Faculty of Engineering

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Faculty of Engineering

Development of Time Domain Inverse Scattering Algorithm for the Detection and Imaging of Buried Objects

Deanne Anak Edwin

Master of Engineering 2020

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Development of Time Domain Inverse Scattering Algorithm for the Detection and Imaging of Buried Objects

Deanne Anak Edwin

A thesis submitted

In fulfilment of the requirement for the degree of Master of Engineering

(Electronics Engineering)

Faculty of Engineering

UNIVERSITI MALAYSIA SARAWAK 2020

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DECLARATION

I declare that the work in this thesis was carried out in accordance with the regulations of Universiti Malaysia Sarawak. Except where due acknowledgements have been made, the work is that of the author alone. The thesis has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.

………

Signature

Name: Deanne Anak Edwin

Matric No.: 16020116

Faculty of Engineering Universiti Malaysia Sarawak Date : 24^th November 2020

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DEDICATION

Dedicated to my beloved parents, relatives and friends

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ACKNOWLEDGEMENT

Firstly, I want to express my deepest appreciation to my main supervisor, Dr Shafrida Sahrani for her valuable inputs, able guidance, encouragement, whole-hearted cooperation and constructive criticism throughout the duration of my project. I am greatly indebted to her and this research is an acknowledgement of her tenacity and confidence in me.

Not to forget the contribution of my co-supervisor, Assoc. Prof. Ir. Dr Kismet Hong Ping for his insightful comments and encouragement, but also for the hard question which helped me to widen my research from various perspectives.

I would like to thank and express my gratitude to my parents for giving me unfailing support and encouragement throughout my years of study and through the process of researching and writing this thesis. This accomplishment would not have been possible without them. Thank you.

My special gratitude and thanks to my research team, Azarina Azman, Eustacius Jude, Nurul Syuhada and Bong Siaw Wee, who were abundantly helpful and offered invaluable assistant and sharing their knowledge with constructive thought throughout my research.

I would like also to express my gratitude to the lecturers, technicians and staff in the Department of Electronic Engineering, Faculty of Engineering, who have offered their advice during the course of my study.

Last but not least, I would like to thank all my friends for their constructive advices and motivational supports. A token of appreciation is also dedicated towards anyone who directly or indirectly helped in making this research a success.

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iv ABSTRACT

The tremors from the earthquake created heavy damages and cracks to some buildings, infrastructures and caused landslips. Therefore, the post-hazard assessments have to be held to certify the quality and condition of the damaged buildings, infrastructures and lands before continue to use it in the future. Object and crack detection is widely used in structural health monitoring (SHM) application especially in civil structure. There are some of previous methods use for the object and crack detection such as ground penetrating radar (GPR), non-destructive microwave radar and analytical method. Those methods are able to detect the presence of the unknown buried object. However, the information that obtained from the methods is not enough because they are not able to reconstruct the image such as shape and size of the unknown objects. In this research, a new approach is proposed which combines the advantages of both Forward Backward Time Stepping (FBTS) technique and Overset Grid Generation (OGG) method in Finite Difference Time Domain (FDTD) method to develop the efficient numerical method for the image reconstruction in the detection of unknown object and cracks under the soil.

Firstly, the accuracy of proposed method is investigated by analysing the measured electric signal or direct problem with empty grids and in stationary case between the proposed method with FBTS technique utilizing FDTD method only. Then, the investigation is furthered to inspect the accuracy of the proposed method by analysing the different kind ratio of grid size between the main mesh and sub mesh. The proposed method is then applied to SHM application focusing on crack and object detection. From the results obtained in Section 4.6.1(a) and 4.6.2(a), it is shown that the proposed method has 5.22%

error for object detection; meanwhile the crack detection has 21.55% respectively.

Therefore, it is observed that the proposed method can detect and reconstruct the image of

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the objects and crack clearly because the percentage of relative error is near to the actual value.

