Search Results (16)

The overturning resistance of curved single-column pier bridges has garnered increasing attention with the rise in infrastructure demands. However, aspects such as the secondary effects of overturning and the dynamic interactions between vehicles and bridges have not been fully explored. Hence, a refined finite element model incorporating Vehicle–Bridge Interaction (VBI) dynamics has been applied to a highway ramp bridge in this study, aiming to elucidate how VBI-induced vibrations contribute to bridge overturning and to develop effective reinforcement strategies for enhanced stability under eccentric loads. The analysis suggests that the rotation of the main girder, influenced by eccentric overload, is a significant factor in the overturning process. The initial overturning stability coefficient was found to be 0.948, pointing to potential areas for improvement. By implementing targeted reinforcement measures, specifically the addition of cover beams, the stability coefficient was improved to 2.626. The study provides insights into VBI-induced overturning in curved single-column pier bridges, offering a reinforcement strategy aimed at enhancing stability under eccentric loads. Full article

The reconstruction of bridge responses has been a significant area of focus within the field of structural health monitoring. This process entails the cross-reconstruction of responses from various cross-sections to identify any anomalies at specific locations, which may indicate the presence of structural defects. Traditional research has concentrated on simulating the relationships between different cross-sections for both high- and low-frequency components in isolation. However, this study introduces an innovative approach using a residual network (ResNet) to reconstruct high-frequency bridge responses under vehicular loading and demonstrates its applicability to low-frequency response reconstruction as well. The theoretical basis of this method is established through an analysis of the dynamics within a simplified vehicle-bridge-interaction (VBI) system. This analysis reveals that the transfer matrices for both high- and low-frequency components remain consistent across various loading conditions. Then, a data interception technique is introduced to separate high-frequency, low-frequency, and temperature-related components based on their spectral characteristics. The ResNet modeled the inter-sectional relationships of the high-frequency components and was then used to reconstruct the low-frequency responses under vehicular loading. The methodology was validated using a series of finite element models, confirming the uniformity of the transfer matrix between high- and low-frequency vibration components of the bridge. Field testing was also conducted to evaluate the practical effectiveness of the method. The proposed transfer–reconstruction method is expected to significantly reduce training dataset requirements compared with existing methods, thereby enhancing the efficiency of structural health monitoring systems. Full article

Damage detection is a critical aspect of bridge health monitoring. While data reconstruction has been posited as a promising method for damage detection, its effectiveness in this context has rarely been empirically validated. In this study, we introduce a novel approach to pinpoint potential bridge damage by reconstructing bridge inclination data. For an intact bridge, we selected reference cross-sections and trained multiple Backpropagation Artificial Neural Networks (BP-ANNs) to simulate transfer matrices for inclination between these base sections and other sections of the bridge. These BP-ANNs were then employed to reconstruct inclination data at the same cross-sections on a bridge with artificial damage. We demonstrated that damage localization is feasible through a comparison of the reconstructed and actual measured responses. The theoretical underpinnings of the transfer matrix and the damage localization method were initially elucidated through an analysis of the dynamics of a simplified vehicle–bridge interaction (VBI) system. A series of finite element models were constructed to substantiate the theoretical basis of the damage localization method. Additionally, a large-scale laboratory experiment was carried out to assess the practical effectiveness of the proposed method. The proposed method has been demonstrated to effectively pinpoint the location of potential structural damage. It successfully differentiates between areas in close proximity to the damage and those that are more distant. Compared to existing research, our method does not necessitate prior knowledge of factors such as mode shape functions, traffic conditions, or the constraint of inspecting with a single vehicle. This approach is anticipated to be more convenient for engineering applications, particularly in the development of online monitoring systems, due to its streamlined requirements and robust performance in identifying damage localization. Full article

