part 1.4: vibration-based shm: lab tests

Part 1.4:Vibration-based SHM: lab tests

Rosario Ceravolo

Politecnico di TorinoDep. Structural Engineering

UNIVERSITA’ DI TRENTOCourse on: «Identification and Control of Dynamical Systems»

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Bridge scour: a serious threat

The bridge scour is the lowering of the level of theriverbed by water erosion which leads to theexposure of the foundations

Historical arch bridges are particularly sensitive tothe loss of the bearings and the foundationsettlements

The opening of cracks in the arch barrels results tothe formation of hinges which leads to collapsemechanisms

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Validation of vibration-based SHM techniques

Experimental investigation of theeffects of foundation settlements(scour) on a half-scaled model ofmasonry arch bridge

Validation of different noveltydetection techniques on anartificially damaged structure

acceleration acquisitions on the healthy and damaged structure produced byambient noise and sledge hammer impacts

scour-like damage introduced by means of a purposely designed settlementapplication system

signal processing, features extraction from time, frequency and spectral domains,damage detection through data-driven algorithms

(in cooperation with G. Ruocci, A. Quattrone, A. De Stefano, K. Worden)

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The damage scenario: scour simulation

Screws and bearings tointroduce differential settlements

Settlements applicationsystem

Hydraulic flume tests Scour profile image monitoring

Numerical simulation andsettlements calculation

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The damage scenario: differential settlements

Reference

DS 0 DS 1 DS 2 DS 3

30cm polystyrene removed 40cm polystyrene removed 60cm polystyrene removed 75cm polystyrene removed

0.5mm settlement applied 1.5mm settlement applied 2.5mm settlement applied

Free and forced (hammer impacts) vibration measurements after each damage state

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The damage scenario: damage effects

Detachment between the arch barrels and the spandrel walls

Cracks opening along the mortar joints at the arch barrels intrados

Damage effects observed after the application of the last settlement step

Politecnico di TorinoDept. of Structural Engineering

PHYSICAL MODEL OF A TWO SPAN MASONRY BRIDGE

System identification

SHM: Model-driven approach

Post-Processing Symptom-based decision

Sensor

Pre-processing

Post-processing Model-based decisionModel updating

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The dynamic response tests

orthogonal toarch barrels

longitudinalto the pier

transversal to the pier

transversal tospandrel walls

vertical on spandrel walls

18 monoaxial accelerometers

Experimental setups:

several sensors configurations investigated

signals acquired with 400Hz sample frequency

Sensors locations:



Modal frequencies and shapes from the FE model

Mode I – 33.74 Hz Mode II – 33.75 Hz Mode III – 36.81 Hz

Mode IV – 50.94 Hz Mode V – 60.33 Hz Mode VI – 63.15 Hz



From November 2008 to March 2009 several dynamic testingcampaigns were executed.

A single acquisition used 18 uniaxial accelerometers, which weredistributed according to different set-ups, so as to capture the 3Dresponse.



Excitation sources

Ambient

Impact (instr. sledge)

Shaker

Identif. techniques

SSI (ambient)

ERA (impact)

EMD-HHT (impact,non-linear)



Identified frequencies and shapes (ERA method from impulsive testsignals)

Mode I – 35.90 Hz Mode II – 37.20 Hz Mode III – 37.60 Hz

Mode IV – 46.50 Hz Mode V – 60.40 Hz Mode VI – 63.3 Hz

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PHYSICAL MODEL OF A TWO SPAN MASONRY BRIDGEFrequency vs Time

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80 100 120 140 160 180 200 220 240Days after building

Freq

uenc

y [H

z]

MODE I

MODE II

MODE III

MODE IV

MODE V

MODE VI

Damping ratio vs Time

0.00

0.01

0.01

0.02

0.02

0.03

0.03

0.04

80 100 120 140 160 180 200 220 240Days after building

Dam

ping

ratio

[-]

MODE I

MODE IV

MODE VI



Frequency vs Damping - MODE I

November 2008

January 2009February 2009

March 2009

0.000

0.010

0.020

0.030

34 35 36 37

Frequency [Hz]

Dam

ping

ratio

[-]

Frequency vs Damping - MODE II

November 2008

January 2009

February 2009

March 2009

0.000

0.010

0.020

0.030

0.040

0.050

0.060

32 33 34 35 36 37 38Frequency [Hz]

Dam

ping

ratio

[-]

Frequency vs Damping - MODE III

November 2008

January 2009

February 2009

March 2009

0.000

0.010

0.020

0.030

0.040

0.050

0.060

36 37 38 39 40 41Frequency [Hz]

Dam

ping

ratio

[-]

Frequency vs Damping - MODE IV

November 2008January 2009

February 2009

March 2009

0.000

0.010

0.020

43 44 45 46 47Frequency [Hz]

Dam

ping

ratio

[-]

