Upload
ledan
View
216
Download
0
Embed Size (px)
Citation preview
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Structural Health Monitoring:
stato dell’arte e sviluppi futuri
ALESSANDRO DE STEFANO, Politecnico di Torino
ANTONINO QUATTRONE, Politecnico di Torino
Gli autori ringraziano per la fruttuosa collaborazione Emiliano Matta, libero professionista, e Gianluca
Ruocci, emigrato all‟Ecole de ponts et chaussées di Parigi, brillanti ricercatori che l‟Università italiana
non sa trattenere.
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Structural Health Monitoring:
?How
?forlooktoWhat
?isWhat
What is?: observation and measurement programme making sense
only if strictly related to ordinary maintenance, residual life and safety
assessment, decision making support about critical maintenance actions
PAY ATTENTION! “Monitoring” is often associated to “damage
detection”. In fact not every damage is relevant to residual life, not
every damage can be easily detected by monitoring actions!
NEED OF RISK ANALYSIS!
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
What to look for and How?
WG 1, ISHMII-CSHM 1, Waikiki, Honolulu, 2004 MEASURABLE ENTITIES
EVENTS Strain Defor-
mation Accele- ration
Tempe- rature
Geo- metry
Ima-ge Electric potential
Acoustic emission or attenua- tion
Chemicals, including moisture
Magnetic properties Research needed
Fire FAIR FAIR POOR GOOD 1 2 FAIR Y
Explosion GOOD FAIR 1 High priority
Collision to Girders and columns
FAIR POOR1 POOR
2 Y
Earthquake FAIR2
POOR1
GOOD FAIR Y
Scour 3 4 High priority
Traffic loads
6, 5 5 GOOD GOOD 7 Especially WIM
Wind GOOD FAIR GOOD GOOD Y
Corrosion FAIR FAIR POOR
High priority for corrosion of prestressing tendons, current methods make indirect measurements
only
Structural fatigue
FAIR Y for fatigue of bridge deck
slabs
Dead Load GOOD GOOD GOOD
Notes:
1. Important, but difficult to measure
2. Current method too tedious
3. Change in column strains can detect effects of scour
4. Ultrasonic imaging has been used to map erosion due to scour
5. Laser vibrometer and tiltmeters can be used to monitor traffic
6. Weighing-in-motion (WIM)
7. Laser scanner can be used to detect change in geometry due to traffic
8. Forces in cables of cable stayed bridges have been obtained from
their vibration characteristics
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
RISK ANALYSIS
how reliable the existing structure is to carry current and futureloads and to fulfill its task for a given time period?
Structural assessment
• Structural models
• Deterioration mechanisms
• Material resistances
• Geometries
• Measurements error
• Loads
UNCERTAINTIES (structural, epistemic,
social behavior related)
Stochastic approachesVARING
IN TIME
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
RISK ANALYSIS (linerized simplified model)
Risk= Prob[pS<pR]
Probability function of loading model
Probability function of resistance model
FAILURERisk can be seen as the convolution between Hazard and Vulnerability
Hazard: probability that in a time t an external (like earthquakes, floods..) or internal (material degradation, fatigue..) event capable of causing damage occurs
Vulnerability : Conditional probability that, when an event occurred, the whole structure or a part of it suffers a predefined damage
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Approach to safety:
from variable time to symptom
Probability that the time it takes a system to reach a damage limit stateassociated to a damage admissible level, tb, is greater than a generictime t
•Symptoms can be regarded as evolutionary and sudden changes in observable
qualitative properties and/or measurable responses.
•Correlating symptoms to damage can require a knowledge based direct search or
direct incomplete knowledge supported by a model based predictive assessment.
