Seismic Structural Health Monitoring

Carlos E. Ventura
Real-time Damage Assessment
The University of British Columbia
16th World Conference on Earthquake Engineering
Carlos E. Ventura
Department of Civil Engineering
ventura@civil.ubc.ca
January 2017

16th WCEE January 2017
Carlos E. Ventura 2
Saeid Allahdadian
Palle Andersen
Giacomo Bernagozzi
Ruben Boroschek
James Brownjohn
Mehmet Celebi
Mauricio Ciudad Real
Michael Döhler
Charles Farrar
Sharlie Huffman
Yavuz Kaya
Laurent Mevel
Babak Moaveni
Farzad Naeim
Robert Nigbor
Erdal Safak
Martin Turek
Keith Worden
Acknowledgment

Carlos E. Ventura 16th WCEE January 2017
7th Story Building recorded motions
3

7th Story Building recorded motions
4

Carlos E. Ventura
Recorded Motion at 6th floor

Carlos E. Ventura
Column damage

Carlos E. Ventura
Damage Details
Courtesy of Dr. Naeim

Variation of Fundamental Periods since 1970’s

Structural Health Monitoring (SHM)
Structural health monitoring is a question of verification of
constructional design (both short and long term)
• Verification of design
• Active operated guiding system
– Minimizing load to prolong lifetime of object
– Operate close to capacity when necessary (military)
• Damage detection
• Condition based maintenance
10

11
Structural Health Monitoring

Carlos E. Ventura 16th WCEE January 2017 12
Structural
Health
Monitoring
Earthquake
Monitoring
of Structures

Carlos E. Ventura
Vibration-Based SHM
• Hardware: contains all of the
components that deal with
measurement and remote
transmission
• Software: includes tools for post-
processing of measured
information.
Vibration-based SHM can be described by two parts: hardware and software
The output should be something that is of
use for the client
• Acceleration
• Displacement
• Wind
• Temperature
• Strain
• Tilt/deflection
• Differential
GPS
• Other

The Structural Health Monitoring Process
1. Operational evaluation
Defines the damage to be detected and begins to
answer questions regarding implementation
issues for a structural health monitoring system.
2. Data acquisition
Defines the sensing hardware and the data to be
used in the feature extraction process.
3. Feature extraction
The process of identifying damage-related
information from measured data.
4. Statistical model development for feature
discrimination
Classifies feature distributions into damaged or
undamaged category.
• Data Cleansing
• Data
Normalization
• Data Fusion
• Data
Compression
(implemented by
software and/or
hardware)
14

Carlos E. Ventura 15
Stakeholders Needs
Rapid answers to important questions related to the
functionality (or “state of health”) of structures during
and immediately following an event.
The owner needs reliable and timely expert advice on
whether or not to occupy the building following an event.
Data gathered will enable the owner to assess the need for
post-earthquake connection inspection, retrofit and repair
of the building.
After Celebi

16
Bridge Owner’s Concerns
•MoT Responsibility
•aged bridges and rehabilitation challenges
•post earthquake inspections
•keeping the routes open
•Common Needs & Emergency Demands
•Extent of damage
•Loss of economy and recovery times

17
What does MOT need?
• Fast, accurate field intelligence
• Speed of initial response
• Effective risk assessment and
decision making

MOT Post Earthquake Response
• What is damaged – how bad?
– Staffing
– Inspections
– Route condition
• Prioritization
• Changing plans
• Risks/decisions

Understanding Damage

Structural Damage
Damage can be deﬁned as changes introduced into a system, either
intentionally or unintentionally, that adversely affect the current or future
performance of that system.
Failure occurs when the damage progresses to a point where the system
can no longer perform its intended function because a performance
criterion related to strength, stability or deformation-related has been
exceeded.
The cause of the damage and how to prevent it or mitigate its effects,
detection the presence of damage, and how fast damage will growth and
exceed a critical level need to be understood in order to understand
damage in a structural system.

Damage Prognosis
Damage prognosis (DP) is defined as the estimate of an engineered
system’s remaining useful life, and it is based on:
• a quantified definition of system failure,
• assessments of the structure’s current damage state and
• the output of models that predict the propagation of damage in the
system based on some estimate of the system’s future loading.
The main goal of DP is to forecast the performance of a structure by:
• assessing its current state,
• estimating the future loading environments that are likely to affect the
structure,
• predicting via simulations and past experience the remaining useful life
of the structure.

