Evidence map›Paper›PMID 38732931›Full record

ReviewSensors (Basel, Switzerland)2024

Review of Sensor-Based Subgrade Distress Identifications.

Zhiheng Cheng, Zhengjian Xie, Mingzhao Wei, Yuqing Peng, Cong Du, Yuan Tian, Xiuguang Song

Abstract readReview
In one paragraph

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

Zhiheng ChengSchool of Qilu Transportation, Shandong University, Jinan 250061, China.
Zhengjian XieCCCC-FHDI Engineering Co., Ltd., Guangzhou 510220, China.
Mingzhao WeiSchool of Qilu Transportation, Shandong University, Jinan 250061, China.
Yuqing PengSchool of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China.
Cong DuSchool of Qilu Transportation, Shandong University, Jinan 250061, China.
Yuan TianSchool of Qilu Transportation, Shandong University, Jinan 250061, China.
Xiuguang SongSchool of Qilu Transportation, Shandong University, Jinan 250061, China.

Funding

China Postdoctoral Science Foundation 2023M732064National Key Research and Development Program of China 2022YFB2602102Natural Science Foundation of Shandong Province ZR2023QE185
6 · The paper itself

Abstract

The attributes of diversity and concealment pose formidable challenges in the accurate detection and efficacious management of distresses within subgrade structures. The onset of subgrade distresses may precipitate structural degradation, thereby amplifying the frequency of traffic incidents and instigating economic ramifications. Accurate and timely detection of subgrade distresses is essential for maintaining and repairing road sections with existing distresses. This helps to prolong the service life of road infrastructure and reduce financial burden. In recent years, the advent of numerous novel technologies and methodologies has propelled significant advancements in subgrade distress detection. Therefore, this review delineates a concentrated examination of subgrade distress detection, methodically consolidating and presenting various techniques while dissecting their respective merits and constraints. By furnishing comprehensive guidance on subgrade distress detection, this review facilitates the expedient identification and targeted treatment of subgrade distresses, thereby fortifying safety and enhancing durability. The pivotal role of this review in bolstering the construction and operational facets of transportation infrastructure is underscored.

Indexed as

data processingdetection sensorsubgrade distress

Identifiers

PMID38732931
PMCPMC11086097

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.