RISKb
ttPtR 1)()( Reliability based on time:
Reliability based on symptom:
S
dSflSSbSSPSR s)()(
Probability that a system, which is still able to meet the requirements for
which it has been designed (S<Sl), is active and displays a value of the S
smaller than Sb
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
1) from Thomas B. Messervey , Integration of Structural Health Monitoring into the Design, Assessment, and Management of Civil Infrastructure , Ph.D. Thesis
Lifetime structural performance without maintenance and with maintenance (1)
EFFECTS OF MAINTENANCE ON STRUCTURAL PERFORMANCE
Preventive maintenance• Increase in performance • Decrease in the rate of deterioration
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
• The reliability deterioration profile refers to deterioration of a measure of structural performance, defined by b
CONDITION AND RELIABILITY DETERIORATION
• The condition deterioration profile refers to deterioration in VISUAL terms of singular components of the structure. deterioration occurs at discrete intervals using a stochastic process based on historical records
Reliability Index (t=0 Overdesign)
22
SR
SR
b
μR ,μS :mean values resistance and load effect
σR ,σS :SD of resistance and load effect
Combined effects of different failure modes can be capture by reliability deterioration profiles
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
In general, the capacity (resistance) of a structure decreases over time as the structure deteriorates and the load demand increases.
Reaching unacceptable performance (or collapse) during the operational lifetime
GOAL: • Prediction of a realistic life cycle performance of the structure and his singular components (deterioration models )• Define effective maintenance and risk mitigation programs
Time of Failure
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Reliability of the monitored system
R0(S) primary reliability that applies to a given type of
systems;
R(S,L) reliability characterised for the particular system by the
introduction of a logistic vector Li ;
Li denotes the individual element of the sample, it may contain a series of
specific parameters depending on which aspect of the system we want to
monitor.
It is defined:
S
dxLxhLSR
0
),(exp),(
and putting h(S, L) = h0(S) g(L),
where g(L) is an unknown function to be defined , in the assumption of
small changes of L :
))(0ln(1)0,(00
),( SRL
gTLLSRLL
LSR
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Symptom Observation Matrix (SOM)
S(i,1) S(i,2) S(i,3) S(i,N)
Column 3:
set of observations of the symptom 3
Row i:
set of symptoms
at observation i
LOGISTIC VECTOR Li
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
• The SVD is an “exact” decomposition and it leads to
optimized orthogonal components;
• SOM[p,r]=U[p,r]*SV[diag r,r]*VT[r,r]
rr
p
=
pxrpxr rxr diag rxr
SOM
U
VT
Unitary
matrix
(UUH=UHU= I):
Scaling
factors
Unitary matrix
(VVH=VHV= I):
xSV x
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
• Important property of SVDGiven an integer number s <r the sum
s
∑
supplies the optimal approximation of SOM given the reduced rank s
Suppose that s=1
In such case SOM is approximated by a one-dimension vector product:
Approx1(SOM)[pxr]≈u1[px1]xSV1xv1T[1xr]
k=1ukxSVkxvT
k
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
r
p
≈
u1(px1)
rxr diag rxr
Approx1(SOM)
SV
SV1vT
1(1xr)
xxx
u1(j) is not equal but not far the average of the jth row of SOM and can be considered as
a a set of values representative of the state of the structure at each jth observation. A set
of damage states shall be associated to each u1(j) value through a multi-model based
exploration.
VT1(i) is not equal but not far from the average of the ith column of SOM and can be
considered as a set of representative values of the significance of each ith symptom
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
• Given the previously stated optimal approximation property, the
difference: SOM –Approx1(SOM) is a matrix of residuals (or errors)
with the minimum possible Frobenius norm (i.e. the minimum sqare
error), compatible with any one-dimensional approximation of SOM.
• In Approx1(SOM) the vector v1 , given that the SOM columns are
effectively centered and normalized, can be interpreted as average,
invariant significance of the symptoms along the observation process,
whilst the vector u1 contains a set of numbers that can be correlated
with the damage state of the structure, influenced but not strictly
governed by the scattering and evolution of symptoms.
• Approx1(SOM) leads to the best first order linearized
assessment of the damage evolution and symptom significance.
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
• Once removed the shadow of the first dominating
components, the residual matrix can bear indirect information
on defects or damages.
• The u2 and v2vectors of the SOM are also the first order
orthonormal expansions of the residual matrix of the first
order approximation
• The SVjs associate to each corresponding couple of singular
vectors a weight (scaling factor); It is reasonable to expect
that as much their values are lesser than SV1 so much better
and more robust is the first order assessment and less
relevant the contribution of higher order components.
