Metaheuristic algorithm-optimized framework for predicting bridge measurement data using TVFEMD and deep learning

Zhenwei Zhou

doi:10.1177/14759217251351533

2025-08-11

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DOI：10.1177/14759217251351533

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桥梁风速和响应预测对于桥梁健康监测中的早期预警和异常状态检测至关重要。然而，由于长期暴露于车辆与环境载荷的联合作用下，测量数据的复杂性和随机性给提高预测精度带来了挑战。为应对这一挑战，本研究提出了一种新颖的混合框架，结合基于时变滤波的经验模态分解（TVFEMD）、灰狼优化（GWO）、梯度优化（GBO）和长短期记忆（LSTM）网络，用于预测桥梁测量数据。GWO算法被用于优化TVFEMD方法的分解参数（即带宽阈值和B样条阶数），GWO-TVFEMD能够自适应地将测量数据分解为若干稳定子序列。此外，GBO算法被用于优化LSTM的隐藏层数、学习率和最大迭代次数，以增强深度学习性能。来自桥梁现场测量的实验结果表明，所提出的混合模型优于变分模态分解LSTM和TVFEMD-LSTM模型。此外，所提出的框架在预测桥梁风速、位移和应变方面表现出良好的泛化能力，为实际桥梁工程提供了可靠的结果。

Keyword:

bridge wind speed prediction

empirical mode decomposition

Grey wolf optimization

gradient-based optimization

LSTM network

期刊

Structural Health Monitoring

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341

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East China Jiao Tong University

学者数:

论文数: 17

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China Minmetals Corporation

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论文数: 3

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Central Research Institute of Building and Construction Co., Ltd

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论文数: 1

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