ANALYSIS OF GEOPOLYMER SLABS

USING

SUPPORT VECTOR MACHINE

Under the guidance of

R. Mourougane

Dept. of Civil Engineering,MSRIT.

Presented by

Naresh Reddy G.N

2nd Sem. M.Tech.

CONTENTS

1. Definition

2. Usage

3. History

4. Concept of SVM

5. Classification of SVM Models

6. Applications

7. Analysis of GPC Slabs

8. Limitations

9. References

Definition

Support Vector Machine (SVM) is a technique to analyze data and recognize patterns, used for classification and regression analysis.

Usage

Widely used for predictions and forecasting.

History

Originally developed by Vladimir N Vapnik in 1992.

Concept of SVM

H3 (green) doesn't separate the two classes. H1 (blue) does, with a small margin H2 (red) with the maximum margin.

Classification of SVM Models

Linear SVM Non Linear SVM

Basis of classification

Kernels: Mathematical functions such as linear, polynomial, sigmoid, radial basis function.

Applications of SVM

Text (and hypertext) categorization

Image classification

Face recognition

Bioinformatics (Protein classification, Cancer classification)

Hand-written character recognition

Engineering Applications (Environmental, Traffic data

analysis, Remote Sensing)

Versions AvailableSVMdark, SVMlight, SVMstruct, mySVM, libSVM.

Using SVMdark for comparison of experimental and predicted values for GPC Slabs.

Procedure

1.The experimental data is split into three sets (in the ratio 50%, 25%, 25%) namely training set, validation set and test set.

2.The train set and the validation set are used to fit in a suitable decision boundary based on regression analysis.

3.Further based on the constants obtained through optimizing the data, the test data is used to obtain the predicted values.

Input Output

Exp. Load (in KN)

Deflection (in mm)

Predicted Load (in KN)

Training Set(50%)

0 0

2 0.18

4 0.415

6 0.842

8 1.346

10 1.858

12 2.429

14 2.75

16 3.184

18 3.622

Validation Set(25%)

20 4.087

22 4.695

24 5.104

26 5,687

28 6.266

Test Set(25%)

30 6.786 ?

32 6.894 ?

34 6.936 ?

36 7.284 ?

38 7.316 ?

Slab Details

M60 Grade GPCSimply Supported

1080X500X65 mm

Input Output

Exp. Load (in KN)

Deflection (in mm)

Predicted Load (in KN)

Training Set(50%)

0 0

2 0.18

4 0.415

6 0.842

8 1.346

10 1.858

12 2.429

14 2.75

16 3.184

18 3.622

Validation Set(25%)

20 4.087

22 4.695

24 5.104

26 5,687

28 6.266

Test Set(25%)

30 6.786 29.701195

32 6.894 31.093738

34 6.936 33.241844

36 7.284 34.502888

38 7.316 36.617127

Limitations of SVM

Choice of the Kernel.

Selection of Kernel function parameters.

Depends only on a subset of the training data.

Speed

References

A User's Guide to Support Vector Machines.- Asa Ben-Hur & Jason Weston

The Solution Path of the Slab Support Vector Machine.

- Michael Eigensatz, Joachim Giesen & Madhusudan

Wikipedia

END