ebook img

Study on Reinforced Concrete Frame with Solid Infill Masonry using Artificial Neural Network (ANN) RC Infill Wall Model in ANSYS (Ajay Gupta) PDF

82 Pages·2011·1.55 MB·English
Save to my drive
Quick download
Download
Most books are stored in the elastic cloud where traffic is expensive. For this reason, we have a limit on daily download.

Preview Study on Reinforced Concrete Frame with Solid Infill Masonry using Artificial Neural Network (ANN) RC Infill Wall Model in ANSYS (Ajay Gupta)

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 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) iii 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. iv 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 v 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 vi 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 viii

See more

The list of books you might like

Most books are stored in the elastic cloud where traffic is expensive. For this reason, we have a limit on daily download.