Evidence map›Paper›PMID 30654493›Full record

ArticleSensors (Basel, Switzerland)2019

Optimal Placement of Virtual Masses for Structural Damage Identification.

Jilin Hou, Zhenkun Li, Qingxia Zhang, Runfang Zhou, Łukasz Jankowski

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Jilin HouDepartment of Civil Engineering & State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China. houjilin@dlut.edu.cn.ORCID 0000-0002-3554-7036
Zhenkun LiDepartment of Civil Engineering & State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China. lizhenkunn@gmail.com.
Qingxia ZhangDepartment of Civil Engineering, Dalian Minzu University, Dalian 116650, China. zhangqingxia_hit@hotmail.com.
Runfang ZhouDepartment of Civil Engineering & State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China. zhou0430@126.com.
Łukasz JankowskiInstitute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw 02-106, Poland. ljank@ippt.pan.pl.ORCID 0000-0002-9773-0688

Funding

Narodowym Centrum Nauki DEC- 2017/25/B/ST8/01800National Key Research and Development Program of China 2018YFC0705604National Natural Science Foundation of China 51878118Natural Science Foundation of Liaoning Province 20180551205
6 · The paper itself

Abstract

Adding virtual masses to a structure is an efficient way to generate a large number of natural frequencies for damage identification. The influence of a virtual mass can be expressed by Virtual Distortion Method (VDM) using the response measured by a sensor at the involved point. The proper placement of the virtual masses can improve the accuracy of damage identification, therefore the problem of their optimal placement is studied in this paper. Firstly, the damage sensitivity matrix of the structure with added virtual masses is built. The Volumetric Maximum Criterion of the sensitivity matrix is established to ensure the mutual independence of measurement points for the optimization of mass placement. Secondly, a method of sensitivity analysis and error analysis is proposed to determine the values of the virtual masses, and then an improved version of the Particle Swarm Optimization (PSO) algorithm is proposed for placement optimization of the virtual masses. Finally, the optimized placement is used to identify the damage of structures. The effectiveness of the proposed method is verified by a numerical simulation of a simply supported beam structure and a truss structure.

Indexed as

damage identificationParticle Swarm Optimization (PSO) algorithmsensitivitysensor optimizationVirtual Distortion Method (VDM)

Identifiers

PMID30654493
PMCPMC6359493

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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.