Evidence map›Paper›PMID 41360864›Full record

ArticleScientific reports2025

Integration of InSAR and numerical modelling to assess tailings pond slope deformation affected by reservoir water.

Liwei Lu, Menghua Li, Liang Chao

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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Liwei LuFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, 650093, Yunnan, China.
Menghua LiFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, 650093, Yunnan, China. menghuali@kust.edu.cn.
Liang ChaoFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, 650093, Yunnan, China.

Funding

Central guidance of local science and technology development fund 202401AS070071National Natural Science Foundation of China 42404036
6 · The paper itself

Abstract

Slope stability in tailings ponds is crucial for both environmental safety and operational efficiency, as instability can lead to significant hazards. This study introduces a novel integrated approach combining SBAS-InSAR and fluid-structure interaction numerical modeling to enhance the monitoring and stability analysis of tailing pond slopes. A fluid-structure interaction model is presented, incorporating the effects of pore pressure generated by reservoir water level on slope stability, thereby improving the accuracy of internal stress and deformation analysis. Sentinel-1 SAR data were used to monitor surface displacements over a 2-year period (2021-2023), while a three-dimensional numerical model was employed to simulate the mechanical behavior of the tailings dam, considering different material properties and reservoir water level conditions. The results reveal notable spatial patterns of slope deformation, with the presence of reservoir water level and stratigraphic boundaries leading to significant changes in stress and displacement profiles. Comparison between the InSAR results and numerical simulations shows a high level of agreement, with error and statistical analyses, respectively, validating the robustness of the integrated approach. This study provides a comprehensive multi-source method for tailing pond slope monitoring, offering a more accurate and reliable technique for predicting slope instability and improving risk assessment.

Indexed as

Fluid–structure interactionReservoir waterSBAS-InSARSlope stabilityTailing ponds

Identifiers

PMID41360864
PMCPMC12686519

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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.