hoeslywhyte

Information about hoeslywhyte

Published on December 28, 2007

Author: Funtoon

Source: authorstream.com

Content

Optimal Sensor Placement and Damage Detection for Structural Health Monitoring:  Optimal Sensor Placement and Damage Detection for Structural Health Monitoring REUJAT 2004 The University of Tokyo Bridge & Structure Laboratory Nathan Hoesly Catherine Whyte Benefits of Structural Health Monitoring:  Benefits of Structural Health Monitoring Continuous monitoring instead of periodic manual inspections. Measurement data can be used to detect damage that is not visible. Anticipated lower costs than visual inspection of structures. Research Objectives:  Research Objectives Use two types of models to study sensor placement. Simply supported beam model. Continuous beam bridge model. Implement three existing sensor placement techniques to identify damage induced in each model. Examine the importance of minimizing the number of sensors and finding the most effective placement. Bill Emerson Memorial Bridge:  Bill Emerson Memorial Bridge Pier 1 Pier 2 Bridge about 200 km south of St. Louis, and the subject of a collaborative project between WUSTL and Univ. of Tokyo. 32 element simply supported beam model. 30 element continuous bridge model (58 elements is too many for probabilistic method). Assembled using Finite Element Toolbox created by Juan Caicedo at WUSTL. Sensor Placement Analysis (I) :  Sensor Placement Analysis (I) Effective Independence Method (Kammer) Fisher Information Matrix [[ψ]T[ψ]] Effective Independence Distribution Vector: The DOFs with the highest EfI values represent the best sensor locations. Sensor Placement Analysis (II) :  Sensor Placement Analysis (II) Eigenvector Sensitivity Method (Shi) Fisher Information Matrix as distribution of strain energy [[ψ]T [K] [ψ]] The sensitivity matrix for one element is defined as     Then the contribution vector is defined as Sensor Placement Analysis (III) :  Sensor Placement Analysis (III) Damage Measurability Method (Xia) Sensitivity of the damage to the noise in measurement is defined as   Then the damage measurability is defined as where {F} is the contribution of measurement points to a Fisher Information Matrix. Eigenvector Sensitivity Method:  Eigenvector Sensitivity Method 11 sensors 7 sensors 11 sensors 7 sensors Beam Model Bridge Model Effective Independence Method:  Effective Independence Method 11 sensors 7 sensors 11 sensors 7 sensors Beam Model Bridge Model Damage Measurability Method:  Damage Measurability Method 11 sensors 7 sensors 11 sensors 7 sensors Beam Model Bridge Model Damage Detection:  Damage Detection Damage toolbox (Damtool) created by Dr. Jerome Lynch at Stanford University used to determine whether the sensors identified the location of the damage correctly. Continuous damage monitoring can only use ambient excitation sources in most types of civil structures. The first four modes are used to perform damage detection. Each damage case takes about 20 minutes to analyze. Damage Detection Analysis:  Damage Detection Analysis Bayesian Probabilistic Method (Hoon) Bayes’ Theorem Error Function   Numerical Experiment: Test Plan:  Numerical Experiment: Test Plan Accelerometers record only vertical displacements. Each element has a length of 1 meter. 7 and 11 accelerometers, 1% and 2% noise, 7 sets. Random noise is added when mode shapes are computed. Each beam element defined by the Finite Element Model is damaged individually. All damage cases are defined as 10% of stiffness reduction of the selected element. Damtool Procedure:  Damtool Procedure Damage Detection Tree:  Damage Detection Tree Damage Detection Tree:  Damage Detection Tree Damtool Report:  Damtool Report Damtool Report:  Damtool Report Beam Model: Eigenvector Sensitivity Method Results:  Beam Model: Eigenvector Sensitivity Method Results Bridge Model: Eigenvector Sensitivity Method Results:  Bridge Model: Eigenvector Sensitivity Method Results Eigenvector Sensitivity Method Conclusions:  Eigenvector Sensitivity Method Conclusions Beam Model Most consistent method. Not as affected by noise as other two methods. Bridge Model Still stable. Problems identifying damage near the supports and center of the span. Effective Independence Method Conclusions:  Effective Independence Method Conclusions Beam Model Highly influenced by noise and number of sensors. Bridge Model Sometimes better results achieved with 7 sensors than 11 sensors or 2% noise than 1% noise. Damage Measurability Method Conclusions:  Damage Measurability Method Conclusions Beam Model Easily affected by noise. Damtool identifies many damage possibilities with the same probability so there is very little certainty in which is correct. Increased number of sensors did not necessarily improve the quality of damage detection. Bridge Model Performed similarly to eigenvector sensitivity method but fewer damage cases were identified in the center of the span. General Conclusions:  General Conclusions Difficult to locate damage in the regions near the supports and the center of the span. According to our study, the Eigenvector Sensitivity Method seems to perform the best for sensor placement in these structures. Further studies are needed to implement this probabilistic method in more complex structures such as cable-stayed bridges. Many Thanks to::  Many Thanks to: Fujino Sensei (University of Tokyo) National Science Foundation Carlos Riveros (University of Tokyo) Dr. Shirley Dyke (WUSTL) Dr. Makola Abdullah (FAMU) Diego Giraldo (WUSTL) Juan Caicedo (WUSTL) Dr. Jerome Lynch (Stanford University) Terri Norton (FAMU)

