Intelligent Testing Method for Multi-Point Vibration Acquisition of Pile Foundation Based on Machine Learning.

Ke Wang, Weikai Zhao, Juntao Wu, Shuang Ma

Abstract read

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. 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

4 authors.

Ke WangCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.

Weikai ZhaoCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.

Juntao WuCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0001-9092-401X

Shuang MaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.

Funding

National Natural Science Foundation of China 52108349National Natural Science Foundation of China 52178358Natural Science Foundation of Zhejiang Province LTGG24E080001

6 · The paper itself

Abstract

To address the limitations of the conventional low-strain reflected wave method for pile foundation testing, this study proposes an intelligent multi-point vibration acquisition testing model based on machine learning to evaluate the integrity of in-service, high-cap pile foundations. The model's performance was assessed using statistical error metrics, including the correlation coefficient R

Indexed as

high-caplow strainmachine learningmulti-point vibration acquisitionpile foundation

Identifiers

PMID40363329

DOI10.3390/s25092893

PMCPMC12074275

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.