Evidence map›Paper›PMID 29921810›Full record

ArticleSensors (Basel, Switzerland)2018

Deformation Monitoring for Chinese Traditional Timber Buildings Using Fiber Bragg Grating Sensors.

Ni-Lei Li, Shao-Fei Jiang, Ming-Hao Wu, Sheng Shen, Ying Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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.

Ni-Lei LiCollege of Civil Engineering, Fuzhou University, Fuzhou 350108, China. linilei@foxmail.com.
Shao-Fei JiangCollege of Civil Engineering, Fuzhou University, Fuzhou 350108, China. cejsf@fzu.edu.cn.ORCID 0000-0003-2149-9296
Ming-Hao WuCollege of Civil Engineering, Fuzhou University, Fuzhou 350108, China. clevermouse11@163.com.
Sheng ShenCollege of Civil Engineering, Fuzhou University, Fuzhou 350108, China. S_Shen@126.com.
Ying ZhangCollege of Architecture, Fuzhou University, Fuzhou 350108, China. zy6163@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Fiber Bragg Grating (FBG) sensing technique is suitable for a wide variety of measurements, including temperature, pressure, acceleration, liquid level, etc., and has been applied to many bridges and buildings in the past two decades. The fact that the FBG technique can only monitor and measure strain data for most cases when it is used for deformation measurements impedes application of the FBG sensing technique in civil infrastructures. This paper proposes FBG sensing-based deformation monitoring methods that are applicable to monitoring beam deflection, column inclination angle and mortise-tenon joint dislocation for Chinese traditional timber structures. On the basis of improved conjugated beam theory and geometrical trigonometric function relationship, the relationships between the FBG sensing strain values and the deflection of beam, inclination angle of column, as well as the amount of dislocation of mortise-tenon joint are deducted for Chinese traditional buildings. A series of experiments were conducted to verify the applicability and effectiveness of the proposed deformation monitoring methods. The results show that a good agreement is obtained between the values given by the methods proposed in this paper and other methods. This implies that the proposed deformation monitoring methods are applicable and effective in the health monitoring of Chinese traditional timber structures.

Indexed as

amount of dislocationChinese traditional timber buildingsdeformation monitoringFBG sensinginclination anglemortise-tenon jointsemi-rigid joint

Identifiers

PMID29921810
PMCPMC6022200

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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.