22
Damage Detection Process
‘The four levels’
1) Is the system damaged? – Identify
2) Where is the damage located? – Locate
3) What type of damage is present? – Quantify
4) What is the extent of damage? – Prognosis, Life Span
22

Issues with Damage Identification
Three issues that have an effect on the measured parameters
in a way that interferes with the interpretation of damage are:
1) Environmental conditions (i.e., humidity and temperature)
2) Boundary conditions (i.e. soil properties and soil stiffness,
affected by ground shaking and moisture)
3) Excitation – excitation levels and frequency content
PLUS THE EFFECT OF NOISE IN THE MEASUREMENTS

Need of Damage Prognosis
• The need for DP in earthquake engineering applications has been
demonstrated over and over in past earthquakes.
• Post-earthquake assessment of buildings can take a very long time
(years in some cases) before reoccupation.
• DP can help alleviate the need to perform timely and quantiﬁed
structural condition assessments and then conﬁdently predict how these
structures will respond to future earthquakes, including severe
aftershocks that occur following a major seismic event.
BUT:
The challenge of damage prognosis is to develop and integrate sensing
hardware, data interrogation software and predictive modelling tools that
lead to reliable estimates of the remaining life of the structure.

1) What are the damage cases of concern and how is failure deﬁned for
these damage cases?
2) What future loading conditions will the structure experience?
3) What techniques should be used to assess and quantify the damage?
4) What type of models will be used to predict the damage propagation in
the system?
5) What is the goal of the prognosis?
The most obvious and desirable type of prognosis estimates for
earthquake engineering application is how long the structure can be used
safely before one no longer has conﬁdence in the safety of the structure.
Implementing Damage Prognosis

26
Before Event Data After Event Data
Database:
Modes and Frequencies
Mass and Stiffness
FEM
Loads
Deflections
New Information:
Modes and Frequencies
Mass and Stiffness
FEM
Loads
Deflections
Damage Detection
Process
Condition Assessment
How do we do it?

Damage Detection Methods
1) Based on Bayesian Analysis
2) Based on Control Theory
3) Damage Index Methods
4) Based on Empirical Mode Decomposition and Hilbert-Huang Transform
5) Impedance Based Methods
6) Based on Modal Strain Energy
7) Based on Finite Element Model Updating
8) Based on Neural Network, Novelty Detection, and Genetic Algorithms
9) Based on Principal Component Analysis or Singular Value Decomposition
10) Modal Identification Based Methods:
a. Changes in Natural Frequencies
b. Changes in Flexibility
c. Changes in Frequency Response Functions
d. Changes in Modal Curvature
e. Changes in Stiffness
f. Other Methods
11) Based on Residual Forces
12) Based on Time Domain Data
13) Wavelet Based and Time-Frequency Domain Methods
Adapted from Moaveni

Some early work on damage detection
After Naeim, 1996

29

30

31

32

33

34
After Funaki &Xue
Variation of Fundamental Frequency of the Building

35

36

37

Real-time Damage Detection
• Real-time: information about the structure is made
available as it happens; this includes the
measurement, transfer, processing, interpretation and
delivery to the relevant parties
• Detection: the ability to identify, locate, quantify the
damage; ultimately the remaining life of the structure
can be determined

Carlos E. Ventura 16th WCEE January 2017 - 39
Damage detection
Structure healthy
Measurement in
healthy state
Measurement in
current state
Statistical comparison concerning
the vibration characteristics
Significant change?
Damage
(or something else?)
Control Chart

Research Project 2008 funded by the
‘’Dipartimento della Protezione Civile’’ of
Friuli Venezia Giulia
The bridge on the Fella River (Dogna, Udine)
Case study: Dynamic testing of RC Bridge (artificially damaged)
• Built in 1979
• Four nominally equal spans
- Span length 16.0 m
- Lane width 4.0 m
- Two footways 0.90 m width
• Structure:
- Three longitudinal RC beams of
rectangular cross-section 0.35x1.20 m
and a RC slab deck of 0.18 m
thickness
- RC diaphragms of rectangular cross-
section 0.30x0.70 at mid-span, at the
ends and at span-quarters
• Constraints: longitudinal beams simply
supported at both ends
Exceptional flood – August 31, 2003 Courtesy of F. Benedettini and A. Morassi

