The document is a review of recent advances in using neural network techniques for damage identification of bridges. It begins with an introduction discussing traditional structural identification methods versus soft computing methods such as neural networks, genetic algorithms, and fuzzy logic. It then provides numerous examples of recent studies from 2008-2013 that have used soft computing methods like neural networks, genetic algorithms and fuzzy neural networks for structural assessment of bridges. The document discusses nonlinear feedforward neural network models and traditional learning methods. It also discusses a probabilistic interpretation of neural networks that allows for Bayesian inference and a hierarchical multi-level Bayesian approach for neural network modeling.

1. Neural networks based techniques for
damage identification of bridges:
a review of recent advances
Sapienza University of Rome – StroNGER s.r.l.
S. Arangio
stefania.arangio@uniroma1.it, stefania.arangio@stronger2012.com
Cagliari, September 5th 2013

2. 2/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Introduction
Part I
Conclusions
Part II
Neural networks and Bayesian enhancements
Outline
Case study:
Bayesian neural networks
for the assessment of the bridge of the ANCRiSST benchmark problem
Soft computing approaches for the structural assessment of bridges

3. 3/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Introduction
Part I
Conclusions
Part II
Neural networks and Bayesian enhancements
Outline
Case study:
Bayesian neural networks
for the assessment of the bridge of the ANCRiSST benchmark problem
Soft computing approaches for the structural assessment of bridges

4. 4/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Methods for structural identification and damage detection
Input – output
techniques
• The structure has to be artificially excited and
in case of large structures it is not always
possible
• The operation of the structure has to be
interrupted
Only output
techniques
• The excitation is given by the ambient
vibration
• Measurements in real operational conditions
• Suitable in case of continuous monitoring
Traditional
methods
Soft computing
methods
• Time domain
approaches
• Frequency
domain
approaches
• Neural
networks
• Genetic
algorithms
• Fuzzy Logic
Introduction

5. 5/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Examples of structural assessment
by using soft computing methods (2008-2013)
Adeli H., Jiang X., Intelligent infrastructures – Neural Networks, wavelets, and Chaos Theory for Intelligent Transportation Systems and
Smart Structures, CRC Press, Taylor & Francis, Boca Raton, Florida, 2009
Al-Rahmani A.H., Rasheed H.A., Najjar A.Y., A combined soft computing-mechanics approach to inversely predict damage in bridges,
Procedia Computer Science, 8, 461 – 466, 2012
Arangio S., Beck J.L. Bayesian neural networks for bridges integrity assessment, Structural Control & Health Monitoring, 2012; 19(1), 3-21.
Arangio S., Bontempi F. Soft Computing based Multilevel Strategy for Bridge Integrity Monitoring, Computer-Aided Civil and Infrastructure
Engineering 2010; 25, 348-362.
Bhattacharyya P., Banerji P., Improved Damage Classification and Detection on a Model Bridge using Fuzzy Neural Networks, 4th
International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-4), 22-24 July 2009, Zurich, Switzerland,
2009.
Cheng J., An artificial neural network based genetic algorithm for estimating the reliability of long span suspension bridges, Finite Elements
in Analysis and Design, 46, 658–667, 2010.
Cheng J., Li Q.S., Reliability analysis of structures using artificial neural network based genetic algorithms, Comput. Methods Appl. Mech.
Engrg., 197, 3742–3750, 2008.
Firouzi A., Rahai A., An integrated ANN-GA for reliability based inspection of concrete bridge decks considering extent of corrosion-induced
cracks and life cycle costs, Scientia Iranica, 19 (4), 974–981, 2012.
Flood I., Towards the next generation of artificial neural networks for civil engineering, Advanced Engineering Informatics 22, 4–14, 2008
Freitag S., Graf W., Kaliske M. Recurrent neural networks for fuzzy data, Integrated Computer-Aided Engineering - Data Mining in
Engineering, 2011; 18(3), 265-280.
Graf W.S., Freitag S., Sickert U., Kaliske M., Structural Analysis with Fuzzy Data and Neural Network Based Material Description,
Computer-Aided Civil and Infrastructure Engineering 27, 640–654, 2012.
Li S., Li H., Liu Y., Lan C., Zhou W., Ou J., SMC structural health monitoring benchmark problem using monitored data from an actual cable-
stayed bridge, Structural Control and Health Monitoring, published online form March 26th 2013, DOI:10.1002/stc.1559
Mehrjoo M., Khaji N., Moharrami H., Bahreininejad A., Damage detection of truss bridge joints using Artificial Neural Networks, Expert
Systems with Applications 35, 1122–1131, 2008.
Park J.H., Kim J.T, Honga D.S., Hoa D.D., Yib J.H., Sequential damage detection approaches for beams using time-modal features and
artificial neural networks, Journal of Sound and Vibration, 323, 451–474, 2009.
Sgambi L., Gkoumas K., Bontempi F. Genetic Algorithms for the Dependability Assurance in the Design of a Long-Span Suspension Bridge,
Computer-Aided Civil and Infrastructure Engineering 2012; 27(9), 655-675.
Tsompanakis Y., Lagaros N.D., Stavroulakis G. Soft computing techniques in parameter identification and probabilistic seismic analysis of
structures, Advances in Engineering Software 2008, 39(7), 612-624.
Wang Y.M., Elhag T.M.S., An adaptive neuro-fuzzy inference system for bridge risk assessment, Expert Systems with Applications 34,
3099–3106, 2008.
Zhou H.F., Ni Y.Q., Ko J.M., Constructing input to neural networks for modeling temperature-caused modal variability: Mean temperatures,
effective temperatures
Introduction

