The aerodynamic characteristics of train/vehicles under cross winds depend on not only the shapes of the vehicles but also those of infrastructures. Accordingly three kinds of wind tunnel tests were made to evaluate the aerodynamic characteristics of typical configurations of the vehicles on typical configurations of infrastructures such as bridges and embankments. The main results obtained from the wind tunnel tests are summarized below. 1. The aerodynamic side force coefficient of the vehicle increases more as the thickness of the bridge girder becomes larger. It also increases more as the roof of the vehicle becomes edgier. 2. The aerodynamic characteristics of the vehicle on the embankment depend on the distribution of the boundary layer on the ground. The aerodynamic side force coefficient of the vehicle on a high embankment is larger than that on a low embankment.

Aerodynamic Characteristics of Train/Vehicles under Cross Winds

Long short-term memory (LSTM)-based wind speed prediction during a typhoon for bridge traffic control

Effect of non-Gaussian turbulence on extreme buffeting response of a high-speed railway sea-crossing bridge

A short-term wind speed prediction method based on interval type 2 fuzzy model considering the selection of important input variables

Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm

A new framework to establish a traffic control strategy is proposed for sea-crossing bridges. This new framework is particularly focused on the threat posed by strong winds, and two geomet...

Decision Framework for Traffic Control on Sea-Crossing Bridges during Strong Winds

Vehicles running on sea-crossing bridges are vulnerable to strong winds with instantaneous speeds of over 20 m/s. Bridge operators should secure the safety of the bridge users by limiting vehicle speeds or restricting the traffic when wind speed measured on the bridges exceeds a certain threshold value. To guarantee the safety of the bridge users during typhoons, an accurate forecasting of the strong winds would be essential. In this study, an Artificial Neural Network (ANN) was considered to model the occurrence characteristics of the strong wind speed at the sea- crossing bridge during typhoons. The Long Short-Term Memory (LSTM), which is generally used in the time-series analysis, was applied. This research utilized 16 years of wind speed data acquired by sensors located on a suspension bridge in South Korea and Best Track data of typhoons from the Regional Specialized Meteorological Center (RSMC) in Tokyo.

Jae-Yeong Lim

Papers

Long short-term memory (LSTM)-based wind speed prediction during a typhoon for bridge traffic control

Decision Framework for Traffic Control on Sea-Crossing Bridges during Strong Winds

Short-Term Forecasting of the Occurrence Time of Strong Wind Speed during a Typhoon based on LSTM for Sea-Crossing Bridge Operation