FuzzyNeuro System for Bridge Health Monitoring

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FuzzyNeuro System for Bridge Health Monitoring

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Title: FuzzyNeuro System for Bridge Health Monitoring

1
Fuzzy-Neuro System for Bridge Health Monitoring

Lakshmanan Meyyappan (Laks)

2
Birds Eye View

Objective
Overall System
Experimental Setup
Fuzzy Logic Decision System (Clustering)
Neural Network Prediction System (BP)
Research Findings
Future Work

3
Objectives

The main goal is to develop a practical
real-time structural health monitoring system
using smart systems engineering concepts and
tools.

4
2. Overall System
5
3. Experimental Setup

Data Type
Data Acquisition Method
Experiment
Data Analysis

6
3.1 Data Type

Acoustic or ultrasonic
X-radiography
Eddy-currents
Microwaves
Magnetic fields
Thermal fields
Vibration Signatures
Many more

7
3.1.1 Real Time Analysis Requirements

It must be a non-destructive evaluation (NDE)
technique
It should be able to monitor the structure on a
global basis
It must not cause any disruption to the normal
operation of the structure
The equipment used for collecting data should not
be bulky, must be easy to replace and must be
reliable
Initial and operating costs should not be very
high
It must be sensitive and must be able to detect
small damages

8
3.1.1 Vibration Signatures

The basic premise of vibration based structural
health monitoring is that damage in the structure
or change in its physical properties (i.e.,
stiffness, mass and/or damping) will, in turn,
alter the dynamic response of the system

9
3.2 Data Acquisition Methods

The Structure has to be excited by some means to
collect vibration data
Measured Input Excitation Methods
Step Relaxations
Shakers
Ambient Excitation Methods
Test Vehicle Method

10
3.2.1 Test Vehicle Method

Advantages
Realistic
Easy to collect (not as tedious as the measured
input excitation methods)
Does not cause damage to the structure
Normal operation of structure is possible
Disadvantages
The collected data depends on the type of test
vehicle, traffic on the bridge, speed of the
vehicle

11
3.3 Experiment

Teardrop
Bridge

12
3.4 Data Analysis

Pre-Processing
Feature Selection
Signal processing
Data Cleansing

13
3.4.1 Pre-Processing
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3.4.2 Feature Selection

Feature selection involves condensing the data
and finding out a distinguished feature that can
be used as an input to a tool, which would
analyze the data and detect damage.
Peak
Mean
Median
Mode

15
3.4.3 Signal Processing

Processing the raw data to get some useful
information (Also reduces the dimension of the
data)
Fourier Transform
Sampling and Quantization
Power Spectrum

16
3.4.3 Signal Processing
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3.4.4 Data Cleansing

Data cleansing is the process of selectively
choosing data to accept for, or reject from the
feature selection process.
Power Spectrum Peak Values

18
Why Fuzzy Logic Neural Networks

Fuzzy logic and neural networks are used to make
the predictions because of their inherent
robustness and their abilities to handle
nonlinearities and uncertainties in structural
behavior
Conventional decision making systems and control
systems rely on the accuracy of the modeling of
the system dynamics. A fuzzy system does not
require accurate dynamic modeling

19
4. Fuzzy Logic Decision System

Damage Detection
Goal
Fuzzy Clustering
Fuzzy Output

20
4.1 Damage Detection

Interesting Facts on Extensive Analysis
Vibration Value ( Power Spectrum Peaks) vary with
speed and mass of the vehicle, and few other
parameters like the temperature.
All these relations are non linear
Even though vibration varies with a number of
parameters, the pattern, that is, its relation
with a different member remains the same

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4.1 Damage Detection

For simplicity of explanation the data collected
with the sensors attached to the above five
locations are used.

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4.1 Damage Detection
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4.1 Damage Detection

Relationship between the members remains
the same that is member 3 has the highest power
spectrum value in all of the above cases followed
by member 1, 5, 4 and 2 respectively

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4.1 Damage Detection

At all different conditions the relationship
between the members remains the same that is
member 3 has the highest power spectrum value in
all of the above cases followed by member 1, 5, 4
and 2 respectively
This is true for any number of members
Advantage
Independent of all other conditions like speed,
mass, type of the vehicle.

25
4.2 Fuzzy Logic Decision System

Goal
To take power spectrum values of various members
as input and predict a possible damage
Method
Fuzzy Clustering (Fuzzy C-Means)

26
4.3 Fuzzy Clustering

Data Clustering
Fuzzy Clustering
Fuzzy C-Means (FCM)

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4.3 Fuzzy Clustering

Input Data Power spectrum values obtained from
5 specific members Clusters 3 Clusters are
formed Output 5 members clustered in 3 groups
28
4.3 Fuzzy Clustering