6. 6/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Introduction
Part I
Conclusions
Part II
Neural networks and Bayesian enhancements
Outline
Case study:
Bayesian neural networks
for the assessment of the bridge of the ANCRiSST benchmark problem
Soft computing approaches for the structural assessment of bridges

7. 7/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII
Nonlinear feed-forward basis functions
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8. 8/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Traditional learning
t
ij
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Weights updating
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9. 9/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Probabilistic interpretation
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2 = 1/α
hyperparameter

10. 10/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Network learning as inference
10
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OUTPUT
NETWORK ARCHITECTURE
n°INPUT
n°UNIT IN THE HIDDEN LAYERS
POSTERIOR: BAYES’ THEOREM
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• NOISE MODEL
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OPTIMIZATION
(MINIMUM OF )),,,(log MβαDwp−
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1) Model fitting

12. 12/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Bayesian techniques for neural networks
• Level 1 Model fitting: inferring the model parameters given the
model and the data
• Level 2 Optimization of the hyperparameters α and β
• Level 3 Model class selection: optimal model complexity
• Level 4 Automatic relevance determination (ARD):
evaluation of the relative importance of different inputs
Network learning as inference (model fitting) is only one level in
which Bayesian inference can be applied in the neural network
field
Hierarchical multi-level approach
PartI

13. POSTERIOR FOR α, β
TRAINING: OPTIMIZATION
w = wMAP?
?( ) ( )DMEVDMEV ii 1−>
INFERENCE OF NEW DATA
CHOOSE MODEL Mi-1
?
POSTERIOR FOR Mi
α, β = αMP, βMP
DATA PRE- PROCESSING
OUTPUT
NETWORK MODEL Mi
N HIDDEN = i
N INPUT = k
POSTERIOR FOR w
yes
DATA POST PROCESSING
PROBABILISTIC MODEL
no
INPUT
CHOOSE INITIAL α, β
INITIALIZE WEIGHTS w
RE-ESTIMATION OF α, β
yes
no
Wγ ≈
yes
no
i= i+1
is α1,…,αk
‘very large’?
k= k-1
yes
no
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MwpMwDp
Dwp
,,
,,,
),,,(
βα
αβ
βα =M
1st level
Model fitting

14. POSTERIOR FOR α, β
TRAINING: OPTIMIZATION
w = wMAP?
?( ) ( )DMEVDMEV ii 1−>
INFERENCE OF NEW DATA
CHOOSE MODEL Mi-1
?
POSTERIOR FOR Mi
α, β = αMP, βMP
DATA PRE- PROCESSING
OUTPUT
NETWORK MODEL Mi
N HIDDEN = i
N INPUT = k
POSTERIOR FOR w
yes
DATA POST PROCESSING
PROBABILISTIC MODEL
no
INPUT
CHOOSE INITIAL α, β
INITIALIZE WEIGHTS w
RE-ESTIMATION OF α, β
yes
no
Wγ ≈
yes
no
i= i+1
is α1,…,αk
‘very large’?
k= k-1
yes
no
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,,,
),,,(
βα
αβ
βα =M
1st level
Model fitting
2nd level
Evaluating the hyperparameters α, β
( ) ( )
( )MDp
MpMDp
Dp
βαβα
βα
,,,
),,( =M

