structural health monitoring -...

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.



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!



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



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



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



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



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



• 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



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



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



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



• 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



• 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



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



• 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.



• 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.



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



• 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)



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).



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



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









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










FEATURE SELECTION

Model/Non-model based sensitivity analysis to identify the best features

Condensation of significant information in reduced-size features vectors










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



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



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



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



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



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



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



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:



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



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



• 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



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



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



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



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



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



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




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




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,...).



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.



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



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



• 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

structural health monitoring -...

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