TWO DIMENSIONAL NETWORK ADJUSTMENT AND DEFORMATION ANALYSIS VIA MATLAB

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1 I TWO DIMENSIONAL NETWORK ADJUSTMENT AND DEFORMATION ANALYSIS VIA MATLAB Roya Olyazadeh A thesis submitted in fulfillment of the requirements for the award of the degree of Master of (Geomatic Engineering) Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia JULY 2010

2 To my beloved mother and father III

3 IV AKNOWLEDGMENT First and foremost, I would like deeply to thank to my supervisor of this project, Professor Dr. Halim Setan for the valuable guidance and advice. He inspired me greatly to work in this project. His willingness to motivate me contributed tremendously to my project. I also would like to thank him for showing me some examples that related to the topic of my project. Besides, I would like to thank the authority of University Technology Malaysia (UTM) for providing me with a good environment and facilities to complete this project. Thanks and appreciation to the helpful people at Mathworks community to handle programming difficulties via MATLAB specially Mr.Walter Roberson. Finally, an honorable mention goes to my families and friends for their understandings and supports on me in completing this project specially my parents and my friends. For all the people who helped me a lot, thank you very much and may god bless you all. Without helps of the particular that mentioned above, I would face many difficulties while doing this project. And it would not be successful without God who guides me in my everyday life and activities; I thank him for the good health he has given to me, and for the success of my study.

4 V ABSTRACT Network adjustment and deformation monitoring are one of the main activities of engineering surveying. Geodetic networks are estimated by the method of least-squares estimation (LSE) adjustment and the goodness of the network is measured by a precision analysis based upon the covariance matrix of the estimated parameters. When such a network is designed, global test and local test are assessed to test compatibility of variance factor via two-tailed chi-square distribution and residual of measurements to detect gross error by using a technique pioneered by the geodesist Baarda. The second step is Deformation detection. Generally, the deformation computations can be divided into geotechnical and geodetic methods. There are two types of geodetic networks, namely the absolute and relative networks. In the absolute network, some of the points are assumed to be located out of the deformable body. However, in a relative network, all points are assumed to be located on the deformable body. This study focuses on two dimension (2-D) absolute geodetic deformation detection via MATLAB7. Deformation analysis can be handled by several techniques such as Robust (IWST and LAS), Congruency, Bayesian method and etc. This study presents a deformation analysis procedure via geodetic methods that consists of LSE of individual epochs, trend analysis of the displacement field and the deformation detection by IWST between epochs. This research focuses on the development of two programs in MATLAB7 for network adjustment (distance, angle and azimuth observation respectively) and deformation detection between two epochs. In this project, first LSE adjustment is done for each epoch and creates the appropriate input files for deformation program. Next, deformation program reads them and creates an output file and displays a graphic consists of error ellipse for each stations and displacement vector for moved stations. There are three data sets to run these two programs. For adjustment part the results are compared with STARNET results and for deformation part the results are compared with previous works by Caspary (1987), Ranjit(1999) and Khairulnizam(2004).The results show that these two programs work and there are not significant difference between these results and results from other works.

5 VI ABSTRAK Pelarasan jaringan dan pengawasan deformasi merupakan salah satu kegiatan utama dalam ukur kejuruteraan. Jaringan geodetik dianggarkan dengan kaedah pelarasan kuasa dua terkecil (LSE) dan kualiti jaringan diterbitkan berdasarkan analisis daripada matriks kovarians bagi setiap parameter terlaras. Apabila jaringan tersebut direka, ujian global dan ujian lokal dijalankan untuk menguji keserasian faktor varians melalui taburan dua hujung khi kuasa dua dan sisa pengukuran untuk mengesan selisih kasar dengan menggunakan teknik yang dipelopori oleh penyelidik Baarda. Langkah kedua adalah pengesanan deformasi. Secara umum, perhitungan deformasi boleh dibahagikan kepada kaedah geoteknik dan geodetik. Terdapat dua jenis jaringan geodetik iaitu jaringan absolut dan relatif. Dalam jaringan absolut, beberapa titik kawalan diandaikan terletak diluar jasad tercanggakan. Namun, dalam jaringan relatif, semua titik kawalan diandaikan terletak dalam jasad tercanggakan. Kajian ini menumpu pada pengesanan deformasi dua dimensi (2D) geodetik absolut melalui MATLAB7. Analisa deformasi dapat diterbitkan oleh beberapa teknik seperti kaedah Robust (IWST dan LAS), Congruency, Bayesian dan lain-lain. Kajian ini menbentangkan prosidur analisis deformasi melalui kaedah geodetik yang terdiri daripada LSE bagi setiap epok, analisis trens tentang magnitud anjakan dan pengesanan deformasi oleh IWST antara epok. Kajian ini berfokus pada pembangunan dua program dalam MATLAB7 untuk pelarasan jaringan (cerapan jarak, sudut dan azimut masing-masing) dan pengesanan deformasi antara dua epok. Dalam projek ini, pelarasan LSE dilakukan terlebih dahulu untuk setiap epok dan menerbitkan fail input yang sesuai untuk program deformasi. Selanjutnya, program deformasi membaca fail input yang diterbitkan dan menghasilkan output fail serta memaparkan grafik yang terdiri daripada selisih elips bagi setiap stesen dan vektor anjakan bagi stesen yang telah berganjak. Terdapat tiga dataset dihitung dengan kedua-dua program tersebut. Untuk bahagian pelarasan, hasil dibandingkan dengan keputusan STARNET dan untuk bahagian deformasi pula hasil dibandingkan dengan kerja sebelumnya oleh Caspary (1987), Ranjit (1999) dan Khairulnizam (2004). Kesimpulannya, kajian ini menunjukkan bahawa kedua-dua program berfungsi dan hasil yang didapati tidak banyak perbezaan berbanding dengan kerja sebelumnya.

