Actual Bridge Research Articles

Structural analysis and optimization of steel truss bridge based on finite element method

In view of the defects of the current mainstream structural safety analysis method of Truss bridge, the safety analysis method at the component level should be further developed. Therefore, it is necessary to carry out the overall safety analysis and structural optimization at the structural level. Based on the concrete data of the actual steel truss beam bridge, this paper uses ABAQUS and MIDAS finite element software to model the truss structure. First of all, the static analysis is carried out, and then the structural analysis and optimization of the steel truss beam bridge is carried out with the elastic modulus reduction method. Based on the linear elastic finite element method, the bearing state of each component is solved, and the ultimate bearing capacity of the whole structure is calculated iteratively. Thus, the limitation of incremental nonlinear finite element method is overcome, and higher calculation accuracy and efficiency are realized.

Analysis on the synergistic variation law of pile refreezing process and bearing capacity based on high-strain dynamic test method

The high-strain dynamic test method possesses the attributes of speed, efficiency, and environmentally-friendly, low-carbon characteristics. To study the reliability of the high-strain dynamic test method in detecting the bearing capacity of pile foundation in permafrost regions, 4 test piles, each with a length of 15 m and a diameter of 1.0 m, were poured based on the actual bridge construction project in the permafrost region of Daxing'an Mountains in China. Based on the temperature data between the piles and soil, the refreezing state of the pile foundation was comprehensively judged. Subsequently, the static method was employed to assess the friction resistance values and single pile bearing capacity of each soil layer on the pile side under different freezing states before and after the pile foundation refreezing. Building upon these findings, the restrictive parameters for soil elastic limit q_{(i)}, soil resistance R_{U} (i) and other parameters in the pile-soil model of high-strain dynamic test method are clarified. The test and analysis results indicate that under the condition that the ground temperature of frozen soil is about − 1.9 °C, the pile-soil refreezing takes about 120 days, and the pile-soil refreezing is a slow process; In CAPWAPC calculation program, after the values of soil parameters were constrained by the results of static-load test, the calculated curve is in satisfactory agreement with the measured curve, and the parameters of the pile-soil model are reliable, which can be used for the rapid detection of the ultimate bearing capacity of piles in permafrost regions; In the process of pile-soil refreezing, the change of temperature field affects the freezing strength between pile and soil, the process of pile-soil refreezing is positively correlated with the increase of pile foundation bearing capacity.

Cross-correlation-guided operational modal identification of bridge considering asynchronous signals

The accurate identification of modal parameters is crucial for bridge health monitoring. In practical scenarios, there is often an asynchronization in the multi-channel vibration signals obtained from wireless sensors or offline measurements without temporal data. This asynchronization can lead to significant bias in the mode shapes when using the classical operational modal identification method. To address this issue, a new cross-correlation (CC) coefficient-based time lag detection is proposed. This technique aims to determine whether the multi-channel signals are synchronous and to prepare the reliably synchronous signals for modal identification. Because this detection technique specifically focuses on detecting the time lag between single degree-of-freedom (DOF) asynchronous signals, a multivariate variational mode decomposition (VMD) technique is developed to handle the multiple DOF signals of a bridge. To ensure that the decomposed components are not distorted or mixed, the penalty parameter in the multivariate VMD is optimized by minimizing the proposed similarity index. The proposed time lag detection process is validated using numerical and actual bridge examples. The results demonstrate that the new CC coefficient-based method is more effective than the classical CC coefficient or phase-based method, regardless of the employed mode or the amount of time lag present.

