Experiment Study on Rock Mass Classification Based on RCM-Equipped Sensors and Diversified Ensemble-Learning Model.

Feng Li, Huike Zeng, Hongbin Xu, Haokai Sun

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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.

Delineating homogeneous zones for rock joint wall mechanical properties in open-pit mine slope based on a multi-indicator stacked generalization model.Scientific reports · 2026
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

4 authors.

Feng LiCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China.

Huike ZengSchool of Qilu Transportation, Shandong University, Jinan 250061, China.ORCID 0000-0001-8512-4931

Hongbin XuCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0002-4383-6962

Haokai SunSchool of Qilu Transportation, Shandong University, Jinan 250061, China.

Funding

China Postdoctoral Science Foundation 2022M722187China Postdoctoral Science Foundation 2022TQ0218National Natural Science Foundation of China 52378309Shenzhen Science and Technology Program KQTD20180412181337494

6 · The paper itself

Abstract

The geological condition monitoring and identification based on TBM-equipped sensors is of great significance for efficient and safe tunnel construction. Full-scale rotary cutting experiments are carried out using tunnel-boring machine disc cutters. Thrust, torque and vibration sensors are equipped on the rotary cutting machine (RCM). A stacking ensemble-learning model for real-time prediction of rock mass classification using features of mathematical statistics is proposed. Three signals, thrust, torque and a novel vibration spectrogram-based local amplification feature, are fed into the model and trained separately. The results show that the stacked ensemble-learning model has better accuracy and stability than any single model, showing a good application prospect in the rock mass classification.

Indexed as

multi-source datarock mass classificationstacking ensemble-learning modelTBM-equipped sensor

Identifiers

PMID39409359

DOI10.3390/s24196320

PMCPMC11478974

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.