6 1 CHAPTER ONE INTRODUCTION 1.1 Background Deformation measurements are one of the most important activities of engineering surveying. Deformation analysis is one of the main research fields in geodesy and geomatic. Deformation analysis process comprises of measurement and analysis phases. Measurements can be collected using several techniques such as Total station, GPS, Laser scanner, Photogrammetry or combined techniques. The output of the evaluation of the measurements is mainly point positions. In the deformation analysis phase, the coordinate changes in the point positions are investigated. Generally, the deformation measurement techniques can be divided into geotechnical, structural and geodetic methods. In the geodetic method there are two basic types of geodetic monitoring networks; namely the reference (absolute) and relative networks (Chrzanowski et al., 1986). In a reference network, some of the points or stations are assumed to be located outside of the deformable body or object, thus serving as reference points for the determination of the absolute displacements of the object points. However, in a relative network, all surveyed points are assumed

7 2 to be located on the deformable body (Setan and Singh, 2001). In this study 2D absolute geodetic deformation is considered. 1.2 Problem Statement The results of deformation measurements are related to direct safety of engineering structures and human life. To avoid a wrong interpretation of displacements, an appropriate deformation monitoring network must be established and the data obtained from deformation monitoring network must be carefully evaluated. Deformation measurements and analysis require the use of very accurate surveying equipment and analysis methods (Setan and Singh, 1999). Therefore, deformation detection and network analysis is very important to represent movement of subjects. There are several known adjustment and deformation system software packages, from different university research group like ADJUST4.6 (Ghilani, 2006), also commercial software like STARNET (Starplus Software, 2000), GEOLAB (Micro Search, 1985) and PANDA (GeoTec GmbH, 2004). Specialized computer programming is required because most commercial software like STARNET does not provide the required data for deformation detection. And some of them are not easy to use. This research focuses on the development of a program via MATLAB7. MATLAB is a mathematical computer programming that covers all mathematical functions are required in surveying and this special subject, deformation. So in this work MATLAB programming is developed for network adjustment and (distance, angle and azimuth observation respectively) deformation detection where Adjustment program can create required data for deformation detection. These programs are not as powerful as other software and especially commercial software.

8 3 1.3 Aim and objective of the study The purpose of this work is to monitor and analyze the displacements of deformation for 2D network IWST (Iterative Weighted Similarity Transformation) robust method after network adjustment. Robust method is used when there is no previous information about the movement of points within the network (Singh & Setan 1999). IWST, developed by Chen (1983) in the New Brunswick University, is known as the robust method. The secondary goal is to program via MATLAB and determine whether this program can be converted to executable (EXE) file by using MATLAB compiler. Consequently the specific purposes of this work are: 1. Network Adjustment 2. Deformation Analysis 3. MATLAB Programming 1.4 Significance of the study The results of deformation measurements are related to direct safety of engineering structures and Human life. To avoid a wrong interpretation of displacements, an appropriate deformation monitoring network must be established and the data obtained from deformation monitoring network must be carefully evaluated. Deformation measurements and analysis require the use of very accurate surveying equipment and analysis methods. This project is only limited to 2D network adjustment and 2D deformation detection for distance, angle and azimuth observations.

