Evidence map›Paper›PMID 37532776›Full record

ArticleScientific reports2023

Automatic identification method of bridge structure damage area based on digital image.

Jinchao Wang, Houcheng Liu, Zengqiang Han, Yiteng Wang

Abstract read
In one paragraph

Article in Scientific reports, 2023. 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

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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.

Jinchao WangInstitute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China. jcwang@whrsm.ac.cn.
Houcheng LiuInstitute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China.
Zengqiang HanInstitute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China.
Yiteng WangInstitute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China.

Funding

National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China 42227805National Natural Science Foundation for the Youth of China 41902294
6 · The paper itself

Abstract

It is of great scientific and practical value to use effective technical means to monitor and warn the structural damage of bridges in real time and for a long time. Traditional image recognition network models are often limited by the lack of on-site images. In order to solve the problem of automatic recognition and parameter acquisition in digital images of bridge structures in the absence of data information, this paper proposes an automatic identification method for bridge structure damage areas based on digital images, which effectively achieves contour carving and quantitative characterization of bridge structure damage areas. Firstly, the digital image features of the bridge structure damage area are defined. By making full use of the feature that the pixel value of the damaged area is obviously different from that of the surrounding image, an image pre-processing method of the structure damaged area that can effectively improve the quality of the field shot image is proposed. Then, an improved Ostu method is proposed to organically fuse the global and local threshold features of the image to achieve the damaged area contour carving of the bridge structure surface image. The scale of damage area, the proportion of damage area and the calculation rule of damage area orientation are constructed. The key inspection and characteristic parameter diagnosis of bridge structure damage area are realized. Finally, test and analysis are carried out in combination with an actual project case. The results show that the method proposed in this paper is feasible and stable, which can improve the damage area measurement accuracy of the current bridge structure. The method can provide more data support for the detection and maintenance of the bridge structure.

Identifiers

PMID37532776
PMCPMC10397243

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