高铁桥墩沉降的通用渐进分解长期预测网络模型

doi:10.11947/j.AGCS.2024.20230387

测绘学报 ›› 2024, Vol. 53 ›› Issue (6): 1113-1127.doi: 10.11947/j.AGCS.2024.20230387

高铁桥墩沉降的通用渐进分解长期预测网络模型

龚循强¹^,²(), 汪宏宇²^,³(), 鲁铁定¹^,², 游为³

^1.东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室，江西　南昌　330013
^2.东华理工大学测绘与空间信息工程学院，江西　南昌　330013
^3.西南交通大学高速铁路安全运营空间信息技术国家地方联合工程实验室，四川　成都　611756

收稿日期:2023-09-05 发布日期:2024-07-22
通讯作者: 汪宏宇 E-mail:xqgong1988@163.com;wanghyvv@foxmail.com
作者简介:龚循强（1988—），男，博士，副教授，研究方向为重大交通工程智能监测。　E-mail：xqgong1988@163.com
基金资助:
国家自然科学基金(42101457);自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室开放基金重点项目(MEMI-2023-01)

A general progressive decomposition long-term prediction network model for high-speed railway bridge pier settlement

Xunqiang GONG¹^,²(), Hongyu WANG²^,³(), Tieding LU¹^,², Wei YOU³

^1.Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China
^2.School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China
^3.State-Province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China

Received:2023-09-05 Published:2024-07-22
Contact: Hongyu WANG E-mail:xqgong1988@163.com;wanghyvv@foxmail.com
About author:GONG Xunqiang (1988—), male, PhD, associate professor, majors in intelligent monitoring of major transportation engineering.　E-mail: xqgong1988@163.com
Supported by:
The National Natural Science Foundation of China(42101457);The Key Open Fund of Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources(MEMI-2023-01)

摘要/Abstract

摘要：

高铁桥墩不均匀沉降是导致轨道不平顺的潜在原因之一，准确预测桥墩沉降对于确保铁路建设和运营的可靠性和安全性具有重要意义。目前，常规时间序列领域的多数预测模型仅在预处理良好且没有缺失的数据集上进行测试，而在高铁桥墩沉降的真实场景中，沉降数据相较于其他领域存在观测频次少且不等时距，以及沉降规律复杂多变的问题，造成长期预测困难。为此，本文提出一种高铁桥墩沉降的通用渐进分解长期预测网络（GPDLPnet），摒弃传统的预处理思想，将预处理过程嵌入网络结构，在网络训练过程中实现渐进预处理。首先，GPDLPnet在每轮迭代中利用改进对角掩码自注意力模块分析沉降数据中的缺失模式。然后，通过改进完全自适应噪声集合经验模态分解模块将沉降数据分解并重构为高频、低频和趋势子分量，将子分量作为BiLSTM-RSA-Resnet预测模块的特征输入。最后，输出递归预测结果，从而实现高铁桥墩沉降的长期预测。结合实际工程数据，将数据划分为高频观测和低频观测两类典型的观测模式进行试验，在3~4个月的预测中GPDLPnet均表现出良好的预测性能，并在精度指标上优于其他7种模型。

关键词: 深度学习, 高铁桥墩, 沉降预测, 残差网络, 卷积神经网络

Abstract:

Uneven settlement of high-speed railway bridge pier is one of the potential causes leading to track irregularities. Accurately predicting settlement of bridge pier is of significant importance for ensuring the reliability and safety of railway construction and operation. Most conventional time series prediction models are tested only on well-preprocessed datasets without missing values. However, in real-world scenarios of high-speed railway bridge pier settlement, the data are characterized by infrequent and irregular observation intervals and complex, variable settlement patterns, posing challenges for long-term prediction. To address this, we introduce the general progressive decomposition long-term prediction network (GPDLPnet), which abandons traditional preprocessing concepts and embeds the preprocessing phase within the network structure, achieving progressive preprocessing during training. In each iteration, GPDLPnet uses an improved diagonally-masked self-attention (IDMSA) module to analyze missing patterns in the settlement data, then decomposes and reconstructs the data into high-frequency, low-frequency, and trend sub-components through an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) module. These sub-components serve as feature inputs for the BiLSTM-RSA-Resnet prediction module, which outputs recursive predictions, thus enabling long-term prediction of high-speed railway bridge pier settlement. Utilizing real-world engineering data, experiments under two typical observation modes, high-frequency and low-frequency, are conducted. GPDLPnet demonstrates excellent predictive performance over a 3-4 month, surpassing seven other models in accuracy indexes.