Influence of reduced drag load on stay cables on the construction cost of cable-stayed bridges: Two study cases

Rain–wind–induced vibrations (RWIVs) can cause the large-amplitude vibration of flexible cables in cable-stayed bridges; such vibration is one of the engineering safety problems that must be resolved. Currently, aerodynamic measures, such as dimples and spirals, are typically used as measures for suppressing vibration in stay cables. By calculating and comparing the equivalent static wind loads (ESWLs) of six typical cable-stayed bridges, the proportion of ESWL acting on the cable to that acting on the entire bridge increases approximately linearly with the bridge span. Hydrophobic coating can effectively suppress RWIVs and evidently relieve the ESWL. Hence, compared with the commonly used aerodynamic measures with surface roughness, this coating has a considerable potential as an alternative for suppressing vibration. Based on the foregoing, the wind resistance design optimization of two actual cable-stayed bridges with main spans of 420 and 1160 m is implemented to investigate the construction costs of cable wind load reduction. After optimization, the structural responses of the main towers are reduced by 4%–12%, and the optimized tower and foundation sizes are reduced by 5%–8%. The optimization for the two bridges exceed 5% and 7%, respectively. The construction investment savings are approximately 2.3 million and 7.2 million USD, respectively. These values further improve with increasing bridge span and design wind speed.

Modeling of Heat Transfer in Massive Concrete Foundations Using 3D-FDM

Analyzing and modeling the thermal behavior of mass concrete elements has been widely investigated by several researchers. Lately, many contemporary finite element packages have embedded modules for analyzing thermal behavior. Unfortunately, these packages are quite complex and require experts to be properly implemented. This paper proposes a simple and practical approach using the 3D-Finite Difference Model (3D-FDM) developed by MS-Excel to overcome the complexity of the other FE models. The model is utilized to predict the thermal behavior of actual bridge pile caps (3D model) rather than the previously developed 2D models in earlier research. The results of the analysis are compared with the concrete temperatures that were experimentally obtained from the site. Site data was collected using 18 thermocouple probes (K type) that were installed in two pile caps. These thermocouples were installed before concrete pouring to monitor the temperatures generated due to the exothermic reaction of the cement, which occurs during casting and the maturity period of concrete. The readings were registered every 3 hours for 7 days after concrete placement. This research provides a comparison between the recorded site data and the thermal analysis based on the proposed 3D-FDM. Results proved that concrete temperature time histories at different locations of the bridge pile caps could be properly predicted using the developed 3D-FDM. Doi: 10.28991/CEJ-2023-09-10-05 Full Text: PDF

Experimental study on fatigue behavior of UHPC joint in negative moment region of π-shaped steel–concrete composite beams

To improve the crack resistance of concrete bridge decks in the negative moment region of π-shaped steel–concrete composite beam, a novel ultra-high performance concrete (UHPC) joint was first proposed. Based on an actual bridge, a 1:2 scale model was then designed as the specimen. A fatigue test was conducted to investigate the variation of its structural properties during fatigue loading, and a static test was performed to evaluate its residual bearing capacity. The test results show that the new UHPC joint has good crack resistance under fatigue loading and can meet the engineering requirements. The stiffness of the specimen significantly decreases after 3 million fatigue cycles. However, without significant fatigue damage on the surface of specimen, plenty of stiffness is reserved. It is found that there is no significant slip between the concrete deck and steel girder, and between the UHPC and normal concrete in the static test after fatigue, indicating that the studs inside the π-shaped steel–concrete composite beam are arranged reasonably and the interaction of the overall structure is reliable. Furthermore, the ultimate bearing capacity of the specimen decreased by only 9.4% from its design value after being subjected to 3 million fatigue cycles. It is revealed that the application of UHPC in the composite beam effectively improves its crack resistance, resulting in a smaller reduction in bearing capacity. This paper can provide theoretical foundation for the preparation and application of the π-shaped steel–concrete composite beams with UHPC joints.

Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements.

This research aimed to improve the interpretation of electrical resistivity (ER) results in concrete bridge decks by utilizing machine-learning algorithms developed using data from multiple nondestructive evaluation (NDE) techniques. To achieve this, a parametric study was first conducted using numerical simulations to investigate the effect of various parameters on ER measurements, such as the degree of saturation, corrosion length, delamination depth, concrete cover, and the moisture condition of delamination. A data set from this study was used to build a machine-learning algorithm based on the Random Forest methodology. Subsequently, this algorithm was applied to data collected from an actual bridge deck in the BEAST® facility, showcasing a significant advancement in ER measurement interpretation through the incorporation of information from other NDE technologies. Such strides are pivotal in advancing the reliability of assessments of structural elements for their durability and safety.

