# A novel bridge wind-induced vibration response prediction algorithm based on temporal convolution network

## TL;DR

This paper introduces a new algorithm using temporal convolution networks to accurately predict wind-induced vibrations in bridges during construction.

## Contribution

A novel temporal convolution network-based algorithm for predicting bridge wind-induced vibration with improved accuracy and generalization.

## Key findings

- The proposed TCN model outperforms RNN, LSTM, and GRU in predicting wind-induced vibration acceleration.
- The model effectively captures multi-scale features and handles nonlinear and random fluctuations in wind vibration data.
- The algorithm demonstrates strong generalization across different vibration directions in bridges.

## Abstract

The stiffness of the high pier, large span rigid bridge in the operation period increases its ability to resist wind-induced vibration. However, the structural properties of high piers and long cantilevers make it susceptible to wind-induced vibration during construction in solid wind areas, which brings safety risks. The wind vibration response has strong nonlinear and random fluctuation characteristics, which brings significant challenges to the accurate prediction during the construction stage of bridges. A novel prediction algorithm for bridge wind-vibration response based on a temporal convolutional network (TCN) is proposed in this paper. It employs causal convolution to mine the mapping relationship of wind-induced vibration response acceleration data, utilizes dilation convolution to capture the multi-scale features of wind vibration response, and mitigates the gradient vanishing problem by residual connections between network layers. The proposed wind-induced vibration response prediction model based on TCN for bridges is compared in detail with advanced algorithms such as recurrent neural network (RNN), long-short-term memory network (LSTM), and gated unit network (GRU). The results demonstrate that the proposed algorithms have excellent prediction accuracy and generalization ability for wind vibration acceleration in different directions, such as torsion, vertical, transverse bridge, and along the bridge.