ann seminar

8/8/2019 ANN Seminar

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ARTIFICIAL NEURAL NETWORKS FORSTRUCTURAL DAMAGE DETECTION USING

MODAL DATA

Presented By :

KIRAN H

VII Sem. C.E.


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Seminar Contents

1. Introduction

2. Modal analysis

3. Damage detection process

3. Design of ANN.

4. Damage detection in cantilever plate

5. Training data for ANN

6. Test/Validation data

7. Damage detection scenarios

24. Applications of ANN

25. Conclusion

Slide no.


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Biological Neurons

Neuron : computational unit in the nervous system. It has:

(i) Dendrites (inputs) Neuron receives input from other neurons

(ii) Cell body An electrical pulse that travels from the body, down the

axon (iii) Axon (output) Touch the dendrites or cell body of the next

neuron.


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Artificial Neurons

Multiple inputs and a single output.

The neurons can be trained to fire (or not), for particularinput patterns.

Inputs associated with weights. These weights correspond to synaptic efficacy in a

biological neuron.

Each neuron also has a single threshold value.

The weighted sum of the inputs is formed, and the thresholdsubtracted, to compose the activation function.

Note also that weights can be negative, which implies thatthe synapse has an inhibitory rather than excitatory effecton the neuron


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Biological andArtificial Neurons

Human neuron A simple neuron with weight


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Architectureofneuralnetworks

Feed-forward networks Feedback networks


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Structural damage detection

Assess the condition of structures.

Gives improved understanding about structures capacity

and typical performance during its service.

Can be found out by monitoring change in structural

responses, natural frequencies and mode shapes and strain

mode shapes.

These direct methods cant locate and quantify damage.

Modal analysis can give the location and magnitude ofdamage.


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Modal analysis

The modal vibration test data is used.

The natural frequencies and mode shapes can be considered

to represent the state of the structure.

Modal parameters depend only on the mechanical

characteristics of the structure and not on the excitation

applied.

Theoretically the structure can be represented by

measurements taken at a single location. Reduction in time and cost of performing damage

monitoring and predictive maintenance.


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Damage Detection Process

Every structure has its own natural vibration (or resonance)

frequencies.

Each vibration mode has a different energy distribution.

Any localized damage will affect each mode differently

depending on the location and severity of the damage.

Modal parameters are sensitive to boundary conditions, i.e.,

physical constraints of the structure.

Damage will soften the structure and thus modify itsdynamic characteristics such as frequency and mode shape.


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Damage Detection Process


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Artificial Neural Network

The feed-forward, multilayered, supervised neural network

with the error back propagation algorithm is used.

Two stages.1st is data feed forward.

The output of each node is defined as

netj =ij Oi + j

Oj = f( netj )

Threshold function is

{1 x>1

f(x) = {x -1x1

{-1 x


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Artificial Neural Network

The 2nd stage is error back propagation and weights

adjustment.

The system error is defined as E

E= (1/2P)* p=1P

n=1N (dpn opn)

P - number of instances in the training set,

dpn ,opn - desired and calculated output of the nth output node

for the pth instance, respectively


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Damage Detection in Cantilever plate

Cantilever steel plate properties

Cross-section of 50.75 x 6.0 mm2 and mass density of 7670

kg/m3.

The first 6 natural frequencies for the intact and damaged

configurations for this cantilever plate are taken from

literature.

The damage configuration was introduced by saw cuts at

the clamped end (Cut-1) and the mid-span (Cut-2) of theplate.

The depth (d) of the cut is 20 mm.


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Damage Detection in Cantilever plate

Damage made by cuts at locations 1 and 5 for the cantilever

plate

Division into 9 components for simulation of damage in

FEA


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Measured and computed (FEA) frequencies.

Frequencies, Hz

Case Mode 1 Mode 2 Mode 3 Mode 4 Mode 5 Mode 6

Measured(undamaged)

5.67 35.58 100.33 196.38 319.50 485.71

Measured

(damaged)

5.11 31.60 94.44 179.97 308.89 452.71

FEA(undamaged)

5.68 35.61 99.70 195.36 322.90 482.27


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The Neural Network Architecture

A feed forward back-propagation neural network has been

used.

In input stage, 6 processing elements in the input layer

representing a vector of the first 6 natural frequencies of thebeam.

In output stage,10 PEs for the output layer, where the first

node consists of damage magnitude and the remaining 9

nodes represent the damage indicators at each of 9 locations. The damage indicators '0' and '1' represent the undamaged

and fully damaged conditions, respectively.


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The Neural Network Architecture

In the hidden layer design ,18 PEs in single hidden layer

network was chosen as it results in less RMS error.


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Training Data

To obtain training data finite element (FE) model has been

generated for the cantilever plate with 36 finite elements.

The damage was defined as the fractional loss of the second

moment of area over one location.

The data was generated for a wide range of damage

magnitudes of 1%, 5%, 10%, 15%, 20%, 25% and 30%.

Hence, the database consists of training sets corresponding

to 7 damage states in each of 9 locations of the cantileverbeam. This results in 63 training sets.


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Training Data For multiple damage scenario, namely, simultaneous

damage in two locations, training data has been generated

for 1%, 10% and 20% damage magnitudes in each of two

damage locations simultaneously which resulted in 144training sets.


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Test /Validation Data For validating the network, a test data set has been

generated for3 damage magnitudes of3%, 13% and 23%.

This test database consist of data pertaining to 3 damage

levels for each of 9 damage locations which resulted in 27test sets for single damage scenario.

For validating multiple damage state, test data set

corresponding to the above three damage magnitudes in

three selected pairs of damage locations, namely, at (1,3),(1,5) and (1,7), have been generated which resulted in 9 test

sets.


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Neural Network Training

The learning rate parameter () for the network is chosen as

0.01 and the error tolerance is chosen as 0.01 after trials.

Fractional change in resonant frequencies used to get

accurate results.

zi = (fui fdi ) / fui

zi is the fractional change in the i-th mode,

fu and fd are the frequencies of undamaged and damaged

structure, respectively.


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Damage detection using ANN


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Multiple damage detection


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Applications for Neural Networks

Detection of medical phenomena

Stock market prediction

Credit assignment

Monitoring the condition of machinery. Engine management

In marketing

Facial recognition

Robotics


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Conclusion

The detection and identification of structural damage is a

vital part of the monitoring and servicing of structural

systems during their lifetime.

It was observed that prediction capability of ANN is betterby using fractional difference of frequencies instead of

using the frequencies.

The results show that the ANN method is capable of

predicting the location and magnitude of damage with goodaccuracy for single and multiple damage scenarios.


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