Improving cable-stayed bridge longitudinal aseismic capability via fluid viscous damper parametric optimization and experimental investigation

The seismic vulnerability of Cable-stayed Bridges (CSBs) in high-earthquake intensity areas can be of significant concern to structural safety and resilience. A promising design practice for CSBs depends on decoupling the girder from the girder-pylon connection and involving energy dissipation devices such as fluid viscous damper (FVD) to mitigate the seismic responses. However, the parametric damper optimization algorithm and experimental validation on the FVD of a longitudinal aseismic system for CSB is still limited. In this study, three longitudinal aseismic systems (a semi-floating system with an elastic or damper connection, and a pylon girder consolidation system with a rigid connection) were investigated and compared based on the actual bridge Xigu Yellow River CSB erected in Lanzhou, China. The non-linear time history methodology was implemented to analyze the pylon and girder’s displacement and internal force responses, and the fluid viscous damper (FVD) aseismic system was the optimal choice considering the oval seismic performances. The optimal damping parameters were obtained by conducting parametrical analysis based on qualitative analysis and non-linear multi-function optimization in the pylon-girder and auxiliary pier-girder connections. A full-scale FVD model was designed and manufactured to conduct low velocity, constitutive law, and damping efficiency tests. The experimental results indicate that FVD achieved good energy dissipation capability and stability. FVD damping parameters were deduced according to the constitutive law test. The optimal analytical FVD damping parameter agreed well with the experimental results. Furthermore, the optimization was implemented in the final bridge design, referencing longitudinal aseismic systems for CSBs.

New Boca del Cerro Bridge at Section 1 of the “Tren Maya” railway

AbstractThe new Boca del Cerro Bridge is in southern Mexico at the exit of the Usumacinta River Canyon, in the city council of Tenosique in the state of Tabasco. A place of an extraordinary environmental value.This area is also a strategic crossing point, in which the actual Bridge over the Usumacinta River was built in 1950. It is a structure of extraordinary value, which is a milestone of the engineering of the time, with an upper arch of steel truss.The new Boca del Cerro Bridge has been designed, for the new Section 1 of the “Tren Maya” railway, parallel to the actual bridge. The solution adopted for the new bridge consists of a main span of double steel truss with double composite action (steel and concrete) and variable depth, that will be embedded in two lateral compensation spans of prestressed concrete.The new bridge has been designed in order to ensure a minimal “competition” and intrusion with the existing bridge and to enhance the beauty of the surrounding nature.The structural typology election has been conditioned by the advantages of its constructive process, that allows structure built‐up without riverbed support. To achieve this, the central span is planned to be erected by incremental cantilevers, carrying truss segments over the deck.

Research on hydration-caused thermal cracking risk of steel-concrete composite bridge pylons

Due to the large volume and different material of composite bridge pylons, a significant cracking risk has not yet been verified during concrete hydration. To clarify the hydration-caused temperature characteristics of concrete in composite pylons, based on an actual cable-stayed bridge, a 3D thermal-mechanical coupling model was established, demonstrating a good agreement between the FEM and measurements. On this basis, the temperature field and effects of pylon segments were analyzed. The key parameters influencing the temperature field, including the pouring sequences, hydration heat release, cement content, initial temperature, and convective coefficient，were analyzed. Results indicate that the temperature evolution can be divided into three stages: rapid warming, sharp cooling, and fluctuation. The temperature distributions exhibit high inside, low outside, and single-peak along the thickness direction. During the hydration process, the growth rate of the concrete elastic modulus is higher than its tensile strength. The maximum stress of concrete is 5 MPa, considerably higher than its tensile strength, leading to a significant cracking risk. Additionally, the maximum stress of the steel shell reaches 50 MPa. Adjusting the constructional parameters and environmental effects are the most effective improvement measure. The cracking risk of concrete can be reduced by 20% and 25% for every 20 kg/m3 and 50 kJ/kg reduction in the cement content and the cement heat release, respectively. Furthermore, reasonable methods for reducing unfavorable thermal effects are provided, which can facilitate the rational design and construction of composite bridge pylons.