15. 15/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Issues in neural network design: selection of the optimal model
RULES OF THUMBS
-…between the input layer size and the output
layer size (Blum, 1992)
- (Software Neuroshell, 2000)
- (Berry and Lynoff, 1997)
- n = dimension needed to capture 70-80% of the
variance
(Boger and Guterman, 1997)
OPTIMAL NUMBER OF UNITS
(“OCKHAM’S RAZOR”)
)(
3
2
oI NNn +=
INn ⋅<2
examplesNn ⋅<
30
1
They aren’t rigorous methods
INPUT
LAYER
OUTPUT
LAYER
HIDDEN
LAYERS
PartI

16. POSTERIOR FOR α, β
TRAINING: OPTIMIZATION
w = wMAP?
?( ) ( )DMEVDMEV ii 1−>
INFERENCE OF NEW DATA
CHOOSE MODEL Mi-1
?
POSTERIOR FOR Mi
α, β = αMP, βMP
DATA PRE- PROCESSING
OUTPUT
NETWORK MODEL Mi
N HIDDEN = i
N INPUT = k
POSTERIOR FOR w
yes
DATA POST PROCESSING
PROBABILISTIC MODEL
no
INPUT
CHOOSE INITIAL α, β
INITIALIZE WEIGHTS w
RE-ESTIMATION OF α, β
yes
n
o
Wγ ≈
yes
no
i= i+1
is α1,…,αk
‘very large’?
k= k-1
yes
no
( ) ( )
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MwpMwDp
Dwp
,,
,,,
),,,(
βα
αβ
βα =M
1st level
Model fitting
2nd level
Evaluating the hyperparameters α, β
3rd level
Model class selection
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prior = constantevidence
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MpMDp
Dp
βαβα
βα
,,,
),,( =M

17. POSTERIOR FOR α, β
TRAINING: OPTIMIZATION
w = wMAP?
?( ) ( )DMEVDMEV ii 1−>
INFERENCE OF NEW DATA
CHOOSE MODEL Mi-1
?
POSTERIOR FOR Mi
α, β = αMP, βMP
DATA PRE- PROCESSING
OUTPUT
NETWORK MODEL Mi
N HIDDEN = i
N INPUT = k
POSTERIOR FOR w
yes
DATA POST PROCESSING
PROBABILISTIC MODEL
no
INPUT
CHOOSE INITIAL α, β
INITIALIZE WEIGHTS w
RE-ESTIMATION OF α, β
yes
n
o
Wγ ≈
yes
no
i= i+1
is α1,…,αk
‘very large’?
k= k-1
yes
no
( ) ( )
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MwpMwDp
Dwp
,,
,,,
),,,(
βα
αβ
βα =M
1st level
Model fitting
2nd level
Evaluating the hyperparameters α, β
3rd level
Model class selection
( ) ( )MpMDpDMp ∝)(
prior = constantevidence
is α1,…,αk
‘very large’?
4th level
Automatic Relevance Determination
( ) ( )
( )MDp
MpMDp
Dp
βαβα
βα
,,,
),,( =M

18. 18/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Introduction
Part I
Conclusions
Part II
Neural networks and Bayesian enhancements
Outline
Case study:
Bayesian neural networks
for the assessment of the bridge of the ANCRiSST benchmark problem
Soft computing approaches for the structural assessment of bridges

19. 19/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
The ANCRiSST benchmark problem
• Consortium of 20 research institutions
• Established in 2002 with the purpose of:
• assessing current progresses on smart materials and structures technology
• Developing synergies that facilitate joint research projects that cannot easily carried
out by individual centers
In October 2011 they opened for
researchers in the SHM community a
benchmark problem based on a real
bridge: the TianjinYonghe bridge
http://smc.hit.edu.cn/
PartII

