Evidence map›Paper›PMID 30513628›Full record

ArticleMaterials (Basel, Switzerland)2018

Innovative Strain Sensing for Detection of Exterior Wall Tile Lesion: Smart Skin Sensory System.

Chih-Yuan Chang, San-Shan Hung, Li-Hua Liu, Chien-Pang Lin

Open access · goldAbstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2018. 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
0.5field-weighted citation impact, top 32% of its field
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, 9 citations in OpenAlex.

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 at 1 institution in 1 country.

Chih-Yuan ChangDepartment of Civil Engineering, Feng Chia University, Taichung 40724, Taiwan. rchang@fcu.edu.tw.
San-Shan HungDepartment of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan. sshung@fcu.edu.tw.
Li-Hua LiuDepartment of Civil Engineering, Feng Chia University, Taichung 40724, Taiwan. liuli0063@gmail.com.
Chien-Pang LinDepartment of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan. chienpang8379@gmail.com.
Feng Chia University · TW

Funding

Ministry of Science and Technology, Taiwan 106-2221-E-035 -016 -
6 · The paper itself

Abstract

Tiles are commonly used to cover the exteriors of buildings in Taiwan. However, older buildings in Taiwan encounter the problem of tiles falling off due to natural deterioration, which is unsightly, and more importantly, a threat to public safety. Nevertheless, no current method exists that can effectively detect flaws in building tiles in real time. This study combined the fields of civil engineering and automatic control to reduce risks caused by falling tiles by improving real-time detection of at-risk areas. Micro-resistance was combined with fuzzy theory as the logical foundation for evaluating tile status. String-type strain gauges were adopted as sensors to design a smart skin sensory system that could measure signs of deterioration in tile surface lesions. The design was found to be feasible. In the future, it can be further developed for facile real-time assessment of tile status.

Indexed as

public safetysmart buildingSmart skinstrain gaugetile

Identifiers

PMID30513628
PMCPMC6317046
OpenAlexW2902344595

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.