Bridge Damage Detection Using Ambient Loads by Bayesian Hypothesis Testing for a Parametric Subspace of an Autoregressive Model

This study explores a change detection method in modal properties to automate and generalize in-service damage detection for vibration-based structural health monitoring of bridges. The noisy conditions caused by ambient loading pose difficulty for in-service damage detection because the load-induced noise often masks the difference in the modal properties. The proposed method directly converts measured time series into a simplified anomaly indicator robust against load-induced noise. This study adopts a vector autoregressive model to represent the vibration of bridges. Bayesian inference produces a posterior probability distribution function of the model parameters. Principal component analysis extracts a subspace comparable to the modal properties in the model parameters. Bayesian hypothesis testing quantifies anomalies in the extracted subspace. The feasibility of the proposed method is assessed with vibration data from field experiments conducted on an actual steel truss bridge. The field experiment includes damage severing the truss members. The modal frequencies and mode shapes estimated from the principal component analysis correspond well to earlier reported results. The proposed damage detection method successfully indicated all damage considered in the experiment.

Development of Data-based Hierarchical Learning Model for Predicting Condition Rating of Bridge Members over Time

Bridge maintenance strategy is implemented to ensure that effective decisions are made based on the evaluation of the current performance and predicted future conditions of a bridge. The current bridge maintenance system relies on traditional manpower-based methods to determine bridge performance. However, the present techniques employ a material deterioration model that has uncertainty in predicting bridge performance. Moreover, the utilization of collected bridge inspection-related data is insignificant, and related research is lacking. Accordingly, a new type of bridge deterioration model is proposed using state performance data based on bridge inspection. To formulate the deterioration model, bridge condition inspection data accumulated over a long period were utilized. The data have been continuously updated since 1992 by the US Federal Highway Administration and consist of basic information and various influencing factors of bridges. The developed model uses the long short-term memory (LSTM) algorithm (a type of recurrent neural network) as well as layer normalization and label smoothing to improve the applicability of fundamental data. For the stable application of data, predicting the model performance for up to 30 years every 2 years is possible. By implementing the many-to-many type of the LSTM algorithm, the predicted probability of each grade derived for each sequence was weighted and averaged with the grade value to derive a continuous state grade result. Thus, the proposed model can express discrete historical degradation indices in continuous form according to the service life. In addition, it enables the prediction of bridge performance using only the fundamental information regarding new and existing bridges. For the effective use of basic data, an optimization model was derived using preprocessing and various regularization techniques. Additionally, a feed-forward deep learning model and stochastic model were developed using the same data. For performance assessment, a regression analysis evaluation method was applied, and comparative analysis was performed using the inspection data of an actual bridge. The use of a time-series model enabled the continuous and stable prediction of bridge performance.

FATIGUE REAL SCALE TEST COMPARISON WITH ACTUAL BRIDGE MONITORING

In railway bridges, special attention needs to be paid to the interaction between the railway track and the bridge. Forces occur in the rails, as well as in the deck and the bearings of the bridge. These forces result in additional stresses, which need to be kept sufficiently low. Peak stresses due to e.g., temperature variations and bridge deck bending are especially to be feared at the bridge/abutment transition. Special rail fastening systems may be used to limit these additional stresses. Real scale tests were performed to verify the behavior of the track and the bridge equipped with such modified fasteners, which are designed to limit the transfer of longitudinal forces to the bridge. Based on the data from these tests, a detailed finite element model is constructed in this research paper. In contrast to the generally accepted modeling approach prescribed by international norms such as Eurocode NBN EN 1991-2 and UIC Code 774-3R, this research is performed, assuming the fact that the behavior due to track-bridge interaction can be simulated by a rail model supported by springs, i.e., without the need to model the actual bridge structure in detail. The results of these finite element model simulations are compared with data of a long-term monitoring program using strain gauges on all elements of the rails and support structure of an actual railway bridge in Belgium.

