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Title: Structural health monitoring algorithm comparisons using standard data sets

Abstract

The real-world structures are subjected to operational and environmental condition changes that impose difficulties in detecting and identifying structural damage. The aim of this report is to detect damage with the presence of such operational and environmental condition changes through the application of the Los Alamos National Laboratory’s statistical pattern recognition paradigm for structural health monitoring (SHM). The test structure is a laboratory three-story building, and the damage is simulated through nonlinear effects introduced by a bumper mechanism that simulates a repetitive impact-type nonlinearity. The report reviews and illustrates various statistical principles that have had wide application in many engineering fields. The intent is to provide the reader with an introduction to feature extraction and statistical modelling for feature classification in the context of SHM. In this process, the strengths and limitations of some actual statistical techniques used to detect damage in the structures are discussed. In the hierarchical structure of damage detection, this report is only concerned with the first step of the damage detection strategy, which is the evaluation of the existence of damage in the structure. The data from this study and a detailed description of the test structure are available for download at: http://institute.lanl.gov/ei/software-and-data/.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
961604
Report Number(s):
LA-14393
TRN: US200920%%193
DOE Contract Number:  
DE-AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; ALGORITHMS; MECHANICAL STRUCTURES; DAMAGE; DETECTION; MONITORING; PATTERN RECOGNITION; LABORATORY BUILDINGS; SIMULATION

Citation Formats

Figueiredo, Eloi, Park, Gyuhae, Figueiras, Joaquim, Farrar, Charles, and Worden, Keith. Structural health monitoring algorithm comparisons using standard data sets. United States: N. p., 2009. Web. doi:10.2172/961604.
Figueiredo, Eloi, Park, Gyuhae, Figueiras, Joaquim, Farrar, Charles, & Worden, Keith. Structural health monitoring algorithm comparisons using standard data sets. United States. doi:10.2172/961604.
Figueiredo, Eloi, Park, Gyuhae, Figueiras, Joaquim, Farrar, Charles, and Worden, Keith. Sun . "Structural health monitoring algorithm comparisons using standard data sets". United States. doi:10.2172/961604. https://www.osti.gov/servlets/purl/961604.
@article{osti_961604,
title = {Structural health monitoring algorithm comparisons using standard data sets},
author = {Figueiredo, Eloi and Park, Gyuhae and Figueiras, Joaquim and Farrar, Charles and Worden, Keith},
abstractNote = {The real-world structures are subjected to operational and environmental condition changes that impose difficulties in detecting and identifying structural damage. The aim of this report is to detect damage with the presence of such operational and environmental condition changes through the application of the Los Alamos National Laboratory’s statistical pattern recognition paradigm for structural health monitoring (SHM). The test structure is a laboratory three-story building, and the damage is simulated through nonlinear effects introduced by a bumper mechanism that simulates a repetitive impact-type nonlinearity. The report reviews and illustrates various statistical principles that have had wide application in many engineering fields. The intent is to provide the reader with an introduction to feature extraction and statistical modelling for feature classification in the context of SHM. In this process, the strengths and limitations of some actual statistical techniques used to detect damage in the structures are discussed. In the hierarchical structure of damage detection, this report is only concerned with the first step of the damage detection strategy, which is the evaluation of the existence of damage in the structure. The data from this study and a detailed description of the test structure are available for download at: http://institute.lanl.gov/ei/software-and-data/.},
doi = {10.2172/961604},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2009},
month = {3}
}

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