Table Of ContentTRIBHUVAN UNIVERSITY
INSTITUTE OF ENGINEERING
PULCHOWK CAMPUS
DEPARTMENT OF CIVIL ENGINEERING
M .Sc. Program in Structural Engineering
Thesis No: SS00147
STUDY ON REINFORCED CONCRETE FRAME WITH SOLID INFILL
BRICK MASONRY USING ARTIFICIAL NEURAL NETWORK
AJAY KUMAR GUPTA
February, 2011
TRIBHUVAN UNIVERSITY
INSTITUTE OF ENGINEERING
PULCHOWK CAMPUS
DEPARTMENT OF CIVIL ENGINEERING
M .Sc. Program in Structural Engineering
Thesis No: SS00147
STUDY ON REINFORCED CONCRETE FRAME WITH SOLID INFILL
BRICK MASONRY USING ARTIFICIAL NEURAL NETWORK
A Thesis Submitted By
AJAY KUMAR GUPTA
In partial fulfillment of the requirement for the degree of
MASTER OF SCIENCE
IN
STRUCTURAL ENGINEERING
February, 2011
COPYRIGHT ©
The author has agreed that the Library, Department of Civil Engineering, Institute of
Engineering, Pulchowk Campus, may make this thesis freely available for inspection.
Moreover, the author has agreed that permission for extensive copying of this thesis
for scholarly purpose may be granted by the professor who supervised the thesis work
recorded herein or, in his absence, by the Head of the Department or concerning
M.Sc. program coordinator or the Dean of the Institute in which thesis work was
done. It is understood that the recognition will be given to the author of this thesis and
to the Department of Civil Engineering, Institute of Engineering, Pulchowk Campus,
in any use of the material of this thesis. Copying or publication or other use of the
thesis for financial gain without approval of the Department of Civil Engineering,
Institute of Engineering, Pulchowk Campus and the author’s written permission is
prohibited. Request for permission to copy or to make any other use of the material of
this thesis in whole or in part should be addressed to:
…………………………..
Head
Department of Civil Engineering
Pulchowk Campus
Institute of Engineering
Lalitpur, Nepal.
i
CERTIFICATE
This is to certify that the work contained in this thesis entitled “Study on Reinforced
Concrete Frame with Solid Infill Brick Masonry using Artificial Neural
Network” submitted by Mr. Ajay Kumar Gupta (Roll No. 065/MSS/r/102) for the
award of partial fulfillment of the degree of Master of Science in Structural
Engineering of Institute of Engineering, Tribhuvan University, Kathmandu is a
bonafide record of work carried out by him under my supervision and guidance, no
part of it has been published or submitted elsewhere for the award of degree.
……………………………….. …………………….
Assoc. Prof. Prajwal Lal Pradhan Date
Department of Civil Engineering
Institute of Engineering
Pulchowk Campus
Lalitpur, Nepal
ii
ACKNOWLEDGEMENT
I would like to express my deep gratitude to my thesis supervisor, Assoc. Prof.
Dr. Prajwal Lal Pradhan his valuable guidance, expertise, encouragement and critical
suggestion without whom, this thesis could not come in this complete form. I highly
appreciate his scholastic attitude and pragmatic thinking over thesis problems.
I also express my gratitude to Mr. Shashidhar Ram Joshi, HOD, Department
of Electronics, Pulchowk campus for giving me the concept of Artificial Neural
Network. He has allowed me to attend the full semester course afford by the
Electronics department for the M.Sc. students of that department. He has also helped
me during my thesis period.
Special appreciation goes to all the teachers of the Department of Civil
Engineering, Pulchowk Campus, especially Prof. Dr. Prem Nath Maskey, Prof. Dr.
Hikmat Raj Joshi and Dr. Jishnu Subedi for their kind support and suggestions during
the entire thesis period.
I owe a debt of gratitude to many other seniors and colleagues who provided
technical support and social encouragement, especially Mr. Sujan Tripathi, Mr.
Dinesh Gupta, Mr. Anup Chaudhary, Mr. Arvind Jha and Mr. Chandan Karna. And
they, all, by virtue of proximity, became living sounding boards of ideas.
Finally, I would like to express my profound gratitude to my family for their
continuous support and encouragement during my study period.
Ajay Kumar Gupta
(065/MSS/r/102)
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TRIBHUVAN UNIVERSITY
INSTITUTE OF ENGINEERING
DEPARTMENT OF CIVIL ENGINEERING
M .Sc. Program in Structural Engineering
ABSTRACT
Student: Ajay Kumar Gupta Supervisor: Dr. Prajwal Lal Pradhan
Despite being the most common construction practice throughout the ages, infills
have not found the space it deserves, in the structural design. There is lack of proper
and easy method to consider the effect of the in-filled. So, this research is a small
effort in the search of the alternative approach for analyzing the infill frames. The
FEM Models are normally incapable of considering all the effecting factors such as
non-linear behavior of the infill materials, lack of fit, non-homogeneity of the
materials, etc. This research gives some idea to the structural engineer how to guess
initially the parameters of interest during the design of infills. Structural design
process is an iterative process and an approximate initial guess can reduce the time
and cost involved in the analysis. The tentative design parameters can be predicted
using the Artificial Intelligence and this computing power of the modern day
computers has been used to fulfill the intended purpose. The data sets, which are
generated by computer from the simulation of the infill-frame structure done in
sophisticated software (ANSYS v10.0) capable of non-linear analysis, are used for the
training of Neural Network. Few other unique data sets are taken for the validation of
the Network trained. The comparison of the results from the ANN and that of
software were in reasonable agreement with each other except in few rare cases.
