测绘学报 ›› 2024, Vol. 53 ›› Issue (6): 1113-1127.doi: 10.11947/j.AGCS.2024.20230387
龚循强1,2(), 汪宏宇2,3(
), 鲁铁定1,2, 游为3
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:
Xunqiang GONG1,2(), Hongyu WANG2,3(
), Tieding LU1,2, Wei YOU3
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:
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。
中图分类号:
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.
表1
低频观测数据试验各模型精度指标"
模型 | A1 | A2 | B1 | B2 | ||||
---|---|---|---|---|---|---|---|---|
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 |
表2
高频观测数据试验各模型精度指标"
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
RMSE | 0.736 | 0.918 | 1.001 | 1.049 | 1.025 | 1.009 | 0.401 | 0.243 |
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