Dogna Bridge artificially
damaged

42
Dogna Bridge Control Chart

Carlos E. Ventura 16th WCEE January 2017 - 43
Damage localization
Statistical comparison
concerning
the parameters of a FEM
one statistical test for each element
Element healthy
Significant change?
Element damaged
Finite Element
Model (FEM) of the
structure
Measurement in
healthy state
Measurement in
current state
After M.Dohler

44
DAMAGED STRUCTURE
UNDAMAGED STRUCTURE
A B
X
Y
DAMAGE LOCALIZATION using AV data
X direction
Modal flexibility-based INTERSTORY DRIFTS
Y direction
Damage-induced variations
Z index test - outlier analysis
Bernagozzi G, Ventura CE, Allahdadian S, Kaya Y, Landi L, Diotallevi PP, Application of modal flexibility-based deflections for damage detection of a steel frame
structure. EURODYN 2017 conference, Rome, September 2017

Noise superposition
i i i
 
ND D R

Noise effect in frequency domain
• The lower amplitude frequencies of the data are “drowning” in the noise
• Since the probability distribution of the random number generator is evenly
distributed, the generated noise in the frequency domain is almost even too
(about 6.0 in this figure)

Inter-Story Drifts

0 2 4 6 8 10
0
0.5
1
1.5
2
x 10
4
FOURIER AMPLITUDE
AMP.
FREQUENCY (Hz)
0 2 4 6 8 10 12
-400
-200
0
200
400
TOP-FLOOR RECORD
ACC.
S/N=1, Drift=0.05
0 2 4 6 8 10 12
-400
-200
0
200
400
BOTTOM-FLOOR RECORD
ACC.
S/N=1, Drift=0.05
Yavuz Kaya and Erdal Safak, 5th IOMAC 2013, Guimaraes, Portugal. 13-15 May, 2013

Inter-Story Drifts
0 2 4 6 8 10 12 14 16 18 20
-0.4
-0.2
0
0.2
0.4
EFFECT OF NOISE ON THE CALCULATED INTERSTORY DRIFT
S/N=10 (Ave.Err.=20%)
0 2 4 6 8 10 12 14 16 18 20
-4
-2
0
2
4
S/N=10 (Ave.Err.= x10)
0 2 4 6 8 10 12 14 16 18 20
-40
-20
0
20
40
TIME
S/N=10 (Ave.Err.= x100)
ACTUAL
CALCULATED
5
1
S/N ratio is very sensitive to the calculated inter-story drifts

Inter-Story Drifts

Torre Central Building CHILE
Structure Description
• Reinforced concrete shear and gravity walls.
• 9 stories above ground (30.2 m height).
• 2 underground levels (30x19 plan area).
• Typical wall thickness is 35 cm.
• Typical slab thickness is 25 cm.
After Boroschek et al

Building Modal Properties
Frequency Variations
• ~5 years of data.
• 2010 earthquake
produced a drop on
the frequencies.
• There is a clear
effect of:
– Seismic events
and
– Daily ambient
variations
– Seasonal
Environmental
factors on the
frequencies
variation.
Jun09 Sep09 Dec09 Mar10 Jun10 Sep10 Dec10 Mar11 Jun11 Sep11 Dec11
1.8
2
2.2
2.4
2.6
2.8
3
3.2
Time [Month/Year]
Frequency
[Hz]
Central Tower Frequencies - SSI
f1
f2
f3
Period without data due
to failure in data
acquisition system
Frequency change due to
8.8 Mw 2010 Earthquake

Frequency variations
Rainfall Variations
05/Jun 10/Jun 15/Jun 20/Jun 25/Jun 30/Jun 05/Jul
Frequency 1
55
60
fmax
: 1.983
fmin
: 1.886
Var. [%]: 5.0
Frequency 2
Frequency
[Hz]
55
60
Soil
Saturation
[%]
fmax
: 2.408
fmin
: 2.292
Var. [%]: 4.9
Frequency 3
Time [day/month]
55
60
fmax
: 2.831
fmin
: 2.719
Var. [%]: 4.0