9 4 1.5 Research Scope The research scope covers the limits of the research. In this project the limits are as follows; The research area covers a real network with 6 stations and 4 stable stations. For network adjustment and deformation detection, MATLAB programming is carried out, while this program is objected to do all 2D network adjustments and deformations with distance and angle and azimuth. Another example have been used to test, is a known data of a dam monitoring network, taken from Caspary (1987) with 12 stations and 7 stable stations. The third data set is 12 stations with distance and azimuth observation (Setan, Abidin Md Som and M.Idris, 2003). 1.6 Research Methodology Several methods or approaches can be employed for deformation analysis like The robust analysis of deformation (Caspary, 1987),The Generalized approach (Chen UNB, 1986), The integrated analysis (Teskey, 1988),The Grey theory (Chen HK),The robustness analysis (Vanicek).This study will be focused on robust method ( IWST) via MATLAB. Robust method was made using both an Iterative Weighted Similarity Transformation (IWST) and the Least Absolute Sum (LAS) method. The commonly adopted methods for monitoring deformation by geodetic methods are based on the repeated observation of a survey monitoring network at difference epochs (Caspary, 1987), followed by two-step analysis (i.e. independent least squares estimation (LSE) or adjustment of single epoch, and deformation detection between epochs). The detection of deformation uses two-epoch analysis, an absolute monitoring network and a static model to compare the coordinate between the epochs (Setan, 1997).

10 5 Deformation analysis using the geodetic method mainly consists of these steps analysis (Setan and Singh, 2001): 1. pre-analysis 2. data acquisition 3. The network least squares adjustment 4. Trend analysis 5. Deformation analysis LITERATURE REVIEW Choose one method for Deformation Program via MATLAB for Adjustment (LSE) Check results with STARNET Robust Method (IWST) To check if MATLAB can compile to an Executable file Does MATLAB can convert.m file to a web page? Web Server Program via MATLAB for Deformation analysis (IWST) Two Epoch Analysis Deformation Figure 1.1 Flowchart of the research methodology 1. Literature review stage The literature review is to explore the 2D network adjustment computation and deformation analysis methodology referring to Internet, books or journal paper.

11 6 2. Planning stage The planning stage is where preparation is done prior to research implementation. This stage consists of selection for deformation detection methodology as well as adjustment computation, MATLAB programming practice and learning how to use compiler in MATLAB and to launch program in a web server by learning Java or.net. 3. Implementation stage This stage involves what is done in order to obtain the final output for deformation detection. The process is; Input data, processing via MATLAB and output file for deformation and adjustment and graphic display. First input data (approximate coordinates and observation) can be read by MATLAB, and then programming for this network is performed. Finally results are saved in text file and graphics display in a plot. 4. Results and analysis stage After implementation stage, the final results are produced from adjustment program and deformation program which analysis is made for further decisions and conclusion as well as future work. 5. Conclusion and recommendation stage The final outcome of the entire process in this thesis is concluded, from which necessary recommendations are made based on the outcome.

12 7 1.7 Thesis Outline The thesis is in six chapters as detailed below: Chapter one: Introduce the research topic, the background of the study, problem statement, research objective, scope and methodology. Chapter two: This chapter covers literature review which is to explore methodology for adjustment and deformation and software packages that can perform these analyses. Chapter three: This chapter provides the methodology of this study. The steps include; data input, processing stage and output. Chapter four: This chapter discusses implementations of this research. The steps include; how text file can be read by MATLAB, processing and iterations, M file to function and how to make an executable file by compiler and launch it to web server. Chapter five: This chapter discusses the results and analysis of this study from which necessary conclusion and recommendation will be made. Chapter six: In this chapter, conclusions and recommendation for future study are discussed.

13 VII LIST OF CONTENTS CHAPTER TITLE PAGE DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF APPENDICES II III IV V VI VII XI XIV XVII 1 INTRODUCTION Background Problem Statement Aim and objective of the study Significance of the study Research Scope Research methodology Thesis outline 7

14 VIII 2 LITERATURE REVIEW Deformation definition Deformation analysis The network adjustment computation Adjustment programming STARNET GEOLAB MOVE ADJUST Deformation methodology Robust Method Congruency Method Deformation Modeling Deformation Monitoring System software packages DETECT GPSAD2000/DEFORM DEFORM GPSAD PANDA LOCATA DIMONS 28 3 RESEARCH METHODOLOGY Introduction Network Adjustment LSE 31

15 IX Global test and local test Error Ellipses Deformation Analysis Datum Defect S-transformation Initial checking of data and test on Variance Ratio Trend analysis Robust Method (IWST) Final S-transformation 45 4 IMPLEMENTATION Introduction Data Input Adjustment File Deformation File MATLAB Procedure Reading text in MATLAB SYMS and SUBS in MATLAB Rearranging Matrixes in MATLAB Chi-Square and F distribution in MATLAB Plot Error Ellipse and displacement vector Script M-file to Function M-file Creating Graphical User Interfaces (GUIDE) Features Stand Alone Executable file for M file MATLAB Web Server 69 5 DISCUSSION AND RESULT 71