A case study of processing abnormal GNSS monitoring data of a cable-stayed bridge in Vietnam

Global Navigation Satellite System (GNSS) plays an important role in the real-time monitoring of superstructures, especially for long-span cable-stayed bridges. Currently, GNSS has been applied to integrate a Structural Health Monitoring system (SHMs) of many long-span cable-stayed bridges worldwide through its advantages in observing a large displacement of structures and monitoring the global deformation of bridges. However, some studies of actual GNSS monitoring data of a cable-stayed bridge showed that there are a lot of abnormal data occurrences such as missing data or several abnormal data. This paper investigates the application of some methods for processing the GNSS abnormal data acquired from an actual cable-stayed bridge in Vietnam. Firstly, a long-term monitoring dataset of an actual bridge was acquired for the study experiment. A clean-short dataset was used to investigate the accuracy and applicability of some methods in the interpolation of abnormal data, for example, linear formula, Moving Average, Artificial Neural Network, and Hampel Identifier. Some criteria are used to assess the difference between the interpolated data and actual data such as Root Mean Square Error (RMSE) or correlated coefficients with temperature data. Otherwise, the applied methods were then assessed their abilities and effectiveness in real applications, for instance, creating an automatic interpolated program of abnormal data.

Stochastic analysis for bending capacity of precast prestressed concrete bridge piers using Monte-Carlo simulation and gradient boosted regression trees algorithm

The use of precast prestressed concrete bridge piers is rapidly evolving and widely applied. Nevertheless, the probabilistic behavior of the bending performance of precast prestressed concrete bridge piers has often been overlooked. This study aims to address this issue by utilizing actual precast bridge piers as the engineering context. Through the implementation of the Monte-Carlo simulation and Gradient Boosted Regression Trees (GBRT) algorithm, the stochastic distribution of the bending performance and their critical factors are identified. The results show that the normal distribution is the most suitable for the random distribution of bending performance indicators. The variability of the elastic modulus of ordinary steel bars, initial strain of prestressed steel hinge wires, and constant load axial force has little effect on the bending moment performance, while the yield stress of ordinary steel bars, elastic modulus of concrete, compressive strength of unrestrained concrete, and elastic modulus of prestressed steel hinge wires have a greater impact on the bending performance. Additionally, the compressive strength of unrestrained concrete has a significant influence on the equivalent bending moment of the cross-section that concerns designers.

Remote Bridge Inspection and Actual Bridge Verification Based on 4G/5G Communication Environments

The close-up visual inspection of bridges faces several problems, including a lack of financial resources and human personnel. Hence, there has been increasing use of artificial intelligence (AI) and information and communications technology (ICT) to solve them. We previously investigated remote inspection—in which skilled engineers provided on-site support from a remote location—with the aim of reducing the labor required for on-site work and addressing the lack of personnel through the use of AI and ICT. Sharing images of bridges from inspection sites to remote locations via the Internet enables remote assessment of the sites and the ability to consider and diagnose damage. Mobile communications can be used to upload images, although the volume of image data required for inspection can be enormous and take considerable time to upload. Consequently, in this study, we investigated image uploads using 5G communication—that is, the fifth-generation technology standard for broadband cellular networks. Moreover, we measured the upload times when using 4G and 5G services and examined their operation based on differences in the communication environments. We concluded that the simulated remote inspection can be efficiently performed by adjusting the inspection method to the communication environment.

Damage detection of bridges subjected to moving load based on domain-adversarial neural network considering measurement and model error

Bridge damage detection methods based on moving load induced response and deep learning are popular, and they are effective when the training data generated by a finite element model (FEM) and the testing data measured from the actual bridge have the same distribution. However, their distributions are normally different due to the measurement and model error. This paper proposes a Domain-adversarial Neural Network (DANN) based damage detection method for bridge structures. The moving load induced displacement responses of the FEM and the actual bridge are taken as network inputs, and the feature space with the minimum differentiation between the edge distributions of the FEM (source domain) and the actual bridge (target domain) is extracted by adversarial training between the domain discriminator and the feature extractor. This feature space is a domain invariant feature of the source domain and the target domain and contains damage information only. Thus the damage label information is transferred from the source domain to the target domain for damage detection. Numerical and experimental examples manifest that the proposed method can reduce the distribution gap between the source domain and the target domain, and achieve higher damage detection accuracy than that of the traditional deep learning method by using a small number of sensors without a large amount of damage label data.