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Life-Cycle Assessment
Civil structures have long service live . During this period are subjected to:
Lifetime
CONTINUOS TIMEExposure to aggressive environmental stressors:• heating/cooling cycles• loading cycles• increasing of loads• chloride attack
DISCRETE TIMEAbnormal loadings:• earthquakes• floods• very strong winds• fire• vehicle impact
REDUCTION OF THE CAPACITY OF SAFETY CARRY LOADS
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
• Estimate the range of lifetime
• Evaluate the deterioration model in service lifetime
STEPS TO THE EVALUATION OF STRUCTURAL PERFORMANCE
Sources of impact affecting the structural performance:
loads effects impact of increasing loads level (i.e. traffic loads on bridges) environmental influences (temperature, radiation, frost action) degradation due to chemical exposure
• Inclusion of discrete time events (expectation model and real observation)• Assessment criteria of real degradation progress
Structural Health Monitoring (SHM)
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
MONITORING STRATEGIES
• PERIODIC VISUAL INSPECTION WITH DESCRIPTION OF CONDITION
• PERIODIC VISUAL INSPECTION WITH CLASSIFICATION OF CONDITION IN TERMS OF DEGRADE AND VULNERABILITY
• PERIODIC VISUAL INSPECTION WITH AMBIENTAL DYNAMIC TESTS
• PERMANENT MONITORING AND PERIODIC VISUAL INSPECTION
• ON-LINE MONITORING WITH INTEGRATED RISK ANALYSIS AND DIRECT INSPECTION IN WARNING CASES
Actually, most of existing bridge management programs (BRIDGIT, PONTIS in USA), are almost exclusively based on visual inspections (second approach).
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
PERMANENT MONITORING
Static monitoring
• displacements
• cracks opening
• chemical exposure
• Pressure
• …
Dynamic monitoring• Accelerations (modal parameter)• Strains•Absolute position (GPS, Radar scanner)•Temperature, humidity, wind•Weigh in motion
STRUCTURAL HEALTH MONITORING (SHM)
LOCAL RESPONSE GLOBAL RESPONSE
Global dynamic monitoring systems provide useful information to understand the
structural behaviour and to detect the damage symptoms
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Vibration-based SHM approachAimed at the characterisation of the structural health state via a non-destructive assessment
Share the same goals of visual inspections overcoming limitations in a more automatic way
Detection of damage symptoms among the features extracted from vibration signatures
The damage assessment process can be subdivided into 4 steps:
Farrar, C. R. , Doebling, S. W., Nix, D. A., (2001) “Vibration-based structural damage identification”, Phil. Trans. R. Soc. A., 359, pp. 131-149.
OPERATIONAL EVALUATION
Definition of likely damage affecting the structure
Definition of the operational condition of the monitoring system and data
acquisition limitations
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Vibration-based SHM approachAimed at the characterisation of the structural health state via a non-destructive assessment
Share the same goals of visual inspections overcoming limitations in a more automatic way
Detection of damage symptoms among the features extracted from vibration signatures
The damage assessment process can be subdivided into 4 steps:
Farrar, C. R. , Doebling, S. W., Nix, D. A., (2001) “Vibration-based structural damage identification”, Phil. Trans. R. Soc. A., 359, pp. 131-149.
OPERATIONAL EVALUATION
DATA ACQUISITION AND CLEANSING
Design of the sensing system (type, number, location of sensors)
Definition of the acquisition and sampling frequency, data normalization
and noise reduction
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Vibration-based SHM approachAimed at the characterisation of the structural health state via a non-destructive assessment
Share the same goals of visual inspections overcoming limitations in a more automatic way
Detection of damage symptoms among the features extracted from vibration signatures
The damage assessment process can be subdivided into 4 steps:
Farrar, C. R. , Doebling, S. W., Nix, D. A., (2001) “Vibration-based structural damage identification”, Phil. Trans. R. Soc. A., 359, pp. 131-149.