20. 20/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Description of the Tianjin Yonghe bridge
Tianjin Hangu
25.15 99.85 260 99.85 25.15
• Cable-stayed bridge
• Opened to traffic since December 1987
• After 19 years of operation damages were detected and the bridge was
retrofitted
• A sophisticated SHM system has been designed and implemented by the
Research Center of Structural Health Monitoring and Control of the Harbin
Institute of Technology
PartII

21. 21/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Structural Health Monitoring System
Tianjin Hangu
2515 5600 5885 5900 5600 5600 5900 5885 5600 2515
1 (3) 2 (4) 3 (5) 7 (9) 9 (10) 11 (12) 13 (14)
Uniaxial/biaxial accelerometers
Hygrothermograph
Anemometer
1, 3, 5, 7, 9 11, 13 2, 4, 6, 8, 10, 12, 14
During 2008:
• Continuous monitoring system
• 14 uniaxial accelerometers on the bridge deck (downward and upward)
• On the top of the tower: 1 biaxial accelerometer; 1 anemometer; 1 temperature
sensor
downward and upward
PartII

22. 22/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
damaged area
Damage situation 1
Cracks at the closure segment
at both side spans
August 2008:
2 damages are detected
PartII

23. 23/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Damage situation 2
Damaged piers
PartII

24. 24/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Available data set
Health condition Damaged condition
• Time histories of the accelerations
recorded at the 14 deck sensors
on January 1st and January 17th 2008
(registrations of 1 h carried out for 24 h )
• Environmental information
(wind, temperature)
• Biaxial accelerations at the top of the
tower
• Time histories of the accelerations
recorded at the same 14 deck sensors
on July 30th 2008
(registrations of 1 h carried out for 24 h)
• Accelerations collected by field testing
August 7th to 10th 2008 (not used)
PartII

25. 25/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Procedure for neural network training
time history of the
acceleration recorded at
sensor #
Structural system
Ambient excitation
1+−dtf 2−tf tf1−tf 1+tfTraining of the neural
network model in
undamaged condition
2+tf
Test of the trained neural
network model on a new time
history

26. 26/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Neural network based damage detection strategy
14 groups of networks have been created
(one for each measurement point e one for each hour of measurements)
14 (points) x24 (hours) = 336 neural network models
Tianjin Hangu
1 (3) 2 (4) 3 (5) 7 (9) 9 (10) 11 (12) 13 (14)
accelerometers

27. 27/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Detection of anomalies
If ∆e ≈ 0
the structure is considered as undamaged
If ∆e is large an anomaly is detected

28. 28/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII
Damaged area
Error in the approximation of the accelerations in the undamaged sections
Training Undamaged
Damage detection
Tianjin Hangu

29. 29/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Bayesian model class selection
The most plausible class can be obtained applying Bayes’ Theorem:
( ) ( )( , ) |j jj
p M D p D M p M∝M M
prior = cost
evidence
provided by D
The various model can be compared by evaluating their evidence






−
+
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
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
+−−
γγ
α
N
E MP
W
2
ln
2
12
ln
2
1
ln
2
1
A
++++− jjMP
MP
D HH
N
E ln2!lnln
2
ββ( )=iMDpln
Data fit term
Penalizing term
“Ockham factor”

30. 30/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Bayesian model class selection
The chosen model has 3 hidden units
Model 1 2 3 4 5
N parameters 7 13 19 25 31
gamma 2,00 3,03 4,02 5,00 6,00
MP
j
MP
D
MP
j
β
N
Eβ ln
2
+− 20770 22682 25078 22153 23500
( ) MP
j
MP
j
HH ln2!ln + 2,08 3,99 5,95 8,01 10,16
data fit term 20772 22686 25084 22161 23510
MP
j
MP
W
MP
j
α
W
Eα ln
2
ln
2
1
++− A -13,08 -79,32 -158 -213 -266






−
+





γNγ
2
ln
2
12
ln
2
1
-3,31 -3,51 -3,66 -3,8 -3,86
penalizing term -16 -83 -162 -217 -270
log evidence 20756 22603 24922 21944 23240

31. 31/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII
BAYESIANMODELSELECTION

32. 32/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Error in the approximation of the undamaged conditions
downriver
upriver
∆e at the various locations
Data for training: January 1st 2008 (H1 to H24)
Data for testing: January 17th 2008 (H1 to H24)
Undamaged conditions

33. 33/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Error in the approximation of the damaged conditions
∆e at the various locations
Data for training: January 1st 2008 (H1 to H24)
Data for testing: July 30th 2008 (H1 to H24
Damaged conditions!

