Towards an efficient method of predicting vehicle-induced response of bridge

Sun, Zhen; Zou, Zilong

doi:10.1108/ec-02-2015-0034

Cited by 19 publications

(8 citation statements)

References 17 publications

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“…e theoretical development research is sorted out, and the impact factor was proposed by Deng [18] for vehicle-bridge interactions. Zhen Sun [19] presented a practical and efficient iterative method for predicting the vehicle-induced response of bridges. Milan Sokol presented the SHM (structural health monitoring) to use static and dynamic load tests to confirm the load-bearing capacity of the bridge with numerical simulations [20].…”

Section: Algorithms Of Vehicle-bridge Interactionmentioning

confidence: 99%

Study on the Technical Status of Old Concrete Truss‐Arch Bridge Based on Vehicle‐Bridge Interaction

Xie

2022

Advances in Civil Engineering

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With the rapid development of the country’s economy, a large number of heavy vehicles are being used due to the expansion of transportation demand and increased transportation costs. For the old concrete truss-arch bridges built in the last century, the bridges may hardly meet the modern traffic volume. With respect to old bridges in poor condition, it takes more time and resources to evaluate the technical status by field load tests, and there are some safety risks. A finite element method based on vehicle-bridge coupled vibration theory was proposed for evaluating the technical status of concrete truss-arch bridges in this paper. An old concrete truss-arch bridge in the suburbs of Suzhou, China, was selected as the research object. The field static and dynamic load tests were conducted and the results are consistent with the results of the numerical simulation based on the method proposed in this study. It can be concluded that the proposed numerical method can be widely used to assess the technical state of the old concrete truss-arch bridges in poor condition.

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Section: Algorithms Of Vehicle-bridge Interactionmentioning

confidence: 99%

Study on the Technical Status of Old Concrete Truss‐Arch Bridge Based on Vehicle‐Bridge Interaction

Xie

2022

Advances in Civil Engineering

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“…For example, a global–local FEM for the 1377‐m Tsing Ma Suspension Bridge 4 includes 17,677 and 406,448 elements for the global and local model, respectively. Moreover, various loading scenarios should be considered in the estimation of the service life of a bridge, including temperature, 5–7 wind, 8,9 railway, 10,11 and roadway traffic loads 12,13 . Such simulations involve high computational costs, even when introducing simplifications for different loads, which may significantly affect the accuracy of the estimated fatigue life of the bridge.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven prediction and interpretation of fatigue damage in a road‐rail suspension bridge considering multiple loads

Sun

Santos

Caetano

2022

Structural Contr & Hlth

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Summary In long‐span suspension bridges, fatigue is a significant concern for steel members as they are continuously under multiple effects such as wind, temperature, and traffic loads. Therefore, it is important to quantify the fatigue damage at critical details for effective maintenance countermeasures. This paper proposes an ensemble machine‐learning‐based method with physical interpretation, which predicts fatigue damage with monitored parameters of temperature, wind, train, and roadway loads. Interpretation of the machine learning model is achieved regarding the different contributions of the monitored parameters to the fatigue damage. Datasets from the structural health monitoring (SHM) system of a long‐span rail‐road suspension bridge are utilized to verify the proposed method. They are divided into 80% of training datasets and 20% of testing datasets. The ensemble machine learning model is established with Gradient Boosting Regression Tree (GBRT), and the fatigue damage for top chords on the upstream and downstream sides is predicted. The results are compared with other state‐of‐the‐art machine learning algorithms, namely, artificial neural network (ANN), support vector machine (SVM), decision tree (DT), and random forest (RF). It is found that GBRT demonstrates the highest accuracy for fatigue damage prediction with R2 of 0.9016 and 0.9180, respectively. Furthermore, interpretation of the results is carried out with feature importance estimates (FIE) and individual conditional expectation (ICE). FIE results indicate that the train load has the most significant influence on fatigue damage of the suspension bridge, and ICE results can provide insights on better maintenance plans to avoid severe fatigue damage.

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“…And the latter includes bridge responses such as displacement, strain, and acceleration at concerned locations. Owing to the SHM development, the behavior of long-span bridges has been captured under thermal loads, 15,16 strong winds, 17,18 vehicles, 19,20 trainloads, 21,22 or earthquakes. 23,24…”

Section: Introductionmentioning

confidence: 99%

Displacement response estimation of a cable-stayed bridge subjected to various loading conditions with one-dimensional residual convolutional autoencoder method

Lei

Siringoringo

Sun

et al. 2022

Structural Health Monitoring

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Displacement is an essential indicator of the functioning and safety of long-span cable-supported bridges under operational conditions. However, displacement estimation is challenging as these bridges are simultaneously subjected to various loading conditions such as temperature, wind, and vehicles. This article investigates an approach for estimating bridge displacement responses under multiple loads using a residual autoencoder model. Monitoring data of a cable-stayed bridge are collected to validate the proposed approach, including comprehensive measurements of the various loads and the displacement responses. Characteristics of temperature, wind, and vehicle loads are taken as the input, and the displacement responses at the mid-span of the main girder and top of the two pylons are taken as the output. The results showed the effectiveness of the proposed approach with an accuracy higher than 95%, which clearly outperformed other models such as long short-term memory networks in accuracy and efficiency. The effects of different types of loads are also investigated, and the wind load is found to be the most influential. Furthermore, multistep ahead prediction is carried out using the proposed approach, and good accuracy is achieved even 5 min ahead. The proposed approach can shed light on early warning of the malfunction of the bridge.

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Towards an efficient method of predicting vehicle-induced response of bridge

Cited by 19 publications

References 17 publications

Study on the Technical Status of Old Concrete Truss‐Arch Bridge Based on Vehicle‐Bridge Interaction

Study on the Technical Status of Old Concrete Truss‐Arch Bridge Based on Vehicle‐Bridge Interaction

Data‐driven prediction and interpretation of fatigue damage in a road‐rail suspension bridge considering multiple loads

Displacement response estimation of a cable-stayed bridge subjected to various loading conditions with one-dimensional residual convolutional autoencoder method

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