Selected Frequency Histograms
• Data filtered
in a range of
20.5-21.5°C,
and
removing
rain and
earthquakes
effects
• Clear
decrease in
variance of
frequencies.
1.82 1.84 1.86 1.88 1.9 1.92 1.94 1.96 1.98 2
0
0.2
0.4
0.6
0.8
1
Frequency 1
Unfiltered data
mean: 1.906
std: 0.022
Filtered data
mean: 1.892
std: 0.017
2.22 2.24 2.26 2.28 2.3 2.32 2.34 2.36 2.38 2.4
0
0.2
0.4
0.6
0.8
1
Frequency 2
Number
of
Observations
Unfiltered data
mean: 2.318
std: 0.025
Filtered data
mean: 2.304
std: 0.022
2.64 2.66 2.68 2.7 2.72 2.74 2.76 2.78 2.8 2.82
0
0.2
0.4
0.6
0.8
1
Frequency 3
Frequency [Hz]
Unfiltered data
mean: 2.731
std: 0.036
Filtered data
mean: 2.711
std: 0.030

Carlos E. Ventura
Ironworkers Memorial Second Narrows Crossing
Bridge
57
100 Acceleration
• 18 strain gauges
• 2 Temperature sensors
• 1 wind speed sensor
• 1 wind direction sensor
122 channels

Kaya, Turek, and Ventura, IMAC XXXI, California, 2013.
ChN:74 Vertical
1st Vertical Mode
at 0.8Hz
1st Torsional Mode
at 1.16Hz

Dr. Yavuz Kaya, Dr. Martin Turek, and Prof. Carlos Ventura, IMAC XXXI, California, 2013.
ChN:75 Vertical
The modal
frequency varies
between 0.75Hz
and 0.82 Hz:
decreasing in
daytime and
increasing at night.
The fluctuation in
modal frequency
coincides with the
traffic load (RMS
values)

Dr. Yavuz Kaya, Dr. Martin Turek, and Prof. Carlos Ventura, IMAC XXXI, California, 2013.
ChN:75 Vertical
The temperature
varies between 11
degrees and -1
degrees Celsius
The maximum and
minimum values are
not consistent from
a day to day basis
Temperature effect
is not directly
affecting the change
in modal frequency.
November

Uses of Real-time monitoring

Commercial Application
62
(After D. Sokolnik)

After M. Ciudad Real
BUSINESS CONTINUITY SOLUTION OVERVIEW
Data &
Information
Real-Time
Processing
Alerting &
Reporting
Decision
Making
Communication
& Interaction
Commercial application of Real-Time Monitoring

5
3
1 2
4
4: Sky Tower
1: The Landmark
5: ADNEC
Capital Gate
6: Al Dar HQs
Selected Unique Buildings for
Abu Dhabi Municipality
2: Trust Tower
6
Courtesy: M. Ciudad-Real

Carlos E. Ventura
2 studies:
(a) 5 month continuous under construction monitoring
(b) Records every 2 or 3 stories during 5 construction stages
Monitoring Under Construction
Titanium Building Chile

Frequency Variations. Daily variation have been filter out
Weekly pattern
(construction of 1 story)
variations range
[1.5 - 5.5]%
With respect to the
frequency at the beginning
of each week

67
Complete Building
Experimental
Model.
f1 = 0.187 [Hz]
Finite Element
Model.
f2 = 0.256 [Hz] f3 = 0.297 [Hz] f4 = 0.713 [Hz] f5 = 0.864 [Hz]
f1 = 0.187 [Hz] f2 = 0.250 [Hz] f3 = 0.288 [Hz] f4 = 0.700 [Hz] f5 = 0.811 [Hz]
Source:
MACEC
3.0
Source:
ETABS

Using Drift as part of Damage Detection System
For buildings, drift ratios can be used as the main
parametric indicator of a damage condition at the
building.
For bridges, the term, “drift ratio” is not generally
used; however, relative displacements of critical
elements of a bridge can be construed as such.
After Celebi

Why Drift Ratio? Connection to Performance
We can assess performance using measured/computed
actual or average story drift ratios.
Drift ratios can be related to the performance-based force-
deformation and can be computed from relative displacements
between consecutive floors
The “damage state” of the building can be estimated from the
measured displacements
After Celebi

• Measuring displacements directly is very
difficult for real-life structures [except for
tests conducted in a laboratory (e.g., using
displacement transducers)].
• For structures with long-period responses
(e.g. tall buildings), displacement
measurements using GPS are obtained
directly at the roof only;
• Drift ratio is an average drift ratio for the
whole building.
After Celebi
Measuring Displacements is NOT Easy

Displacement via Real-time Double Integration
Çelebi et al, 2004

Deterministic or Probabilistic?
Interstory Drift Ratio
Level of Damage
Light damage
Moderate damage
No damage
Severe damage
0.005 0.009 0.015
From Miranda 2005.