34. 34/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
PartII Difference of the errors
The difference of error in the approximation suggests the presence of structural
anomalies around sensor #10

35. 35/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Validation of the results: Structural assessment by applying
the Enhanced Frequency Domain Decomposition
• Data collection and signal preprocessing
• Construction of the the Power Spectral
Density matrix (PSD)
• Whelch averaged modified periodgram method
• 50 % overlapping and periodic Hamming windowing
• Singular Value Decomposition (SVD) of the PSD
• Identification of modal frequencies and mode shapes
• Evaluation of the damping
PartII

36. 36/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
0
0,2
0,4
0,6
0 0,5 1 1,5 2 2,5 3
H6
H11
H15
H17
H19
H21
SingularValues(health)
f [Hz]
0
0,1
0,2
0,3
0 0,5 1 1,5 2
AverageSingularValues(health)
f [Hz]
EFDD: Singular Values DecompositionPartII
0
0,5
1
1,5
2
0 0,5 1 1,5 2
AverageSingularValues(damaged)
f [Hz]
0
0,5
1
1,5
2
0 0,5 1 1,5 2 2,5 3
H6
H9
H12
H15
H18
H20
H22
H23
H24
Undamaged conditions Damaged conditions
Average Singular values Average Singular values

37. 37/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Comparison of the mode shapesPartII
The decrease of the frequencies suggests the presence of damage
f=0.4075 Hz
FEM (“AS BUILT” CONDITION)
FEM Mode 1 - f=0.452 Hz FEM Mode 2 - f=0.632 Hz FEM Mode 3 - f=0.937 Hz
Mode 1 - Mode 2 - f=0.594 Hz Mode 3 - f=0.896 Hz
Mode 1 - f=0.262 Hz Mode 2 - f=0.388 Hz Mode 3 - f=0.664 Hz
UNDAMAGED CONDITION
DAMAGED CONDITION

38. 38/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Introduction
Part I
Conclusions
Part II
The ANCRIiST benchmark problem
Description of the bridge and available monitoring data
Outline
Neural network based damage detection strategy
Results

39. 39/39PartIPartIIConclusionsIntroduction
NEURAL NETWORKS BASED TECHNIQUES FOR DAMAGE IDENTIFICATION OF BRIDGES:
A REVIEW OF RECENT ADVANCES
S. Arangio
Conclusions
Soft computing approaches, like the neural networks model, have
proven to be effective for dealing with large quantities of data and,
recently, have been widely used for the structural assessment of Civil
structures and infrastructures.
Neural networks can be significantly improved by applying Bayesian
inference at different levels in a hierarchical way:
Bayesian Neural Networks (BNN)
The BNNs have been applied for processing the monitoring data
coming from the bridge of the ANCRiSST SHM benchmark problem
and have shown to be able to detect the presence of an anomaly.
The current work is focused on the development of methods for the
localization of the detected damage
Conclusions

40. POSTERIOR FOR α, β
TRAINING: OPTIMIZATION
w = wMAP?
?( ) ( )DMEVDMEV ii 1−>
INFERENCE OF NEW DATA
CHOOSE MODEL Mi-1
?
POSTERIOR FOR Mi
α, β = αMP, βMP
DATA PRE- PROCESSING
OUTPUT
NETWORK MODEL Mi
N HIDDEN = i
N INPUT = k
POSTERIOR FOR w
yes
DATA POST PROCESSING
PROBABILISTIC MODEL
no
INPUT
CHOOSE INITIAL α, β
INITIALIZE WEIGHTS w
RE-ESTIMATION OF α, β
yes
n
o
Wγ ≈
yes
no
i= i+1
is α1,…,αk
‘very large’?
k= k-1
yes
no
email: stefania.arangio@uniroma1.it
stefania.arangio@stronger2012.com
Prof. Bontempi and his research team www.francobontempi.org of
Sapienza University of Rome are gratefully acknowledged.
This research was partially supported by StroNGER s.r.l. from the
fund “FILAS - POR FESR LAZIO 2007/2013 - Support for the
research spin off”.