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Long short-term memory (LSTM)-based wind speed prediction during a typhoon for bridge traffic control
Abstract A short-term wind speed prediction framework is proposed for bridge traffic control under strong winds. The framework mainly focuses on improving the prediction accuracy for the timeframe of traffic control during a typhoon. Two concepts are newly proposed to achieve the goal: 1) hybrid modeling of wind speed at the bridge; and, 2) the adoption of a time-shifted data correction (TSDC) method. First, the hybrid modeling considers two available data types, one from a structural health monitoring system of the bridge and the other from the regional specialized meteorological center (RSMC). The training features of a long short-term memory (LSTM) approach are chosen based on the maximum sustained winds of a typhoon. Second, the TSDC method accounts for a time-delay phenomenon between the maximum wind speed at the bridge deck and the maxima or minima of the selected features. The Mean Absolute Error (MAE)-based grid search method determines the preferable combinations of two parameters: input data length and the time-shifted length of the training data. As a numerical example, typhoons from 2020 are used as test data to demonstrate the improvement in prediction performance via the use of hybrid modeling and the TSDC method.
Highlights A short-term wind speed prediction framework is proposed for bridge traffic control during a typhoon. Hybrid modeling employed two data types: structural health monitoring and satellite data. The training features are chosen based on the maximum sustained winds of a typhoon. A time-shifted data correction reflected a time-delay phenomenon between the maxima or minima of wind speed at the bridge deck and the selected features. The framework was demonstrated to improve the prediction accuracy for the time frame of traffic control.
Long short-term memory (LSTM)-based wind speed prediction during a typhoon for bridge traffic control
Abstract A short-term wind speed prediction framework is proposed for bridge traffic control under strong winds. The framework mainly focuses on improving the prediction accuracy for the timeframe of traffic control during a typhoon. Two concepts are newly proposed to achieve the goal: 1) hybrid modeling of wind speed at the bridge; and, 2) the adoption of a time-shifted data correction (TSDC) method. First, the hybrid modeling considers two available data types, one from a structural health monitoring system of the bridge and the other from the regional specialized meteorological center (RSMC). The training features of a long short-term memory (LSTM) approach are chosen based on the maximum sustained winds of a typhoon. Second, the TSDC method accounts for a time-delay phenomenon between the maximum wind speed at the bridge deck and the maxima or minima of the selected features. The Mean Absolute Error (MAE)-based grid search method determines the preferable combinations of two parameters: input data length and the time-shifted length of the training data. As a numerical example, typhoons from 2020 are used as test data to demonstrate the improvement in prediction performance via the use of hybrid modeling and the TSDC method.
Highlights A short-term wind speed prediction framework is proposed for bridge traffic control during a typhoon. Hybrid modeling employed two data types: structural health monitoring and satellite data. The training features are chosen based on the maximum sustained winds of a typhoon. A time-shifted data correction reflected a time-delay phenomenon between the maxima or minima of wind speed at the bridge deck and the selected features. The framework was demonstrated to improve the prediction accuracy for the time frame of traffic control.
Long short-term memory (LSTM)-based wind speed prediction during a typhoon for bridge traffic control
Lim, Jae-Yeong (author) / Kim, Sejin (author) / Kim, Ho-Kyung (author) / Kim, Young-Kuk (author)
2021-09-22
Article (Journal)
Electronic Resource
English
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