Evidence map›Paper›PMID 35955210›Full record

ArticleMaterials (Basel, Switzerland)2022

Evaluate Pavement Skid Resistance Performance Based on Bayesian-LightGBM Using 3D Surface Macrotexture Data.

Yuanjiao Hu, Zhaoyun Sun, Yuxi Han, Wei Li, Lili Pei

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

5 authors.

Yuanjiao HuSchool of Information Engineering, Chang'an University, Xi'an 710064, China.
Zhaoyun SunSchool of Information Engineering, Chang'an University, Xi'an 710064, China.
Yuxi HanSchool of Information Engineering, Chang'an University, Xi'an 710064, China.
Wei LiSchool of Information Engineering, Chang'an University, Xi'an 710064, China.
Lili PeiSchool of Information Engineering, Chang'an University, Xi'an 710064, China.ORCID 0000-0002-1084-8289

Funding

National Key Research and Development Program, China 2018YFB1600202National Natural Science Foundation of China 52178407
6 · The paper itself

Abstract

The lack of skid resistance performance is a crucial factor leading to road traffic accidents. The pavement surface friction is an essential indicator for measuring the skid resistance. The surface texture structure significantly affects the friction between the tire and the pavement, determining the pavement skid resistance. To deeply study the relationship between surface texture structure and pavement skid resistance performance, two types of asphalt mixture specimens, asphalt concrete (AC) and open-graded friction course (OGFC), are prepared for the skid resistance performance test. Firstly, a high-precision 3D smart sensor Gocator 3110 is used to collect the 3D point cloud data of the asphalt mixture surface texture. The British pendulum tester is used to measure the friction. Secondly, ten feature parameters are extracted to describe the 3D macrotexture characteristics. A data set containing 10 features and 200 groups of texture and friction data was also constructed. Meanwhile, the influence of macro-texture features on the skid resistance performance is discussed. Finally, an optimized Bayesian-LightGBM model is trained based on the constructed dataset. Compared with LightGBM, XGBoost, RF, and SVR algorithms, the Bayesian-LightGBM model can evaluate skid resistance more accurately. The R

Indexed as

3D macrotexture characteristicsBayesian-LightGBMfeature importance analysisfriction coefficientskid resistance

Identifiers

PMID35955210
PMCPMC9369934

What OpenQuestion holds

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LicenceCC BY
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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.