Key words: deep learning, high-speed railway bridge pier, settlement prediction, residual network, convolutional neural network

中图分类号:

P258

龚循强, 汪宏宇, 鲁铁定, 游为. 高铁桥墩沉降的通用渐进分解长期预测网络模型[J]. 测绘学报, 2024, 53(6): 1113-1127.

Xunqiang GONG, Hongyu WANG, Tieding LU, Wei YOU. A general progressive decomposition long-term prediction network model for high-speed railway bridge pier settlement[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1113-1127.

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模型	A1		A2		B1		B2
模型	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
SVR	1.267	1.302	1.365	1.414	1.126	1.196	1.327	1.353
LSTM	1.185	1.212	1.240	1.550	1.191	1.229	1.361	1.382
CNN-LSTM	1.213	1.247	1.155	1.188	1.203	1.248	1.148	1.171
CNN-Attention-LSTM	1.061	1.095	0.813	0.905	1.145	1.192	1.083	1.107
BiLSTM	1.002	1.037	1.092	1.119	1.552	1.576	1.422	1.439
CNN-BiLSTM	1.481	1.513	1.021	1.083	1.045	1.091	1.262	1.282
Resnet	0.597	0.696	1.245	1.307	1.031	1.096	0.555	0.678
GPDLPnet	0.229	0.267	0.355	0.432	0.377	0.467	0.391	0.464

测点	模型	SVR	LSTM	CNN-LSTM	CNN-Attention-LSTM	BiLSTM	CNN-BiLSTM	Resnet	GPDLPnet
E1	MAE	1.144	0.936	0.892	0.772	0.632	0.806	0.836	0.273
E1	RMSE	1.244	0.955	0.933	0.830	0.690	0.849	1.037	0.309
E2	MAE	1.412	1.032	0.772	0.780	0.349	0.748	0.934	0.247
E2	RMSE	1.613	1.050	0.811	0.818	0.453	0.767	1.092	0.355
F1	MAE	0.989	0.603	0.671	0.683	0.504	0.574	0.589	0.278
F1	RMSE	1.288	0.687	0.816	0.809	0.571	0.678	0.774	0.342
F2	MAE	0.996	0.987	0.932	0.948	0.939	0.648	0.641	0.259
F2	RMSE	1.163	1.154	1.061	1.106	1.099	0.768	0.764	0.335
G1	MAE	0.459	0.675	0.408	0.337	0.381	0.350	0.437	0.157
G1	RMSE	0.571	0.746	0.471	0.433	0.457	0.439	0.506	0.242
G2	MAE	0.615	0.482	0.353	0.271	0.774	0.345	0.673	0.233
G2	RMSE	0.692	0.538	0.395	0.304	0.873	0.372	0.821	0.263
H1	MAE	0.785	1.117	1.175	0.938	1.163	1.023	0.639	0.423
H1	RMSE	0.953	1.205	1.278	1.008	1.243	1.106	0.823	0.533
H2	MAE	0.663	0.803	0.855	0.902	0.907	0.875	0.315	0.204
H2	RMSE	0.736	0.918	1.001	1.049	1.025	1.009	0.401	0.243

高铁桥墩沉降的通用渐进分解长期预测网络模型

A general progressive decomposition long-term prediction network model for high-speed railway bridge pier settlement

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