Data fusion is an important issue in bridge health monitoring. Through data fusion, specific unknown bridge responses can be estimated with measured responses. However, existing data fusion methods always require a precise finite element model of the bridge or partially measured target responses, which are hard to realize in actual engineering. In this study, we propose a novel data fusion method. Measured inclinations across multiple cross-sections of the target bridge and accelerations at a subset of these sections were used to estimate accelerations at the remaining sections. Theoretical analysis of a typical vehicle-bridge interaction (VBI) system has shown parallels with the blind source separation (BSS) problem. Based on this, Independent Component Analysis (ICA) was applied to derive surrogate inclination mode shapes. This was followed by calculating surrogate displacement mode shapes through numerical integration. Finally, a surrogate inter-section transfer matrix for both measured and unmeasured accelerations was constructed, enabling the estimation of the target accelerations. This paper presents three key principles involving the relationship between the surrogate and actual inter-section transfer matrices, the integration of mode shape functions, and the consistency of transfer matrices for low- and high-frequency responses, which form the basis of the proposed method. A series of numerical simulations and a large-scale laboratory experiment were proposed to validate the proposed method. Compared to existing approaches, our proposed method stands out as a purely data-driven technique, eliminating the need for finite element analysis assessment. By incorporating the ICA algorithm and surrogate mode shapes, this study addresses the challenges associated with obtaining accurate mode shape functions from low-frequency responses. Moreover, our method does not require partial measurements of the target responses, simplifying the data collection process. The validation results demonstrate the method’s practicality and convenience for real-world engineering applications, showcasing its potential for broad adoption in the field. Full article

The intersectional relationship in bridge health monitoring refers to the mapping function that correlates bridge responses across different locations. This relationship is pivotal for estimating structural responses, which are then instrumental in assessing a bridge’s service status and identifying potential damage. The current research landscape is heavily focused on high-frequency responses, especially those associated with single-mode vibration. When it comes to low-frequency responses triggered by multi-mode vehicle loading, a prevalent strategy is to regard these low-frequency responses as “quasi-static” and subsequently apply time-series prediction techniques to simulate the intersectional relationship. However, these methods are contingent upon data regarding external loading, such as traffic conditions and air temperatures. This necessitates the collection of long-term monitoring data to account for fluctuations in traffic and temperature, a task that can be quite daunting in real-world engineering contexts. To address this challenge, our study shifts the analytical perspective from a static analysis to a dynamic analysis. By delving into the physical features of bridge responses of the vehicle–bridge interaction (VBI) system, we identify that the intersectional relationship should be inherently time-independent. The perceived time lag in quasi-static responses is, in essence, a result of low-frequency vibrations that are aligned with driving force modes. We specifically derive the intersectional relationship for low-frequency bridge responses within the VBI system and determine it to be a time-invariant transfer matrix associated with multiple mode shapes. Drawing on these physical insights, we adopt a time-independent machine learning method, the backpropagation–artificial neural network (BP-ANN), to simulate the intersectional relationship. To train the network, monitoring data from various cross-sections were input, with the responses at a particular section designated as the output. The trained network is now capable of estimating responses even in scenarios where time-related traffic conditions and temperatures deviate from those present in the training data set. To substantiate the time-independent nature of the derived intersectional relationship, finite element models were developed. The proposed method was further validated through the in-field monitoring of a continuous highway bridge. We anticipate that this method will be highly effective in estimating low-frequency responses under a variety of unknown traffic and air temperature conditions, offering significant convenience for practical engineering applications. Full article

Response reconstruction is essential in bridge health monitoring for recovering missing data and evaluating service status. Previous studies have focused on reconstructing responses at specific cross-sections using data from adjacent sections. To address this challenge, time-series prediction methods have been employed for response reconstruction. However, these methods often struggle with the inherent complexities of long-term time-varying traffic conditions, posing practical challenges. In this study, we analyzed the theoretical physical characteristics of high-frequency bridge dynamics within a simplified vehicle–bridge interaction (VBI) system. Our analysis revealed that the relationship between high-frequency bridge responses across different cross-sections is time-invariant and only dependent on the bridge’s mode shape. This relationship remains unaffected by time-varying factors such as traffic loading and environmental conditions like air temperature. Based on these physical characteristics, we propose the backpropagation artificial neural network (BP-ANN) method for response reconstruction. The validity of these physical characteristics was confirmed through finite element models, and the effectiveness of the proposed method was demonstrated using field test data from a continuous bridge. Our verification results show that the BP-ANN method enables effective utilization of short-term monitoring data for long-term bridge health monitoring, without necessitating real-time adjustments for factors such as traffic conditions or air temperature. Full article