Frequency vs Damping - MODE V

November 2008

January 2009

February 2009

March 2009

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

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0.090

54 55 56 57 58 59Frequency [Hz]

Dam

ping

ratio

[-]

Frequency vs Damping - MODE VI

November 2008January 2009

February 2009

March 2009

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

57 58 59 60 61 62 63 64Frequency [Hz]

Dam

ping

ratio

[-]

Accuracy afforded by ERA in damping estimation (impact tests)

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Model driven: symptom based

DS 0 DS 1 DS 2 DS 3Reference

Natural frequencies identified from the responses to the hammer impacts

0 50 100 150 200 250 30015

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Observations

Freq

uenc

y [H

z]



Sensor

Pre-Processing

Feature Extraction

Post-Processing

Pattern Recognition

Decision

SHM: Data-driven approach (Ruocci, PhD thesis 2010)

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Features extraction: time domain

15

15

Free vibration acquisitions in selected locations for each damage state

Band-pass signals filtering about the first identified frequency

RMS calculation and correlations of signals between opposite sensors

Detection of asymmetric effects introduced by the scour event as reliable damage symptoms

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Features extraction: frequency domain

0 100 200 300 400 500 6000

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Spectral lines

Tra

nsm

issi

bilit

y M

agni

tude

i

j

k

FFT

kj

kikji A

ATF

,

,,,

FFTTransmissibility function plot

selected spectralrange

Computation of transmissibility functions for each damage state

Optimization of the selected sensors couples, impact locations and spectral range

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Pattern recognition: KDE method

The cross-correlation between two independent random variables can be expressed by meansof the Probability Density Function (PDF) of their difference

The PDF can be estimated from a data set using the Kernel Density Estimation (KDE)method which assumes that data are normally distributed

The residuals are divided by the RMS of the selected signals in order to normalize the data andallow the comparison between different states

Couples of signals highly correlatedare represented by sharp Gaussianbells

-1.5 -1 -0.5 0 0.5 1 1.50

0.5

1

1.5

2

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3.5KDE

Residuals

Est

imat

ed P

DFLoss of correlation is related to most

probable high values for residuals(smooth bells) and may be seen assymptom of a change in the system:damage occurrence or a change inthe boundary conditions

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Pattern recognition: Outlier Analysis

Statistical method used to detect novelties as deviations from a pre-defined normal condition

xxSxxD T

1

The discordancy value is compared with a statistically computed threshold

If the value is greater than the threshold the novelty is detected and damage can be inferred

D

x

x

S reference sample covariance matrix

reference sample mean vector

single observation vector

novelty index = Mahalanobis squared distance 1p

ppp

Here used to assess the discordancy between the features selected in the frequency andspectral domain: natural frequencies and transmissibility functions

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Damage detection results (decision) : KDEmethod

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-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1-1

0

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6KDE

Residuals

Est

imat

ed P

DF

Reference

DS 0

DS 1 DS 2

DS 3

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Decrease of correlation withdamage states of increasingextent for positionslongitudinally symmetric

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Damage detection results: KDE method

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Decrease of correlation withdamage states of increasingextent for positionslongitudinally symmetric

15

15

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1-1

0

1

2

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6KDE

Residuals

Est

imat

ed P

DF

Reference

DS 0

DS 1 DS 2

DS 3

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Damage detection results: KDE method

15

15

High correlation invariantwith damage states ofincreasing extent forpositions transversallysymmetric

15

15

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1-1

0

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7KDE

Residuals

Est

imat

ed P

DF

Reference

DS 0

DS 1 DS 2

DS 3

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Damage detection results: Outlier Analysis

Natural frequencies of all the first 6 identified modes selected as features

0 50 100 1500

100

200

300

400Outlier Analysis: Natural Frequencies for best NF

samples

Mah

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Squ

are

Dis

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0 50 100 1500

10

20

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50Outlier Analysis: Damping Ratios for best NF

samples

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DS 1 DS 2DS 0 DS 3

INLIERSOUTLIERS

Reference

Novelty detected since the second damage stage: perceptible increasing trend for the noveltyindex with the damage stages

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0 100 200 300 400 500 6000

1

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Spectral lines

Tran

smis

sibi

lity

mag

nitu

de

1317

6

TF13,17,6

Damage Step 1

Damage Step 0

Reference

Damage Step 2

Damage Step 3

sampled TFspectrum

Results of the optimization for the selection of sensors couple, impact location and spectral range

Transmissibility functions

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Transmissibility functions

0 500 1000 1500 2000 2500 3000 3500 4000100

101

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106

Samples

Mah

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squ

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DS 1 DS 2DS 0 DS 3

INLIERS

OUTLIERS

Reference

High damage sensitivity

Novelty detected sincethe first damage stage

Damage index valueincreases with the stepsproviding a damagequantification in arelative manner

part 1.4: vibration-based shm: lab tests

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