&lt;b&gt;Estimation Method of Seismic Damage of Bearings at Railway Structure by Displacement Sensor&lt;/b&gt;

Onodera, Meguru; Toyooka, Akihiro; Yoshida, Yoshinori; Hayashi, G; TAKAHASHI, Hiroju

doi:10.2219/rtriqr.63.1_64

Cited by 2 publications

(1 citation statement)

References 1 publication

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“…This line section comprised mainly bridges and viaducts, and the behaviors of these respective structures was evaluated by the equivalent single degree of freedom model used in seismic design. Here, the equivalent natural period T eq and yield seismic intensity k hy were set using the database [3] calculated in advance for structures with various specifications. We assumed that steel rod stoppers and steel angle stoppers were used for the bearings of these bridges and viaducts, and 12-40-N9B for electric poles is used for all lines.…”

Section: Outline Of Target Line Sectionmentioning

confidence: 99%

<b>Method for Updating Operational Regulation Standards Considering Seismic Risk of Railway Facilities</b>

Wada¹,

Sakai²,

Toyooka³

2023

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The regulation values of seismic intensity to suspend a train operation after earthquakes are determined empirically based on past disasters. Therefore, it is difficult to reflect the effects of seismic countermeasures in the operational regulation standards. To resume train operations as quickly as possible after earthquakes, this study proposes a method for updating the operational regulation standards based on risk analysis. In the proposed method, the seismic risk of each facility is evaluated and used to determine the regulation values for suspending train operations. Using this method, the effects of seismic countermeasures can be directly reflected in the operational regulation standards.

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Section: Outline Of Target Line Sectionmentioning

confidence: 99%

<b>Method for Updating Operational Regulation Standards Considering Seismic Risk of Railway Facilities</b>

Wada¹,

Sakai²,

Toyooka³

2023

QR of RTRI

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Estimating the occurrence of broken rails in commuter railroads with machine learning algorithms

Kang,

Dai,

Liu

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part F:

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Broken rail prevention is critical for ensuring track infrastructure safety. With the increasing availability of rail data, the opportunity for data-driven analyses emerges as a promising avenue for enhancing railroad safety. While previous research has predominantly concentrated on predicting broken rails within the context of freight railroads, the attention afforded to commuter railroads has been limited. To address this research gap, this paper presents an analytical modeling framework based on machine learning (ML) algorithms (including LightGBM, XGBoost, Random Forests, and Logistic Regression) to investigate the occurrence of broken rails on commuter rail segments. It leverages various features such as gradient, curvature, annual traffic, operational speed, and the history of prior rail defects. We use oversampling techniques, including ADASYN, random oversampling, and SMOTE, to address the issue of imbalanced data. This challenge arises due to the majority of commuter rail segments not experiencing any broken rails during the study period, resulting in a small sample size of broken rail instances. The findings indicate that, for the dataset employed in this study, LightGBM, in conjunction with random oversampling, exhibits superior performance. Based on the feature importance results, the critical factors influencing the prediction of broken rail occurrences on this commuter railroad are gradient, operational speed, and prior rail defects.

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<b>Estimation Method of Seismic Damage of Bearings at Railway Structure by Displacement Sensor</b>

Cited by 2 publications

References 1 publication

<b>Method for Updating Operational Regulation Standards Considering Seismic Risk of Railway Facilities</b>

<b>Method for Updating Operational Regulation Standards Considering Seismic Risk of Railway Facilities</b>

Estimating the occurrence of broken rails in commuter railroads with machine learning algorithms

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