Analysis of GPC Slabs Using SVM
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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.
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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
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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.
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Concept of SVM
H3 (green) doesn't separate the two classes. H1 (blue) does, with a small margin H2 (red) with the maximum margin.
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Classification of SVM Models
Linear SVM Non Linear SVM
Basis of classification
Kernels: Mathematical functions such as linear, polynomial, sigmoid, radial basis function.
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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.
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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.
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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
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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
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Limitations of SVM
Choice of the Kernel.
Selection of Kernel function parameters.
Depends only on a subset of the training data.
Speed
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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
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END