OPERATIONAL EVALUATION
DATA ACQUISITION AND CLEANSING
FEATURE SELECTION
Model/Non-model based sensitivity analysis to identify the best features
Condensation of significant information in reduced-size features vectors
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Vibration-based SHM approachAimed at the characterisation of the structural health state via a non-destructive assessment
Share the same goals of visual inspections overcoming limitations in a more automatic way
Detection of damage symptoms among the features extracted from vibration signatures
The damage assessment process can be subdivided into 4 steps:
Farrar, C. R. , Doebling, S. W., Nix, D. A., (2001) “Vibration-based structural damage identification”, Phil. Trans. R. Soc. A., 359, pp. 131-149.
OPERATIONAL EVALUATION
DATA ACQUISITION AND CLEANSING
FEATURE SELECTION
STATISTICAL MODEL DEVELOPMENT
Implementation of damage assessment algorithms to detect, localize,
classify and quantify damage
Testing of the reliability of the developed model in terms of features
sensitivity and false indications
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
SHM techniques use the data of monitoring system applying damage detection techniques to track the healthy state of the structure
Damage detection levels
• Detection: Is damage present?
• Localization: Where is the damage located?
• Diagnosis: How severe is the damage?
• Prognosis: What is the remaining safe
lifetime?
STRUCTURAL HEALTH MONITORING (SHM)
Data-driven damage detection
Model-based damage detection
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Experimental background: accuracy of dynamic
identification techniques (OMA)
FREQUENCY TIME TIME-FREQUENCY
Welch PSD
Ewins-Gleeson
Dobson
Kennedy-Pancu
Spectral Multimatrix
ARMAV
ERA
PRTD
SSI
Time-Frequency
Istantaneous Estimator
(TFIE)
Wavelets, packet
wavelets
FDD
Non stationary input
Stability of phase: good for higher damping
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Data-driven damage assessment
Data-driven techniques can be utilized to avoid direct dependence on analytical models.
• Novelty/outlier analysis• Statistical methods• Direct interpretation of sympthoms
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
The masonry bridge experimental model
1.60m
1.75m
5.90m
Case study of a national research project concerning the surveillance and maintenance of historical structures and infrastructures
Application of pier settlement
Damage states of increasing extent
Global monitoring: vibration tests
Model-based damage assessment
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Polit
ecnic
o d
i T
orino –
Dept. o
f S
tructu
ral E
ngin
eering
The damage scenario: scour simulation
Screws and bearings to
introduce differential
settlements
Settlements
application
system
Hydraulic
flume tests
Scour profile
image monitoring
Numerical simulation
and settlements
calculation
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Polit
ecnic
o d
i T
orino –
Dept. o
f S
tructu
ral E
ngin
eering
The damage scenario: differential settlements
Refer
ence
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
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Polit
ecnic
o d
i T
orino –
Dept. o
f S
tructu
ral E
ngin
eering
The dynamic response tests
orthogonal to arch barrels
longitudinal to the pier
transversal to the pier
transversal to spandrel walls
vertical on spandrel walls
18 monoaxial accelerometers
Experimental setups:
several sensors configurations investigatedsignals acquired with 400Hz sample frequency
Sensors
locations:
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Damage detection algorithmDATA ACQUISITION
AND CLEANSING
FEATURE
SELECTIONSTATISTICAL MODEL
DEVELOPMENT
OPERATIONAL
EVALUATION
PATTERN RECOGNITION: Outlier Analysis
Statistical method which detects novelties as deviations from normal condition
The first session data set assumed as reference condition
xxSxxDT
1
D
x
x
S reference sample covariance matrix
reference sample mean vector
single observation vector
novelty detector scalar value
(Mahalanobis squared distance MSD)
0 100 200 300 400 500 6000
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Spectral lines
Tra
nsm
issib
ility M
ag
nitu
de
TF(ωi)
…
TF(ωk)
…
…
TF(ωN)
Outlier Analysis
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Damage detection algorithmDATA ACQUISITION
AND CLEANSING
FEATURE
SELECTIONSTATISTICAL MODEL
DEVELOPMENT
OPERATIONAL
EVALUATION
PATTERN RECOGNITION: Outlier Analysis
0 50 100 150 200 250 300 3500
50
100
150
200
250Outlier Analysis: Natural Frequencies for best NF
samples
Ma
ha
lan
ob
is S
qu
are
Dis
tan
ce
0 50 100 150 200 250 300 3500
20
40
60
80
100
120Outlier Analysis: Damping Ratios for best NF
samples
Ma
ha
lan
ob
is S
qu
are
Dis
tan
ce
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
The fitness of each solution is expressed as the area obtained subtracting the threshold value
from the series of the Outlier Analysis results and maximised by the genetic optimisation
TF(ωi)
…
TF(ωk)
…
…
TF(ωN)
Outlier
Analysis
Fitness
Outlier Analysis
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
• Model-based damage assessment methods compare the
measured structural response with a numerical simulation
generally provided by a FE model
• The model accuracy is essential to supply a reliable image of the structural health
Model-based damage assessment
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
The Model Updating
Mismatch between the experimental
and the numerical modal parameters
Unfeasibility to apply a model-based
approach to damage assessment
Unreliable definition of the reference
“healthy” state of the model
Model updating techniques try to solve the problem but generally deterministic approaches
fail because the unique optimal solution they pretend to find is prevented by the inverse
nature of the problem.
