Reliability-based vehicle weight limit determination for urban bridge network subjected to stochastic traffic flow considering vehicle-bridge coupling

Chen, Shizhi; Feng, De‐Cheng; Sun, Zhen

doi:10.1016/j.engstruct.2021.113166

Cited by 27 publications

(6 citation statements)

References 39 publications

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“…There has been increasing traffic volume on roadways, 43,44 and the repetitive roadway traffic loads often induce more stress cycles, though the stress amplitude is usually smaller than that caused by train loads. Due to the lack of relevant data on roadway traffic loads, an innovative alternative approach is proposed to consider their effects indirectly.…”

Section: Input and Output Variablesmentioning

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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Section: Input and Output Variablesmentioning

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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“…Te safety of bridges is signifcant to ensure the functioning of the transportation network and economic development in our society. During their service life, bridges are subjected to various loads such as temperature [1], wind [2], trafc [3][4][5], and earthquakes [6], which aggravate the defects of bridges. Tese defects could cause bridge failures if they are not detected and repaired.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, periodic inspection bridge by bridge with visual and nondestructive techniques is widely adopted around the world, such as in the United States [9,10], Japan [11,12], Korea [13], China [14,15], Australia [16], and European countries [17]. In practice, conditions of bridges are rated into diferent levels [18], and the defcient bridges are given more attention for load-carrying capacity evaluation [19] or the implementation of a vehicle weight limit [4]. Condition rating is mainly performed based on the textual description of damages to bridge members such as girders, bearings, expansion joints, and piers, and then the results are combined to obtain the overall rating of the bridge.…”

Section: Introductionmentioning

confidence: 99%

Condition Assessment of Highway Bridges Using Textual Data and Natural Language Processing- (NLP-) Based Machine Learning Models

Feng,

Wang,

Mangalathu

et al. 2023

Structural Control and Health Monitoring

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Condition rating of bridges is specified in many countries since it provides a basis for the decision-making of maintenance actions such as repair, strengthening, or limitation of passing vehicle weight. In practice, professional engineers check the textual description of damages to bridge members, such as girders, bearings, expansion joints, and piers that are acquired from periodic inspections, and then make a rating of the bridge condition. The task is time-consuming and labor-intensive due to the large amount of detailed data buried in the inspection reports. In this paper, a natural language processing- (NLP-) based machine learning (ML) approach is proposed for automated and fast bridge condition rating, which can efficiently extract the information of deficiencies in bridge members. The proposed approach involves three major steps, say, data repository establishment, NLP-based textual data processing, and ML-based bridge condition rating prediction. The data repository is established with the inspection reports of 263 concrete bridges, and in total there, are four condition levels for the bridges. Then, the NLP-based textual data processing approach is implemented to calculate the word frequency and the word clouds to visualize the characteristics of bridges in different condition levels. Finally, four typical ML techniques are adopted to generate the predictive model of the bridge condition rating. The results indicate that the NLP-based ML prediction model has an accuracy of 89% and is very efficient so that it can be used for large-scale applications such as condition rating for regional-level bridges.

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“…Train WIM is of great interest in controlling axles and vehicle loads, detecting wheel defects and predicting train derailment in order to guarantee acceptable train operations and safety [1][2][3][4][5]. The presence of high loads in the track induces damage in the track components, such as rails, sleepers and ballast, which has implications in maintenance periodicity [6].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Weigh-in-Motion Methodology for Railway Tracks

Pintão

Mosleh

Vale

et al. 2022

Sensors

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In railways, weigh-in-motion (WIM) systems are composed of a series of sensors designed to capture and record the dynamic vertical forces applied by the passing train over the rail. From these forces, with specific algorithms, it is possible to estimate axle weights, wagon weights, the total train weight, vehicle speed, etc. Infrastructure managers have a particular interest in identifying these parameters for comparing real weights with permissible limits to warn when the train is overloaded. WIM is also particularly important for controlling non-uniform axle loads since it may damage the infrastructure and increase the risk of derailment. Hence, the real-time assessment of the axle loads of railway vehicles is of great interest for the protection of railways, planning track maintenance actions and for safety during the train operation. Although weigh-in-motion systems are used for the purpose of assessing the static loads enforced by the train onto the infrastructure, the present study proposes a new approach to deal with the issue. In this paper, a WIM algorithm developed for ballasted tracks is proposed and validated with synthetic data from trains that run in the Portuguese railway network. The proposed methodology to estimate the wheel static load is successfully accomplished, as the load falls within the confidence interval. This study constitutes a step forward in the development of WIM systems capable of estimating the weight of the train in motion. From the results, the algorithm is validated, demonstrating its potential for real-world application.

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Reliability-based vehicle weight limit determination for urban bridge network subjected to stochastic traffic flow considering vehicle-bridge coupling

Cited by 27 publications

References 39 publications

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

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

Condition Assessment of Highway Bridges Using Textual Data and Natural Language Processing- (NLP-) Based Machine Learning Models

Development and Validation of a Weigh-in-Motion Methodology for Railway Tracks

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