The range of hyperparameters and results.

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.

运营期高墩大跨度刚构桥的刚度提升了其抗风致振动的能力。然而，高墩与长悬臂的结构特性使其在强风区域施工时极易发生风致振动，进而带来安全隐患。风振响应具有较强的非线性与随机波动特性，给桥梁施工阶段的精准预测带来了极大挑战。本文提出了一种基于时间卷积网络（Temporal Convolutional Network，TCN）的桥梁风振响应新型预测算法。该算法采用因果卷积挖掘风振响应加速度数据的映射关系，利用膨胀卷积捕捉风振响应的多尺度特征，并通过网络层间的残差连接缓解梯度消失问题。本文将所提出的基于TCN的桥梁风振响应预测模型，与循环神经网络（Recurrent Neural Network，RNN）、长短期记忆网络（Long Short-Term Memory Network，LSTM）以及门控单元网络（Gated Unit Network，GRU）等先进算法进行了详细对比。结果表明，所提算法针对扭转、竖向、横向以及顺桥向等不同方向的风振加速度，均具备优异的预测精度与泛化能力。