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TABLE OF CONTENTS
COPYRIGHT © ………………………………………………………………………………………. i
CERTIFICATE ……………………………………………………………………………………….. ii
ACKNOWLEDGEMENT ……………………………………………………………………………. iii
ABSTRACT …………………………………………………………………………………………… iv
LIST OF TABLES …………………………………………………………………………………….. vii
LIST OF FIGURES …………………………………………………………………………………… viii
LIST OF SYMBOLS ………………………………………………………………………………….. ix
CHAPTER 1: INTRODUCTION …………………………………………………………………… 1
1.1 Infilled Frames 1
1.2 Background 2
1.3 Why this Study ? 3
1.4 Objectives of the Study 5
1.5 Methodology 5
1.5.1 Review of Literature 6
1.5.2 Collection of Input Data 6
1.5.3 Modeling of the Structure 7
1.5.4 Analysis of the Structure 7
1.5.5 Result Validation 7
CHAPTER 2: REVIEW OF LITERATURE ………………………………………………………… 8
2.1 General 8
2.2 Experimental Studies 8
2.3 Analytical Studies 9
2.4 About ANN 12
2.4.1 Back-Propagation Neural Network 13
2.4.2 The Back-Propagation Training Algorithm 14
CHAPTER 3: REINFORCED CONCRETE INFILL FRAME MATERIALS …………………….. 16
3.1 General 16
3.2 Masonry 16
3.3 Bricks 17
3.4 Mortar 18
3.5 Reinforced Concrete 19
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CHAPTER 4: FINITE ELEMENT MODEL …………………………………………………………. 22
4.1 About ANSYS 22
4.2 Modelling Strategy 23
4.2.1 Calibration of the Model 23
4.2.2 Dimensions of the Model 23
4.3 Description of the Models 24
4.3.1 Element used in Modeling 24
4.3.2 Boundary Conditions Imposed 25
4.3.3 Overview of the Materials Used 26
4.3.4 Model Descriptions 27
4.3.5 The Outputs 27
4.4 Preparation of the Training sets 28
CHAPTER 5: TRAINING THE DATA SETS USING ANN ………………………………………... 30
5.1 Introduction to NeuNet Pro 30
5.2 Development of ANN Tool 30
CHAPTER 6: RESULTS AND DISCUSSIONS …………………………………………………….. 32
6.1 Parametric Studies 32
6.2 Geometric Parameters 33
6.2.1 Influence of wall thickness 34
6.2.2 Influence of Aspect Ratio 35
6.2.3 Influence of Bricks 37
6.2.4 Influence of Mortar 37
6.3 Variation of stiffness 38
6.4 Effective width of equivalent diagonal strut 40
6.5 Validation of Neural Network 42
CHAPTER 7: CONCLUSIONS AND RECOMMENDATIONS ……………………………………. 48
7.1 General 48
7.2 Conclusions 50
7.3 Recommendations for the future works 51
APPENDIX ……………………………………………………………………………………………… 53
A. ANSYS Contour Result Plot 53
B. Output Result from ANSYS 55
REFERENCES ………………………………………………………………………………………….. 70
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LIST OF TABLES
Table 2-1: Analogy between biological and artificial neural networks 13
Table 3-1: Types and Properties of Bricks (Pradhan, P.L., 2009) 17
Table 3-2: Types and Properties of Mortar (Pradhan, P.L., 2009) 19
Table 3-3: Properties of concrete and rebars used in analysis 20
Table 4-1: Material properties used in analysis (Pradhan, P.L., 2009) 26
Table 4-2: Designation of Models used for analysis 27
Table 6.1: Parameters of Interest 32
Table 6-2: Parametric characteristics of infilled frames analysed 33
Table 6-3: Response variation due to wall thickness 35
Table 6-4: Response variation due to aspect ratio 36
Table 6-5: Response variation due to Bricks 37
Table 6-6: Response variation due to Mortar 38
Table 6-7: Comparison of strut widths 41
Table 7-1: Comparison of Results obtained from ANSYS and ANN 43
Table A-1: ANSYS Results for Span 3m 55
Table A-1: ANSYS Results for Span 3.5m 58
Table A-1: ANSYS Results for Span 4m 61
Table A-1: ANSYS Results for Span 4.5m 64
Table A-1: ANSYS Results for Span 5m 67
vii
LIST OF FIGURES
Figure 1-1: Structure of Artificial Neural Network model 3
Figure 1-3: Flow Chart for Analysis of Infill Wall using ANN 6
Figure 2-1: Single Diagonal Strut Models (Smith and Carter 1969) 9
Figure 2-2: Geometric characteristics in Equations. (2-8) and (2-9) 12
Figure 2-3: Biological Neural Network 13
Figure 2-4: Typical Back-Propagation Network 14
Figure 3-1: Stress-strain characteristics of different bricks used. 18
Figure 4-1: Plane stress element used for Modeling 24
Figure 4-2: Beam3 element used for Modeling Beam and Column 25
Figure 4-4: Sample model prepared for the analysis 26
Figure 4-5: Salient nodal points considered for the output 28
Figure 5-1: Back-propagation Neural Network used for Training 31
Figure 5-2: Error reduction graph during Back-propagation Neural Network Training 31
Figure 6-1 : Infilled frames with different Aspect Ratios 34
Figure 6-2: Variation of displacement with span 39
Figure 6-3: Variation of stiffness with load 40
Figure 6-4 : Equivalent Strut Model 41
Figure 6-5 : Comparison of actual versus predicted data 42
Figure A-1 : Displacement contour plot 53
Figure A-2 : Stress Intensity contour plot 53
Figure A-3 : Shear Stress contour plot 54
Figure A-4 : X-axis Stress contour plot 54
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