In this lecture, we will talk about the use
of artificial neural network in structural
health monitoring processes.
In the last lecture we discussed about the
use of artificial intelligence in general
compared to the computation intelligence,
and how the interference helps us to improve
upon the intelligent sensing, and decision
making processes which are very vital and
important in structural health monitoring
scheme or a network.
So, in this lecture, we will extend that artificial
intelligence source of information to a neural
network.
And see how this can be useful in simple applications
as far as SHM is concerned when we talk about
use of artificial intelligence in structural
health monitoring.
There are about four axioms which are very
useful and directly applicable 
axiom one.
In that case we should name this as per the
order explained in the literature.
So, we should say axiom three, which says
identifying the existence and location of
damage can be done in unsupervised learning
mode, identifying the damage or to be very
precise identifying the type of damage, present
in a system severity of the damage.
Only by supervised learning mode, axiom of
four a it says that 
sensors cannot measure damage.
They only record the data they do not know
really, whether the data recorded the tentative
damage or not sensors cannot measure the damage.
It is only through the feature extraction
done through signal processing and statistical
analysis, classifies the damage from the sensor
data it is very important axiom.
The next axiom is an subset of this so I call
this as axiom four b, without intelligent
feature extraction, changing operational conditions
and environmental data, makes the measure
data of damage more sensitive.
It means any change in data related to operational
condition or environmental conditions may
always give a false implication that it is
relevant to a damage, you have to confirm
that it is a damage only through intelligent
feature extraction.
So, without feature intelligent feature extraction
the sensitivity of these two parameters will
become very dominant.
Axiom five the length and time scale associated,
with damage initiation 
and evolution, decide the 
properties and characteristics of the health
monitoring system they govern ok.
How long you want to and how and what interval
you want to measure the damage parameters.
To really identify the damage initiation and
evolution it decides, what is your characteristic
of a health monitoring system ok?
That is very important the intelligence in
structural health monitoring can be useful
in composite structures.
So, the main aim or the hidden agenda here
is using robust type signal processing protocol.
Let us take an example where the composites
are subjected to damage very often, one classical
example is glass fiber reinforced plastic.
What is GFRP laminates or generally or let
us say widely used as structural materials,
because they have high strength to weight
ratio 
and good corrosion resistance, they are also
useful in military applications, because they
minimize electromagnetic radar signature or
underwater vehicles of GFRP.
They fail mainly due to cracking or delamination,
delamination is more severe because it causes
stiffness reduction 
and leads to catastrophic failure of the structure.
So, therefore, friends, it is vital to detect
delamination in GFRP.
The more vital part is a few delamination’s
maybe, but still, they can cause severe damage
to the mechanical properties and load carrying
capacity 
of the structure.
There are various techniques which are applied
to check this delamination, one is X-ray,
two is ultrasonic C scan, third could be laser
shearography.
Now, with these methods, there are some difficulties
the difficulties are it takes much time to
inspect the laminate or the GFRP structure,
by these techniques.
Therefore, what is desire the desired option
could be online detection of damage, what
is online detection how it is done artificial
neural networks?
pre-processing tools such as damaged, relativity,
analysis, technique, which is damage diagnosis.
Now, the advantages this can predict the location,
size, presence, and extent of the damage precisely.
Now let us see more detail about this artificial
neural networks, they are actually large,
parallel, distributed, processes 
comprising of simple processing units.
These units are called neurons which has multiple
interconnection paths, mapping the relationship
between measurable features of structural
damage 
to the physical parameters.
For example, what kind of damage would cause
what change in the physical parameter of property.
This can be identified and established using
an artificial neural network.
Classification and identification of structural
damage can be successfully done using artificial
neural network.
How does it do it, it uses a set of known
damage features and their corresponding physical
parameters.
It also employs multi-layer feed forward back
propagation network to perform, data segmentation,
data compression and above all most importantly
the pattern recognition if present by identifying
the repetition of data.
Let us see how ANN can be useful in structural
health monitoring, in general, are largely
seen 
in bridge structures.
For example, let us take a railway bridge
whose health monitoring is required to be
done ok.
So, the steps could be very simple data should
be collected 
from the dynamic response of the bridge, through
simulation 
under passage of train.
When this is done, it is assumed that bridge
is healthy bridge is in undamaged state and
it is therefore considered to be healthy.
Which are different damage scenarios in the
first stage, I can use artificial neural network
which are essentially trained with an unsupervised
learning, approach the input comprises of
accelerations of the deck 
under healthy state?
Now based on the acceleration values 
at the previous instant of time, the neural
network predicts; the future acceleration
in the second stage of damage prediction in
the second stage of damage scenario.
The prediction errors are statistically characterized
by a Gaussian process which supports, the
choice of damage decision the choice of damage
detection decision from a known threshold
value.
By comparing the damage indices, with the
threshold value, one can differentiate the
health conditions of the bridge whether it
is damaged or healthy ok.
One can do decide this for each damage case
scenario as seen above, operating characteristic
values, in form of curves are obtained 
than using Bayes theorem one can also estimate
the total cost of the proposed methodology.