Normalising flow-based vehicle load modeling for bridges

Accurate vehicle load modeling is critical to the maintenance, reliability analysis and safety assessment of bridge structures. The weigh-in-motion (WIM) system is usually used to archive actual vehicle load by recording vehicle load variables including the number of axles, axle weight, total weight, vehicle length, inter-axle spacing, and vehicle speed. However, modeling multidimensional vehicle load variables considering their correlations remains a challenge. Normalising flow, as a probability estimation method based on deep learning, models vehicle load variables as a multivariate distribution so that not only their marginal distributions but also their correlation structure can be captured accurately. This article proposed a normalising flow-based method to model the vehicle load for bridges. The normalising flow-based vehicle load model learns from the actual vehicle load variables by maximising their log-likelihood and generates new samples of vehicle load. With the proposed vehicle load model and headway information, stochastic vehicle flow can be established. The results of the WIM data from an actual bridge show that the proposed method can model the vehicle load variables accurately and the stochastic vehicle flow based on the proposed method can accurately reflect the actual vehicle flow.

Feature-constrained real-time simultaneous monitoring of monocular vision odometry for bridge bearing displacement and rotation

The displacement and rotation are critical indicators of bridge's health, especially for the key components of the bridge such as bridge bearings. This study presents a real-time simultaneous measurement method of bridge bearing displacement and rotation to realize round-the-clock structure monitoring. The main contributions include: (1) A displacement and rotation simultaneous measurement algorithm and system is developed based on feature-constrained monocular visual odometry (VO), which uses image sequences of infrared array lamp targets with multiple feature constraints as visual input to construct the mathematical relationship between displacement and rotation and coordinate system transformation matrix. (2) A two-step parameter optimization strategy is proposed based on adaptive multi-window centroid array tracking and transformation matrix purification and refinement, effectively improving displacement and rotation measurement accuracy and robustness. The effectiveness of the proposed method is verified in the laboratory experiment and the actual bridge bearing monitoring of the Huangpu Pearl River Bridge.

Efficacy of Vehicle Scanning Methods in Estimating the Mode Shapes of Bridges Seated on Elastic Supports.

This study systematically assesses the efficacy of the vehicle scanning methods (VSM) in accurately estimating the mode shapes of bridges seated on elastic supports. Three state-of-the-art VSM methods are employed to obtain the mode shapes of bridges using the vehicle data during its travel. Two of the evaluated methods use a signal decomposition technique to extract the modal components of the bridge from the contact point of the response while the third one relies on the segmentation of the measured signals along the bridge deck and applying an operational modal analysis tool to each segmented signal to estimate the mode shapes. Numerical analyses are conducted on one single- and one two-span bridge, considering smooth and rough road profiles, different vehicle speeds, and presence of lead vehicle. The accuracy of the numerical models used in developing and assessing vehicle scanning models is tested, and the results of the study demonstrate the method using a half-car vehicle model and signal decomposition technique shows robustness against increasing vehicle speeds and road roughness while the method applying the segmentation of the measured signals provides relatively accurate mode shape estimates at the bridge edges at low speed, although the three methods have their limitations. It is also observed that simplified bridge and vehicle models can hide potential challenges that arise from the complexity of actual vehicle and bridge systems. Considering that a significant number of bridges worldwide are built on elastic supports, the practical success of vehicle scanning methods depends on their ability to handle elastic boundary conditions with reliability. Therefore, the article provides valuable insights into the capabilities and limitations of the current vehicle scanning methods, paving the way for further advancements and refinements in these techniques.