Evidence map›Paper›PMID 38339583›Full record

ArticleSensors (Basel, Switzerland)2024

Confining Pressure Forecasting of Shield Tunnel Lining Based on GRU Model and RNN Model.

Min Wang, Xiao-Wei Ye, Jin-Dian Jia, Xin-Hong Ying, Yang Ding, Di Zhang, Feng Sun

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Min WangPolytechnic Institute, Zhejiang University, Hangzhou 310058, China.
Xiao-Wei YeDepartment of Civil Engineering, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0003-0012-5842
Jin-Dian JiaDepartment of Civil Engineering, Zhejiang University, Hangzhou 310058, China.
Xin-Hong YingDepartment of Civil Engineering, Zhejiang University, Hangzhou 310058, China.
Yang DingDepartment of Civil Engineering, Hangzhou City University, Hangzhou 310015, China.ORCID 0000-0002-1298-1710
Di ZhangChina Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China.
Feng SunChina Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China.

Funding

National Key R&D Program of China 2022YFC3802301National Science Foundation of China 52178306
6 · The paper itself

Abstract

The confining pressure has a great effect on the internal force of the tunnel. During construction, the confining pressure which has a crucial impact on tunnel construction changes due to the variation of groundwater level and applied load. Therefore, the safety of tunnels must have the magnitude of confining pressure accurately estimated. In this study, a complete tunnel confining pressure time axis was obtained through high-frequency field monitoring, the data are segmented into a training set and a testing set. Using GRU and RNN models, a confining pressure prediction model was established, and the prediction results were analyzed. The results indicate that the GRU model has a fast-training speed and higher accuracy. On the other hand, the training speed of the RNN model is slow, with lower accuracy. The dynamic characteristics of soil pressure during tunnel construction require accurate prediction models to maintain the safety of the tunnel. The comparison between GRU and RNN models not only highlights the advantages of the GRU model but also emphasizes the necessity of balancing speed accuracy in tunnel construction confining pressure prediction modeling. This study is helpful in improving the understanding of soil pressure dynamics and developing effective prediction tools to promote safer and more reliable tunnel construction practices.

Indexed as

confining pressureGate Recurrent Unit (GRU)multi-output recursive strategyrecurrent neural network (RNN)shield tunneltime series forecasting

Identifiers

PMID38339583
PMCPMC10857098

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.