Related presentations


Other presentations created by Funtoon

Marketing Mix 4ps
10. 10. 2007
0 views

Marketing Mix 4ps

manners 1
26. 06. 2007
0 views

manners 1

Telecom Seminar 5 20 06
18. 04. 2008
0 views

Telecom Seminar 5 20 06

nuti
10. 04. 2008
0 views

nuti

ch04
07. 04. 2008
0 views

ch04

Anthrax and Pan Flu scenario
30. 03. 2008
0 views

Anthrax and Pan Flu scenario

Software Development Survey
27. 03. 2008
0 views

Software Development Survey

tts
26. 03. 2008
0 views

tts

Tsamboulas
21. 03. 2008
0 views

Tsamboulas

eie1103
18. 03. 2008
0 views

eie1103

Fluid and Electrolyte
02. 01. 2008
0 views

Fluid and Electrolyte

lvmh
26. 06. 2007
0 views

lvmh

Sodium And Water Balance
04. 01. 2008
0 views

Sodium And Water Balance

dot nyc workshop
27. 09. 2007
0 views

dot nyc workshop

Christmas Greetings 02
02. 10. 2007
0 views

Christmas Greetings 02

people around you
03. 10. 2007
0 views

people around you

Impressionismus
12. 10. 2007
0 views

Impressionismus

Pres Feulefack Zeller
29. 11. 2007
0 views

Pres Feulefack Zeller

HydropowerProjects in Nepal
06. 12. 2007
0 views

HydropowerProjects in Nepal

Project Lead The Way
07. 12. 2007
0 views

Project Lead The Way

OHSummarize Sept2003
22. 08. 2007
0 views

OHSummarize Sept2003

SC tudor timeline
22. 08. 2007
0 views

SC tudor timeline

RDML Sharp MINWARA
07. 11. 2007
0 views

RDML Sharp MINWARA

discogenic lbp
17. 12. 2007
0 views

discogenic lbp

How can I miss you
24. 12. 2007
0 views

How can I miss you

A I in the Military
29. 12. 2007
0 views

A I in the Military

Othello Slide Show
02. 11. 2007
0 views

Othello Slide Show

Day1Session10
07. 01. 2008
0 views

Day1Session10

StarryM 4
22. 08. 2007
0 views

StarryM 4

lhj Tudor Sailors
22. 08. 2007
0 views

lhj Tudor Sailors

elec ppt
21. 11. 2007
0 views

elec ppt

World Internet Project Media
23. 12. 2007
0 views

World Internet Project Media

martinez
26. 02. 2008
0 views

martinez

IndiaSinceIndepencen ce
28. 02. 2008
0 views

IndiaSinceIndepencen ce

march frames consumer
26. 06. 2007
0 views

march frames consumer

Manoj
26. 06. 2007
0 views

Manoj

MADHUSHALA
26. 06. 2007
0 views

MADHUSHALA

E Newsletter Aug2006
26. 06. 2007
0 views

E Newsletter Aug2006

Leipzig 02
26. 06. 2007
0 views

Leipzig 02

lecture2 CS598HL
26. 06. 2007
0 views

lecture2 CS598HL

lecture21
26. 06. 2007
0 views

lecture21

lecture13
26. 06. 2007
0 views

lecture13

Lecture 10 Reliability
26. 06. 2007
0 views

Lecture 10 Reliability

13411
23. 11. 2007
0 views

13411

AFD 061206 049
22. 08. 2007
0 views

AFD 061206 049

Elizabeth Suti
03. 12. 2007
0 views

Elizabeth Suti

Mo0PC06 02 Sekar Sari
02. 01. 2008
0 views

Mo0PC06 02 Sekar Sari

corso Haccp
20. 11. 2007
0 views

corso Haccp

nw mn cropping system
04. 10. 2007
0 views

nw mn cropping system

RLEP 2 Overview Bart Graham
13. 11. 2007
0 views

RLEP 2 Overview Bart Graham

himinhvelfingin
14. 11. 2007
0 views

himinhvelfingin

Real time2
22. 08. 2007
0 views

Real time2

le amiche di sergio
26. 06. 2007
0 views

le amiche di sergio

tudor monarchs
22. 08. 2007
0 views

tudor monarchs

daphne OMAN feb04
22. 08. 2007
0 views

daphne OMAN feb04

PickMaster 2 10 Ext Feb 25
07. 01. 2008
0 views

PickMaster 2 10 Ext Feb 25