The final result is biased
by several errors and
uncertainties sources
referred to:
the experimental measurements
the modal identification results
the simplified modelling assumptions
the construction complexity
A single optimal
solution is an
hard task to
accomplish!
At list a “regularization” (e.g.: Tikhonov) is required to reduce the uncertainties,
but the regularization is not able to resolve the ambiguities
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
The stochastic Model Updating
The stochastic model updating
methods can deal with
uncertainties and problem
complexity in a robust way
probabilistic representation of the
structural updating parameters
the updating output is a class of
reliable models selected among
all the generated solutions
multiple model generation driven
by the parameters probability
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Method outline
• Sensitivity analysis
• Parameters ranges estimation
• Models generation
• Preliminary models selection
• Solutions analysis and clustering
definition of the most
sensitive parameters
reduction of the output
research space
creation of a large models population
by an optimisation algorithm
exclusion of the less
reliable solutions
application of data mining techniques
to group the selected models
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
In Civil Engineering, caution is needed even for simple systems to avoid ill-conditioning, especially when ambient vibration is used, and both
stiffness and mass parameters are unknown
Data Model parameters
Experimental modal analysis + FE model calibration:
AN INVERSE PROBLEM
IN THIS WORK
results from the JETPACS case study are presented to highlight some crucial robustness issues in vibration-based model-
updating and suggest possible criteria to improve reliability
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Introduction
DPC-ReLUIS 2005-08 ProjectJETPACS (Joint Experimental Testing on Passive and
semi-Active Control Systems)
8 Universities involved in the assessment ofenergy dissipation devices for seismicprotection.
A representative FE model isdesired, to be shared by allparticipants for testcalibration andinterpretation.
A preliminary campaign of dynamic tests is conducted, and subsequent attempts, by various partner Research Units, to a parametric identification. Structural Engineering Lab at the
University of Basilicata
Controldevices
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
FE model-updating (2/3)
In iterative approaches, model-updating = optimization problem, where discrepanciesbetween numerical and experimental results are set as an objective function to beminimized by making changes to a pre-selected set of parameters in the FE model
multiple sets of relatively few parameters are selected (and independently solved) basedon:- direct a-priori knowledge, and/or- extensive simulation identifying most plausible condition states (or damage scenarios)
Comparing the resulting multiple solutions enhances reliability
Since large sets of parameters may lead to ill-conditioning
Multi-model approach1:
1 Smith et al. 2006
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
FE model-updating (3/3)
Model 1stiffness matrix depending only on
the lower columns’ stiffness
Model 2like 1 + upper columns’ stiffness: no
real (physical) improvement
Model 3like 1 + beams’ stiffness: reasonably the
best physical matching
Results: Model 1
Model 2
Model 3
Imp
rovi
ng
Icx,2, Icy,2
Ibx,1 = Ibx,2
Iby,1 = Iby,1
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
FE model-updating (3/3)
Results: Model 1
Model 2
Model 3
Imp
rovi
ng
HENCE
A) Even for simple structures, model-updating is not an easy task, and a multiple-model approach should be accomplished to depict at which extent resultsdepend on the (arbitrary) choice of the parameters‘ set.B) Small absolute values of fob are not per se a reliable index of successfulupdating.C) Observing how the solution improves through enlarging the updating set mayprovide useful information on its optimal dimension (and on data redundancy).D) Improvement in fob should always be judged in relative terms: passing from0.827% to 0.753% may indeed represent a drastic improvement, corresponding toa significantly different solution.E) Since small improvements in fob may be so important, every care must be takento minimize all sort of possible errors (in measuring, identification, optimization,...).