The deflection dynamic load allowance (DLA) of stiff bridges with high piers requires sub-millimeter accuracy. New technologies such as the vision-based optical method and GNSS are not yet recognized for use in DLA measurements due to their smaller SNR. Presently, the scaffolding method is widely utilized for dynamic deflection measurements in dynamic load tests owing to the reliability of employing rigid contact. When scaffolding is not available, engineers have to resort to a suspension hammer system. However, the mass eccentricity of the hammer, stretched-wire length, and wind will decrease the measurement accuracy. To overcome these drawbacks of the suspension hammer method (SHM), a preloaded spring method (PSM) and the related stretched-wire-spring system (SWSS) are proposed in this paper. The dynamic deflection of the coupled vehicle-bridge-SWSS was obtained by vehicle-bridge interaction (VBI) analysis. The sensitivity parameters of the PSM were analyzed and optimized to minimize the measurement error. Indoor experiments and field dynamic load tests were conducted to validate the feasibility and accuracy of the PSM. Additionally, the differences in dynamic deflection measurements between the PSM and SHM in windy environments were compared. The results show that, in a windless environment, the DLAs of the PSM are affected by the spring stiffness, stretched-wire length, and stretched-wire section stiffness, independently of the preload force. When the wind speed is less than or equal to 8 m/s and the pier height is less than 30 m, the maximum deflection measurement error of the PSM is −2.53%, while that of the SHM is −15.87%. Due to its low cost and high accuracy, the proposed method has broad application prospects in the dynamic deflection measurement of stiff bridges with high piers. Full article

Vibration analysis of bridges induced by train loads is a crucial aspect of railway design, particularly considering the complexity of vehicle components such as bogie-suspension systems. Consequently, railway engineers have endeavored to improve the computational efficiency and applicability of train models using the finite-element method. This paper introduces a toolbox implemented in ABAQUS through a user-defined element (UEL) subroutine, which incorporates the vehicle–bridge interaction (VBI) element theory. This toolbox effectively handles diverse vehicle–bridge interaction systems. In the proposed theory, the wheel-track contact force is derived based on the bridge response, eliminating the need for an iterative process and significantly reducing computational workload compared to classical physics-based analysis. The presented approach is validated through a moving sprung mass model and a moving rigid bar model. Furthermore, a case study is conducted on a three-dimensional finite-element model of a high-speed railway bridge in China, based on a design sketch, to showcase the capabilities of the developed scheme. The study demonstrates the practical application of the proposed methodology in analyzing vehicle–bridge structures with high complexity. Full article

Constructional material deterioration and member damage can cause changes in the dynamic characteristics of bridge structures, and such changes can be tracked in the responses of passing vehicles via the vehicle-bridge interaction (VBI). Though data-driven methods have shown promising results in damage inspection for drive-by methods, there is still much room for improvement in their performance. Given this background, this paper proposes a novel time-domain signal processing algorithm for the raw vehicle acceleration data of data-driven drive-by inspection methods. To achieve the best data processing performance, an optimizing strategy is designed to automatically search for the optimal parameters, tuning the algorithm. The proposed method intentionally overcomes the difficulties in the application of drive-by methods, such as measurement noise, speed variance, and enormous data volumes. Meanwhile, the use of this method can greatly improve the accuracy and efficiency of Machine Learning (ML) models in vehicle-based damage detection. It consists of a filtering process to denoise the data, a pooling process to reduce data redundancy, and an optimizing procedure to maximize algorithm performance. A dataset is obtained to validate the proposed algorithm through laboratory experiments with a scale truck model and a steel beam. The results show that, compared to using raw data, the present algorithm can increase the average accuracy by 12.2–15.0%, and the average efficiency by 35.7–96.7% for different damaged cases and ML models. Additionally, the functions of filtering and pooling operations, the influence of window function parameters, as well as the performance of different sensor locations, are also investigated in the paper. The goal is to present a signal processing algorithm for data-driven drive-by inspection methods to improve their detection performance of bridge damage caused by material deterioration or structural change. Full article

The issue of monitoring the structural condition of bridges is becoming a top priority worldwide. As is well known, any infrastructure undergoes a progressive deterioration of its structural conditions due to aging by normal service loads and environmental conditions. At the same time, it may suffer serious damages or collapse due to natural phenomena such as earthquakes or strong winds. For this reason, it is essential to rely on efficient and widespread monitoring techniques applied throughout the entire road network. This paper aims to introduce an integrated procedure for structural and material monitoring. With regard to structural monitoring, an innovative approach for monitoring based on Vehicle by Bridge Interaction (VBI) will be proposed. Furthermore, with regard to material monitoring, to evaluate concrete degradation, a non-invasive method based on the continuous monitoring of the pH, as well as chloride and sulfate ions concentration in the concrete, is presented. Full article

