Evidence map›Paper›PMID 40594470›Full record

ArticleScientific reports2025

Influence of simulated traffic on foundation pit deformation via machine vision technology.

Xi Wu, Miao-Miao Sun, Zhi-Yu Xie, Song-Qiang Chen, Zhen-Yu Zhang, Xing-Lang Fan, Hai-Min Qian

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

7 authors.

Xi WuDepartment of Civil Engineering, Hangzhou City University, Hangzhou, 310015, China.
Miao-Miao SunDepartment of Civil Engineering, Hangzhou City University, Hangzhou, 310015, China.
Zhi-Yu XieDepartment of Civil Engineering, Hangzhou City University, Hangzhou, 310015, China. xiezy@hzcu.edu.cn.
Song-Qiang ChenDepartment of Civil Engineering, Hangzhou City University, Hangzhou, 310015, China.
Zhen-Yu ZhangZhejiang Jiaotou Transportation Construction Management Co., Ltd, Hangzhou, China.
Xing-Lang FanCentral Research Institute of Building and Construction, Metallurgical Group Corporation of China, Beijing, China.
Hai-Min QianDepartment of Civil Engineering, Hangzhou City University, Hangzhou, 310015, China.

Funding

National Natural Science Foundation of China 52308535Natural Science Foundation of Zhejiang Province LTGG23E080001
6 · The paper itself

Abstract

This study introduces a novel application of non-contact computer vision technology to simulate and analyse the impact of traffic loads on deformation in soft soil foundation pits, using vehicle flow data captured by roadside cameras. The research employs the YOLO (You Only Look Once) detector coupled with a multi-object tracking algorithm for vehicle classification and tracking. Following statistical analysis of traffic parameters, Monte Carlo sampling is utilised to simulate actual traffic loads. These loads are then input into a PLAXIS 3D finite element model to examine their effects on support structures and soil deformation. The comparison of measured and simulated data confirms the accuracy of the traffic load simulation model. Results demonstrate that vehicular loading can increase pit deformation by up to 48.7%, with significant impacts extending over 101.3 m along the site, significantly influencing engineering planning and construction management. This study provides a scientific basis for safety assessments in urban infrastructure projects, demonstrating the potential of non-contact machine vision technology for analysing the impact of traffic loads on the safety of adjacent excavation projects.

Indexed as

Finite element analysis.Foundation pitMonte Carlo samplingNon-contact machine visionVehicle load simulation

Identifiers

PMID40594470
PMCPMC12215285

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.