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Conclusions
1) even for simple systems and apparently redundant data, the solution may beextremely sensitive to the choice of the updating parameters as well as tomodelling errors
2) testing the whole procedure on a simulated model prior to the real model mayprovide a helpful insight into such dependence
3) spanning alternative modelling assumptions (multi-model approach) is aneffective strategy to increase calibration robustness
4) the influence of unaccounted secondary structural elements (braces) may actas a severe misleading factor in system identification.
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Integrated European Industrial
Risk Reduction System
“Risk” is the key word of the European
Research Project IRIS (Integrated European
Industrial Risk Reduction System )
MotivationAt present the European Industry recognised their obligation to reconsider risk and safety policies, having a more competitive industry and more risk informed and innovation accepting society in vision. Therefore the large collaborative project IRIS is proposed to identify, quantify and mitigate existing and emerging risks to create societal cost-benefits, to increase industrial safety and to reduce impact on human health and environment.
Project OutlineThe project is led and driven by industry to consolidate and generate knowledge and technologies which enable the integration of new safety concepts related to technical, human, organizational and cultural aspects. The partnership represents over 1 million workers. The proposed project integrates all aspects of industrial safety with some priority on saving human lives prior cost reductions and is particular underpinning relevant EU policies.
from
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
Objectives
• Integrated Methodologies for pioneering Risk Assessment and Management
• New Knowledge-based Safety Concepts
• Total Safety of Industrial Systems and Networks
• Knowledge and Technologies for Risk Identification and Reduction
• Online Monitoring with Decision Support Systems
• Pattern Recognition in Signal Processing
• Demonstration & Technology Transfer
• Standardization & Training Activities
Integrated European Industrial
Risk Reduction System
IL MONITORAGGIO DELLE STRUTTURE NELLA PREVENZIONE DEL RISCHIO SISMICO, ROMA, 20-01-2011
Structural Health Monitoring: stato dell’arte e sviluppi futuri A. DE STEFANO & A. QUATTRONE, Politecnico di Torino
• USA - FHWA Long-term Bridge Performance Program (about 25 MUSD)
• International Guidelines for the Selection and Management of Technology Applications to Bridges
• Prepared by
• United States: A.E. Aktan, F.L Moon, S. Chase, D. Mertz, and N. Gucunski
• International: H. Wenzel (Austria), Y. Fujino, (Japan), D. Inaudi (Switzerland), J. Brownjohn (U.K.), H. Soon (Korea), and H-Y Koh (Korea)
• Background and Introduction
• The objective of the guidelines will be to aid infrastructure owners and practicing bridge engineers in the selection and management of sensor technology applications to bridges. It is stressed that this document is not intended to be a „how to‟ guide related to the use of sensors. Rather it will aim to serve as a guide to those who are tasked with the critical responsibilities of (1) identifying the need for sensor technology, (2) ensuring that appropriate approaches are selected, (3) managing the project and ensuring the established best practices are followed throughout the application, and (4) incorporating the results of the application within the decisions-making process.
– Overview of current bridge engineering and management practice in the US, Europe and the Far East.
– Summary of Bridge Performance Definitions and Metrics and a discussion of how these vary between the US, Europe and the Far East.
– Common objectives of infrastructure owners that drive applications of sensor technology to bridges
– Brief history and description of current practice of technology applications to bridges including brief discussions of proof testing, load testing, NDE, modal analysis, long-term monitoring, etc.
– Challenges related to employing technology to help inform decisions, inclusive of a wide range of issues such as owner/engineer risk aversion, lack of standards and accountability, cost, liability and indemnification, and the coordination between teams with diverse skill sets, among others.
– Brief outline of the lessons learned over the last 30 years and a discussion of strategies that may allow for more wide-spread and effective applications of technology in the future.
– Outline and summary of the report