Evidence map›Paper›PMID 41157334›Full record

ArticleSensors (Basel, Switzerland)2025

Inversion of Soil Parameters and Deformation Prediction for Deep Excavation Based on PSO-SVM Model.

Jing Zhao, Longhui Chen, Hongyin Yang, Bin Li, Linlong Yang, Hao Peng, Hongyou Cao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 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

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

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

7 authors.

Jing ZhaoChina Railway 11th Bureau Group First Engineering Co., Ltd., Xiangyang 441199, China.
Longhui ChenChina Railway 11th Bureau Group First Engineering Co., Ltd., Xiangyang 441199, China.
Hongyin YangSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, China.ORCID 0000-0002-7078-7705
Bin LiCapital Construction Department, Wuhan Textile University, Wuhan 430073, China.
Linlong YangSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, China.
Hao PengChina Railway 11th Bureau Group First Engineering Co., Ltd., Xiangyang 441199, China.
Hongyou CaoSchool of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China.

Funding

Construction Science and Technology Plan Projects of Hubei Province Grant No. 2023011National Natural Science Foundation of China Grant No. 51708429Open Projects Foundation of Engineering Research Center of Disaster Prevention and Mitigation of Southeast Coastal Engineering Structures of Fujian Province University Grant No. 2022009
6 · The paper itself

Abstract

During deep excavation, actual soil parameters undergo changes. To enhance the accuracy of soil parameter selection in finite element simulation and improve the precision of finite element analysis, an inversion method for soil parameters based on a PSO-SVM model is proposed. In this method, the particle swarm optimization (PSO) algorithm is utilized to optimize the penalty parameter

Indexed as

deep excavationdeformation predictionfinite element simulationPSO-SVM modelsoil parameters inversion

Identifiers

PMID41157334
PMCPMC12567958

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.