Influence of Fluid Viscous Damper on the Dynamic Response of Suspension Bridge under Random Traffic Load

Zhao, Yue; Huang, Pingming; Long, Guanxu; Yuan, Yangguang; Sun, Yi

doi:10.1155/2020/1857378

Cited by 7 publications

(8 citation statements)

References 15 publications

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“…The key parameters of traffic flow are given in the Table 7 , and the characteristics of traffic flow parameters could then be determined. In combination with the random traffic flow sampling method based on the Monte Carlo method, vehicle samples were obtained [ 28 ], so as to select the traffic flow in typical representative periods for a load effect analysis. The specific traffic flow simulation process is shown in Figure 19 .…”

Section: Corrosion Fatigue Of High-strength Steel Wirementioning

confidence: 99%

Corrosion Fatigue Degradation Characteristics of Galvanized and Galfan High-Strength Steel Wire

Zhao

Fan³

et al. 2023

Materials

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Cables are the main load-bearing components of a cable bridge and typically composed of high strength steel wires with a galvanized coating or Galfan coating. Galfan steel wire has recently started to be widely used because of its better corrosion resistance than galvanized steel wire. The corrosion characteristics of the coating and the difference in the corrosion fatigue process of the two types of steel wire are unclear. To further improve the service performance and maintenance of cable bridges, this study investigated the corrosion characteristics of galvanized steel wire and Galfan steel wire through accelerated corrosion tests and established a time-varying model of uniform corrosion and pitting corrosion of high-strength steel wire. Then, a long-span suspension bridge was taken as the research object, and the corrosion fatigue degradation of the two kinds of steel wire under a traffic load was analyzed on the basis of traffic monitoring data. The results showed that the uniform corrosion of the two types of steel wire conformed to an exponential development trend, the corrosion coefficient of galvanized steel wire conformed to the normal distribution, and the corrosion coefficient of Galfan steel wire conformed to the Cauchy distribution. The maximum pitting coefficient distribution of the two kinds of steel wire conformed to the generalized extreme value distribution. The location parameters and scale parameters of the two distributions showed an exponential downward trend with the increase of corrosion duration. When the traffic intensity was low, the corrosion characteristics of the steel wire was the main factor affecting its service life, and the average service life of Galfan steel wire was significantly higher than that of galvanized steel wire. Under a dense traffic flow, the service life of the steel wire was mainly controlled by the traffic load, and the service life of Galfan steel wire was slightly improved. Effective anti-corrosion measures are a key factor for improving the service life of steel wire.

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Section: Corrosion Fatigue Of High-strength Steel Wirementioning

confidence: 99%

Corrosion Fatigue Degradation Characteristics of Galvanized and Galfan High-Strength Steel Wire

Zhao

Fan³

et al. 2023

Materials

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“…Ref. [33] planned a computational tool for optimizing massive routes of transit networks that minimize transfers and optimize route openness through improved coverage of service. They tested the tool and functioned it to a significant and accurate network optimization drawback to the city of Miami.…”

Section: A Machine Learning-based Ridership Prediction For Public Tra...mentioning

confidence: 99%

Predictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of Transit-Oriented Development

et al. 2023

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The endeavors toward sustainable transportation systems are a key concern for planners and decision-makers where increasing public transport attractiveness is essential. In this paper, a machine-learning-based predictive modeling approach is proposed for metro ridership prediction, considering the built environment around the stations; it is in the best interest of sustainable transport planning to ultimately contribute to the achievement of Sustainable Development Goals (UN-SDGs). A total of twelve parameters are considered as input features including time of day, day of the week, station, and nine types of land use density. Hence, a time-series database is used for model development and testing. Several machine learning (ML) models were evaluated for their predictive performance: ridge regression, lasso regression, elastic net, k-nearest neighbor, support vector regression, decision tree, random forest, extremely randomized trees, adaptive boosting, gradient boosting, extreme gradient boosting, and stacking ensemble learner. Bayesian optimization and grid search are combined with 10-fold cross-validation to tune the hyperparameters of each model. The performance of the developed models was validated based on the test dataset using five quantitative performance measures. The results demonstrated that, among the base learners, the decision tree showed the highest performance with an R2 of 87.4% on the test dataset. KNN and SVR were the second and third-best models among the base learners. Furthermore, the feature importance investigation explains the relative contribution of each type of land use density to the prediction of the metro ridership. The results showed that governmental land use density, educational facilities land use density, and mixed-use density are the three factors that play the most critical role in determining total ridership. The outcomes of this research could be of great help to the decision-making process for the best achievement of sustainable development goals in relation to sustainable transport and land use.

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“…Han et al (2020) studied the fatigue durability of a long-span steel truss suspension bridge under the combined action of wind and traffic flow. Zhao et al (2020) analyzed the influence of the liquid viscous damper on the dynamic responses of a suspension bridge under traffic flow. Referring to the existing research frameworks, a traffic flow-bridge dynamic system is established considering the traffic simulation, road roughness, and dynamic coupling effects in this section.…”

Section: Traffic Flow-bridge Dynamic Analysis Systemmentioning

confidence: 99%

Dynamic amplification factor of multi-span simply supported beam bridge under traffic flow

Xia

Wang

Luo

et al. 2022

Advances in Structural Engineering

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Simply supported bridges occupy the majority of bridges. Compared with the flexible large span bridges, Dynamic Amplification Factor (DAF) of them is relatively large and attracts lots of studies. However, most of these studies are focused on the simplified condition that a single vehicle passes through a single span bridge. In general, the bridge bears complex traffic flow rather than a single vehicle. The vehicle type and dynamic loads on the bridge are thus dispersed. It is necessary to explore the multi-vehicle effects on the DAF caused by the complex traffic flow. Also, adjacent spans of a multi-span simply supported beam bridge may have continuous dynamic effects on the vehicles. In this regard, the DAF of a simply supported bridge considering the multi-vehicle and multi-span effects are explored numerically based on the coupling dynamic analysis of traffic flow and bridge, which can also consider the acceleration and deceleration of the vehicles. Cellular Automata (CA) method is used to simulate the traffic flow. It is found that the worse the driving condition of the road roughness, the greater the DAF. When road roughness is Class C, the DAF exceeds the values of American and Chinese codes. Under the action of traffic flow, DAF of the first span of the multi-span simply supported beam is larger than that of the span under the single vehicle. Only considering the effect of a single vehicle may underestimate the dynamic impact on the bridge. Sparse traffic flow has a larger DAF than moderate traffic flow and dense traffic flow in the statistic meaning of the multi-span, because the driving speed of sparse traffic flow is closer to the resonant speed of the bridge and the vibration disharmony of multi-vehicles is smaller. The greater the deceleration of the traffic, the greater the dynamic impact on the bridge.

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Influence of Fluid Viscous Damper on the Dynamic Response of Suspension Bridge under Random Traffic Load

Cited by 7 publications

References 15 publications

Corrosion Fatigue Degradation Characteristics of Galvanized and Galfan High-Strength Steel Wire

Corrosion Fatigue Degradation Characteristics of Galvanized and Galfan High-Strength Steel Wire

Predictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of Transit-Oriented Development

Dynamic amplification factor of multi-span simply supported beam bridge under traffic flow

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