A 3D coupled model to simulate vehicle–bridge interactions (VBI) to estimate its structural responses and impact factors (IMs) was developed in this study. By structural health monitoring (SHM) of a real concrete bridge, several data were collected to calibrate the bridge model by the finite element method (FEM). These models provide the bridge response in terms of vertical displacements and accelerations. VBI models provide reliable outputs without significantly altering the dynamic properties of the bridge. Modified recent analytical equations, which account for the effects of the asymmetric two-axle vehicles, were developed numerically. These equations, plus some proposed solutions, also quantified the vehicle response in terms of accelerations to estimate a more conservative driving comfort. The goal consisted in fitting the SHM with numerical and analytical models to find a more appropriate response for safety purposes and maintenance. From the codes and the literature, it was shown that a unique IM factor was not found. Moreover, most approaches underestimate the phenomena; in fact, results show that a monitored IM factor is 2.5 greater than IM from codes. Proposed equations for vehicle accelerations provided more conservative values up to about three times the standard comfort value. Full article

When vehicles interact with a bridge, a vehicle–bridge interaction (VBI) system is created. The frequency and modal shape of VBI systems have been widely studied, but the damping of VBI systems has not been adequately investigated. In recent years, several incidents of abnormal bridge vibration due to changes in bridge damping have occurred and aroused widespread concern in society. Damping is an important evaluation index of structural dynamic performance. Knowing the damping ratio of a VBI system is useful for analyzing the damping changes while a bridge is in service. This paper presents a method to extract bridge damping values from a VBI system, which can serve as a guide for bridge damping evaluation. First, a double-beam theoretical model was used to simplify the VBI system for cases involving uniform traffic flow. The damping ratio equation for the simplified VBI system was obtained using the extended dynamic stiffness method (EDSM). A double-beam finite element model and a VBI finite element model were established. The damping ratios of the two models were separately calculated and then compared with the simplified VBI model results. The results verified the accuracy of the simplified method. This paper then explains that bridge damping values can be extracted by estimating the equivalent traffic flow parameters and using the damping formula for the simplified VBI system. The bridge damping ratios extracted using this method in an engineering case ranged from 0.75% to 0.78%, which is smaller than the range that was directly identified using monitoring data (0.83–1.19%). The results show that the method can effectively extract bridge damping ratios and improve damping ratio identification. Full article

Based on virtual simulations of vehicle–bridge interactions, the possibility of detecting stiffness reduction damages in bridges through vehicle responses was tested in two dimensional (2D) and three dimensional (3D) settings. Short-Time Fourier Transformation (STFT) was used to process vehicles’ acceleration data obtained through the 2D and 3D virtual simulations. The energy band variation of the vehicle acceleration time history was found strongly related to damage parameters. More importantly, the vehicle’s initial entering conditions are critical in obtaining correct vehicle responses through the vehicle bridge interaction models. The offset distance needed before executing the vehicle–bridge interaction (VBI) modeling was obtained through different road profile roughness levels. Through the above breakthroughs in VBI modeling, the presented study provides a new and integrated method for drive-by bridge inspection. Full article

Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges. Full article

Fatigue damage of suspenders is a main concern during the life-cycle maintenance of arch bridges and suspension bridges. This paper presents a practical framework for estimating the fatigue life of suspenders under repeated traffic loads by taking a three-pylon suspension bridge as an example. First, the basic theory of vehicle–bridge interaction (VBI) is introduced and a finite element model of the bridge structure is established. Second, the fatigue load spectrum is defined in detail based on the analysis of WIM (weigh-in-motion) data. And then, parametric analysis is carried out to clarify the influence of road roughness, vehicle speed, and driving lanes. Among which, the time-dependent stress laws are simulated according to the defined fatigue load spectrum and the stress range is counted through the Rain flow counting method. At last, the fatigue life of uncorroded suspenders and naturally corroded suspenders is estimated by an S–N curve and Miner cumulative damage criterion. Results reveal that the fatigue life of suspenders is more than 100 years if no corrosion happens, while less than 20 years for short suspenders considering the influence of natural corrosion. Full article