Evidence map›Paper›PMID 36365815›Full record

ArticleSensors (Basel, Switzerland)2022

An Intelligent Correlation Real-Time Analysis Method for the Mechanical Properties of Members in Super-Span Valve Hall Grid Structure Hoisting Process.

Qinghe Zeng, Jin Liao, Xionghui Huang, Weihua Ming, Yi Gao, Cuiying Zhou, Zhen Liu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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
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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

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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

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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.

Qinghe ZengChina Southern Power Grid Ehv Power Transmission Company, Guangzhou 510275, China.
Jin LiaoSchool of Civil Engineering, Sun Yat-sen University, No. 135 Xingangxilu, Guangzhou 510275, China.
Xionghui HuangChina Southern Power Grid Ehv Power Transmission Company, Guangzhou 510275, China.
Weihua MingSchool of Civil Engineering, Sun Yat-sen University, No. 135 Xingangxilu, Guangzhou 510275, China.
Yi GaoSchool of Civil Engineering, Sun Yat-sen University, No. 135 Xingangxilu, Guangzhou 510275, China.
Cuiying ZhouSchool of Civil Engineering, Sun Yat-sen University, No. 135 Xingangxilu, Guangzhou 510275, China.
Zhen LiuSchool of Civil Engineering, Sun Yat-sen University, No. 135 Xingangxilu, Guangzhou 510275, China.ORCID 0000-0003-0083-8754

Funding

the National Natural Science Foundation of China (NSFC) 41977230the National Natural Science Foundation of China (NSFC) 42277131
6 · The paper itself

Abstract

The mechanical performance analysis of the members is the primary basis for evaluating the hoisting quality and safety of the valve hall grid structure. Ordinarily, manual analysis of monitoring data and on-site experience inspection are employed to structural judgment, but it is challenging to evaluate the correlation of the various members and the overall safety of a valve hall. In this paper, an intelligent correlation real-time analysis method based on a BPNN (Back Propagation Neural Network) for the mechanical properties of members is proposed to intelligently control the safety of valve hall grid structure hoisting. The correlation between the mechanical properties of multi-points in the grid structure is used to model the target measuring points. In addition, an intelligent real-time analysis system is used to manage and apply the mechanical property correlation and abnormality of members in real-time. Then, the model is applied to a super-span valve hall in South China, and the application effect is good. The mechanical property correlation model can accurately reflect the mechanical state of the valve hall grid structure hoisting process. Simultaneously, it can effectively pinpoint hidden dangers and locate risk members. It provides a new reference for the normal operation and maintenance of a super-span valve hall grid.

Indexed as

hoisting processintelligent correlationmechanical properties of membersreal-time analysissuper-span valve hall

Identifiers

PMID36365815
PMCPMC9657440

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