16 X 5.1 Introduction Adjustment Results LSE Results for data set one Data set one,epoch Data set one,epoch LSE Results for data set two Data set two,epoch Data set two,epoch LSE Results for data set three Data set three,epoch Data set three,epoch Deformation Results Data set one Data set two Data set three 99 6 CONCLUSION AND RECOMMENDATION Introduction Conclusion Recommendation 104 REFERENCES 106 Appendices A C

17 XI LIST OF TABLES TABLE NO. TITLE PAGE 3.1 Confidence region ellipses Approximate Coordinates for data set one Observations for data set one epoch Comparison results of LSE between Adjustment Program, STARNET and program by Singh R Coordinates comparison of first epoch between Adjustment program and STARNET Error Ellipse comparison of first epoch between Adjustment program and STARNET Observations for data set one epoch Comparison results of LSE between Adjustment program, STARNET and program by Singh R Coordinates comparison of second epoch between Adjustment program and STARNET Error Ellipse comparison of second epoch between Adjustment program and STARNET Approximate coordinates for data set two Observations for data set two epoch one Comparison results of LSE between Adjustment program and STARNET 80

18 XII 5.13 Coordinates comparison of first epoch between Adjustment program and STARNET Error Ellipse comparison of first epoch between Adjustment program and STARNET Observations for data set two epoch two Comparison results of LSE between Adjustment program and STARNET Coordinates comparison of second epoch between Adjustment program and STARNET Error Ellipse comparison of second epoch between Adjustment program and STARNET Approximate coordinates for data set Three Observations for data set three epoch one Comparison results of LSE between Adjustment program and STARNET Coordinates comparison of first epoch between Adjustment program and STARNET Error Ellipse comparison of first epoch between Adjustment program and STARNET Observations for data set three epoch two Comparison results of LSE between Adjustment program and STARNET Coordinates comparison of second epoch between Adjustment program and STARNET Error Ellipse comparison of second epoch between Adjustment program and STARNET Deformation results of data set one for Deformation program vs. program by Singh R Displacement vector and single point test for data set one for Deformation program and Program by Singh R Difference in displacement vector and single point test for data set one for Deformation program and Program by Singh R. 95

19 XIII 5.31 Deformation results of data set two for Deformation program vs. Caspary Displacement vector and single point test for data set two for Deformation program and Caspary(mm) Difference in displacement vector (Delta Disp.) for data set two between Deformation program and Caspary Deformation results of data set three for Deformation program vs. Program by Khairulnizam Displacement vector and single point test for data set three for Deformation program Difference in displacement vector for data set three for Deformation program (Disp1) and Khairulnizam (Disp2) 102

20 XIV LIST OF FIGURES FIGURE NO TITLE PAGE 1.1 Flowchart of the research methodology Preview of STARNET Preview of GEO-LAB Preview of MOVE Preview of ADJUST Linking STARNET and GPSAD2000/DEFORM Program DEFORM Program GPSAD PANDA Program DIMONS Flowchart of least square estimation with statistical testing Observation of horizontal angle, azimuth and horizontal distance Error ellipse Flowchart of Deformation detection by robust method Chi-Square graph F distribution graph 57

21 XV 4.3 Error Ellipse Displacement Vector GUI Flowchart GUIDE Quick Start Dialog Layout Editor Callback Editor Embedded MATLAB and MATLAB Compiler Embedded MATLAB MATLAB Compiler MATLAB DEPLOYTOOL New Deployment Project MATLAB Web Server Adjustment Program and STARNET plots, Data set one epoch one Adjustment Program and STARNET plots, Data set one epoch two Adjustment Program and STARNET plots, Data set two epoch one Adjustment Program and STARNET plots, Data set two epoch two Adjustment Program and STARNET plots, Data set three epoch one Adjustment Program and STARNET plots, Data set three epoch two Displacement vector and error ellipse graphic by Deformation program for data set one Displacement vector and error ellipse graphic by Singh R. program for data set one Displacement Vector (Disp.) 94

22 XVI 5.10 Displacement vector and error ellipse graphic by Deformation program for data set two Displacement vector and error ellipse graphic by Singh R. program for data set two Displacement Vector (Disp.) Displacement vector and error ellipse graphic by Deformation program for data set three Displacement vector for data set three for program by Khairulnizam Displacement Vector (Disp.) 101

23 XVII LIST OF APPENDICES APPENDIX TITLE PAGE A. Results for data set one 111 B. Results for data set two 119 C. Results for data set three 128

PROCEDURE FOR DEFORMATION DETECTION USING STARNET

Proceedings, 11 th FIG Symposium on Deformation Measurements, Santorini, Greece, 2003. PROCEDURE FOR DEFORMATION DETECTION USING STARNET Halim Setan and Mohd Sharuddin Ibrahim Department of Geomatic Engineering,