Other Damage Indicators that can be used
• Categories of measures:
1. “Simple” or “Design Oriented” Measures.
2. Fragility Function Measures Based on PEER/NSF Damage Indicators.
3. Fuzzy Theory Based Multi-Criterion Damage Measures
4. Application of Wavelets Theories
5. Use of Genetic Algorithms for Structural Identification
6. Application of FEMA-356 Performance Indicators
7. Application of HAZUS-MH and Porter & Kiremidjian Fragility Functions
 Structural Systems
 Nonstructural Systems and Components
 The basic idea is that the more information we have about the building and its
components the more accurate our damage assessment must become.
After Naeim

74
Fragility Functions
Structural Response
Parameters
(Engrg. Demand Parameters)
Structural and
Nonstructural Damage
Use of Fragility Functions for Performance
Based Engineering and Damage Assessment
After Naeim

75
LA52 building

76
LA 52 Story – 1991 Sierra Madre

77
LA 52 Story – 1992 Landers

78
LA 52 Story – 1994 Northridge

79
How do we take advantage of all the work in
SHM that has been conducted during the last
two decades?
Or
Avoid “re-inventing the wheel” in SHM?

Challenges in the development of SHM and Damage
Detection
1) System reliability
2) Inappropriate instrumentation and sensor overload
3) Data storage and data overload
4) Communications
5) Environmental factors and noise
6) Data mining and information presentation
7) Funding and vested interests
8) Lack of collaboration

Damage Detection Issues
• There are many algorithms that have been developed; to date
none have apparently been ready to apply directly to civil
engineering structures: too ambitious?
• Some are starting to be used in a more conservative form:
combining them appears to be effective; more sensors and better
processing of data = better results
• Cheaper sensors and wireless: easier deployment of monitoring
technology
• Cooperation of organizations including Universities, industry and
government results in better development of SHM technology
• The final system must be a synthesis of technologies –
hardware, software and engineering knowledge

Fundamental Axioms of SHM
(Worden, Farrar, Manson & Park, 2007)
Axiom I: All materials have inherent ﬂaws or defects;
Axiom II: The assessment of damage requires a comparison between two system states;
Axiom III: Identifying the existence and location of damage can be done in an unsupervised
learning mode, but identifying the type of damage present and the damage severity can generally
only be done in a supervised learning mode;
Axiom IV:
a) Sensors cannot measure damage. Feature extraction through signal processing and
statistical classiﬁcation is necessary to convert sensor data into damage information;
b) Without intelligent feature extraction, the more sensitive a measurement is to damage, the
more sensitive it is to changing operational and environmental conditions;
Axiom V: The length- and time-scales associated with damage initiation and evolution dictate the
required properties of the SHM sensing system;
Axiom VI: There is a trade-off between the sensitivity to damage of an algorithm and its noise
rejection capability;
Axiom VII: The size of damage that can be detected from changes in system dynamics is
inversely proportional to the frequency range of excitation.
82

Research needs:
a) To accurately determine the reliability of a structure based on
diagnostic results, it is important to determine the probability of
underestimation of the severe damage, in which causes structural
failure.
b) Methods for determining the probability distribution of occurrence of
true damage should be further developed with the aim to answer
these key questions that can help stakeholders decide if a structure
is safe to occupy or use:
1) Is there visible or hidden damage?
2) If damage occurred, what is its extent?
3) Does the damage threaten other neighboring structures?
4) Can the structure be occupied immediately without
compromising life safety or is life safety questionable?

84
Damage Detection Process
‘The four levels’
1) Is the system damaged? – Identify
2) Where is the damage located? – Locate
3) What type of damage is present? – Quantify
4) What is the extent of damage? – Prognosis, Life Span
84

Thank you!

Seismic Structural Health Monitoring

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