Evidence map›Paper›PMID 39806081›Full record

ArticleScientific reports2025

Research on spatial prediction technology for mitigating tunnel inrush disasters under complex geological conditions in China's Hengduan Mountain Range.

Yang Zou, XiuJun Dong, Tao Feng, ZhengXuan Xu, Hailin He, ZhangLei Wu

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

6 authors.

Yang ZouChengdu University of Technology, Chengdu, 610059, China.
XiuJun DongChengdu University of Technology, Chengdu, 610059, China. dongxiujun@cdut.cn.
Tao FengChina Railway Eryuan Engineering Group Co.Ltd, Chengdu, 610031, China.
ZhengXuan XuChina Railway Eryuan Engineering Group Co.Ltd, Chengdu, 610031, China.
Hailin HeThe 1st Geological Brigade of Sichuan, Chengdu, 610072, China.
ZhangLei WuChengdu Engineering Corporation Limited, Chengdu, 610072, China.

Funding

China Railway Group Limited Technology Research and Development Program 2022-Major-01
6 · The paper itself

Abstract

The proportion of railway high-altitude buried tunnels in complex and dangerous mountainous areas in southwest China is exceptionally high. With the characteristics of suddenness, intermittency, instantaneousness, and destruction, inrushinrushing and collapse is one of the main risks in the tunnel construction process. Therefore, in the design and construction process of tunnels in Hengduan Mountain area, it is very important to identify the mechanism of sudden s inrushing and collapse risks, predict the spatial location and scale of possible inrushing and collapse, and formulate corresponding tunnel design and construction response measures. In order to achieve the above goals, through extensive exploratory attempts and experiments, it has been found that the use of multi-source information during the survey period can analyze the sources and channels of tunnel water inrushing, as well as the mechanism of water inrushing disasters; By utilizing high-definition images of the excavation surface, ultra deep directional drilling information data, and geophysical 3D attribute information data obtained during tunnel excavation, modeling, coordinate conversion, and 3D integration can be carried out. Various information can be compared and verified with each other, and spatial prediction of potential inrushing and collapse risks in front of the excavation surface can be made, and ultimately response measures for potential or existing surge disasters can be determined. This spatial prediction and analysis method is highly effective and has practical and promotional value.

Indexed as

Hengduan Mountain RangeInrushing and collapseMulti-source informationSpatial prediction

Identifiers

PMID39806081
PMCPMC11729892

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.