concrete girder bridge. In particular, some of the cracks are induced by overloaded vehicles, which is dangerous to its safe operation. However, due to the crack opening and closing effect, it exhibits nonlinear responses, posing challenges for accurately assessing its health condition. Influence lines (ILs) are a promising indicator for bridge damage. However, there is limited research on the effect of cracks on the ILs of a smart concrete girder bridge. A digital twin is commonly used to accompany the smart sensing system to accurately evaluate the health condition, where the finite element (FE) model is of great importance. Therefore, this study proposes an element size–independent FE model construction method based on the concrete damage plasticity (CDP) model to investigate the changes of displacement and strain ILs of different types of smart concrete girder bridges with bending cracks, which is helpful to guide how to use the ILs to identify the cracks and evaluate the health condition. Initially, a concrete constitutive model based on crushing/fracture energy is proposed, and the evolution law of tensile damage based on fracture energy is derived to construct the element size–independent FE model. Subsequently, experiments on a reinforced concrete (RC) simply supported beam and a prestressed concrete (PC) simply supported bridge subjected to bending failure are used to verify the FE models constructed by the proposed method. Finally, the FE models of a smart RC T-beam bridge and a smart three-span PC continuous bridge are established to study the changes in ILs caused by bending cracks. The change of displacement IL at the midspan due to cracks for the smart RC bridge exceeds 10% when the reinforcements yield, while it is less than 10% for the smart PC bridge even if the bridge is in the failure state. The change of both displacement and strain ILs becomes greater when the measurement point approaches the cracks, and the change of strain IL is only detectable when the measurement is close to the cracks. Due to the crack opening and closing effect, the displacement and strain ILs of a smart concrete girder bridge with bending cracks are inconsistent when different loads are applied. The findings can also be used as a pre-IL-based crack detection using the passing inspection vehicle-induced dynamic response on a selection of type of ILs, determination of layout of sensors, and mass of inspection vehicle.

Deformation is a critical indicator for the safety control of high-arch dams, yet traditional statistical regression methods often exhibit poor predictive performance when applied to long-sequence time series data. In this study, we develop a robust predictive model for deformation behavior in high-arch dams by integrating signal dimensionality reduction with deep learning (DL)-based residual correction techniques. First, the fast Fourier transform is employed to decompose air and water temperature sequences, enabling the extraction of temperature cycle characteristics at the dam boundary. A data-driven statistical monitoring model for dam deformation, based on actual temperature data, is then proposed. Subsequently, an improved Bayesian Ridge regression model is used to construct the dam deformation monitoring framework. The residuals that traditional statistical methods fail to capture are input into an enhanced Long Short-Term Memory (LSTM) network to effectively learn the temporal characteristics of the sequence. A high-arch dam with a history of long-term service is used as a case study. Experimental results indicate that the data dimensionality reduction method effectively extracts relevant information from observed temperature data, reducing the number of input variables. Comparative evaluation experiments show that the proposed hybrid predictive model outperforms existing state-of-the-art benchmark algorithms in terms of predictive efficiency and accuracy. Additionally, this approach combines the interpretability of statistical regression methods with the powerful nonlinear modeling capabilities of DL-based models, achieving a synergistic effect.

The bearing capacity of the existing fine-grained soil subgrade is mainly achieved by measuring the modulus, and its testing methods can be divided into two categories including static method and dynamic method. The data connection between the two is still lacking in systematic research. The traditional BB (Benkelman Beam) static test has disadvantages such as slow detection speed, low accuracy, and poor reliability due to the use of deflection index, which is not fully applicable to the expressway. To this end, dynamic and static comparison tests were carried out and a rapid test method for subgrade bearing capacity based on Soil Stiffness Gauge (SSG) and Portable Falling Weight Deflectometer (PFWD) with dynamic modulus was proposed. To establish the connectivity of the static and dynamic modulus, modulus-dependent prediction model was developed. The results show that the multiplier power model with modulus ($$$$) is superior to the usual linear model (E_BB = 0.9415E_PFWD − 6.1507, R² = 0.9639; E_BB = 0.7878E_SSG − 0.4566,  R² = 0.8894), and it can replace the traditional BB method. PFWD and SSG were found to be reliable devices with faster and more accurate monitoring of the modulus change of the fine-grained soil subgrade. But they have the property of overestimating material modulus, the modulus ranking of the three instruments is obtained. In this way, it provides a reference for the dynamic and accurate determination and scientific evaluation of the bearing capacity of the highway subgrade.

The deformation of the main girder is an important manifestation of the overall stiffness of suspension bridges, which is essential for assessing bridge performance. Nevertheless, it is difficult to achieve satisfied prediction without fully considering the overall operational loads. To this end, this paper proposes a method to predict the deflection considering multiple operational loads using the monitoring data of a high-speed rail-cum-road suspension bridge. Initially, an improved Gaussian process regression (GPR) model utilizing Bayesian optimization was employed to predict the deformation of the main girder under the condition of nontrain loads. Furthermore, the distinct contributions of temperature, wind, and vehicle load were analyzed. Subsequently, based on the strain and deflection induced by train loads, the sum of sinusoids method was proposed to construct fitting and shape function (FSF) for predicting the main girder deformation under the influence of train loads. Ultimately, the deformation considering overall loads was obtained by adding the deformation under the nontrain and train loads, and the predicted deformation result was verified using the measured data. When compared to other state-of-the-art machine learning algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and decision tree (DT), the improved GPR demonstrates the highest accuracy in predicting the deformation of the main girder under nontrain loads with R² of 0.9478. In addition, the proposed sum of sinusoids FSF method accurately predicted the deformation of the main girder caused by train loads, with R² of 0.934. The deformation of the main girder under the influence of overall loads can lay a foundation for the early warning and evaluation of the suspension bridges.

To address the limitations in accuracy, reliability, and efficiency of traditional cable tension measurement methods, this paper proposes a cable tension assessment method based on 3D laser scanning technology that considers point cloud density. This study first employed a point cloud plane projection algorithm to reduce a 3D point cloud model to a 2D plane, fitting the actual cable shape by considering point cloud density. Subsequently, the parabolic and catenary cable mechanics models were derived to characterize the relationship between cable tension and shape based on force analysis of cable segments and differential segments. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm was applied to calculate cable tensions using the measured cable shape and the mechanic’s models, and the proposed cable tension assessment method was validated using practical cable point cloud models. Finally, the cable tension assessment method was applied to a specific sea-crossing bridge and compared with the traditional frequency method. The results indicated that the 3D laser scanning cable tension assessment method, considering point cloud density, could quickly and accurately identify cable tensions, offering greater accuracy, reliability, and efficiency compared to the traditional frequency method.

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.