Keywords: Microwave imaging, inverse scattering, overset grid generation, Finite Difference Time Domain, Forward Backward Time Stepping, image reconstruction, buried object, crack detection

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Pelaksanaan Algoritma Teknik Hamburan Songsang dalam Domain Masa untuk Pengesanan dan Pengimejan Objek yang Tertimbus

ABSTRAK

Gegaran yang berlaku semasa gempa bumi telah menyebabkan banyak kerosakan dan keretakan pada bangunan dan infrastruktur serta tanah runtuh. Oleh itu, penilaian taraf bahaya bangunan yang terjejas perlu dilakukan untuk memastikan kualiti dan keadaan bangunan, infrastruktur dan tanah yang rosak sebelum meneruskan penggunaan kawasan tersebut pada masa yang akan datang. Pengesanan objek dan retak digunakan secara meluas dalam aplikasi pemantauan kesihatan struktur (SHM) terutamanya dalam struktur sivil. Sebelum ini, terdapat beberapa cara digunakan untuk pengesan objek dan retak seperti “Ground Penetrating Radar (GPR)”, “non-destructive microwave radar” dan

“analytical method”. Namun, kesemua cara ini hanya dapat mengesan kewujudan objek dan retak sahaja dan tidak dapat memberi informasi yang penuh seperti untuk mengesan saiz, bentuk dan bahan objek tersebut. Oleh itu, tesis ini menggabungkan kelebihan dua teknik iaitu “Forward Backward Time Stepping (FBTS)” dan kaedah “Overset Grid Generation (OGG)” di “Finite Difference Time Domain (FDTD)” untuk membangunkan kaedah berangka yang efisien untuk pembinaan semula imej dalam mengesan objek dan keretakan di bawah tanah. Pertama, ketepatan gabunggan kaedah ini dikaji dengan membandingkan gabungan kaedah yang baru dengan FBTS-FDTD. Seterusnya, ketepatan kaedah ini dikaji dengan simulasi menggunakan nisbah ukuran grid yang berbeza. Akhir sekali, gabungan kaedah yang baru ini akan dilaksanakan dalam pengesanan objek dan retak. Melalui keputusan dari kajian yang dilakukan, peratusan kesalahan untuk pengesanan objek dan retak ialah 5.22% dan 21.55%. Berdasarkan peratusan tersebut, gabungan kaedah yang baru ini terbukti dapat mengesan objek dan retak kerana nilai yang ditunjukkan sangat hamper dengan nilai sebenar.

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Kata kunci: Pengimejan gelombang mikro, songsang berselerak, janaan grid bertindih,

“Finite Difference Time Domain”, “Forward Backward Time Stepping”, pembinaan semula imej, objek tertimbus, pengesanan keretakan

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TABLE OF CONTENTS

Page

DECLARATION i

DEDICATION ii

ACKNOWLEDGEMENT iii

ABSTRACT iv

ABSTRAK vi

TABLE OF CONTENTS viii

LIST OF TABLES xiii

LIST OF FIGURES xiv

LIST OF ABBREVIATIONS xvii

CHAPTER 1 : INTRODUCTION 1

1.1 Motivation of Research 1

1.2 Problem Statement 3

1.3 Objectives 7

1.4 Scope of Research 7

1.5 Chapter Outline 8

CHAPTER 2 : LITERATURE REVIEW 10

2.1 Structural Health Monitoring 10

2.2 Overview of Finite Difference Time Domain (FDTD) Method 14

2.2.1 Determining Cell Size 16

2.2.2 Time Step Size 17

2.2.3 Incident Wave Source 18

2.2.4 Absorbing Boundary Conditions (ABCs) 19

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2.3 Overview of Forward Backward Time Stepping (FBTS) Technique 23

2.4 Overview of Overset Grid Generation (OGG) Method 25

2.4.1 Lorentz Transformation 27

2.4.2 Lorentz Transformation of Electromagnetic Field 29

2.5 Concluding Remark 30

CHAPTER 3 : RESEARCH METHODOLOGY 32

3.1 Introduction 32

3.2 Maxwell’s Equation 32

3.3 Finite Difference Time Domain (FDTD) Method Formulation 35

3.3.1 Two dimensional (2D) FDTD Algorithm 37

3.4 Implementation of Convolutional Perfectly Matched Layer (CMPL)

in FDTD Method 38

3.5 Forward Backward Time Stepping (FBTS) Technique Algorithm 41

3.6 Minimization Approach 43

3.7 Overset Grid Generation (OGG) Method Formulation 45

3.7.1 Interpolation Algorithm and Overset Grid Generation (OGG) Method 46 3.7.2 FDTD Method Combined with Lorentz Transformation by using

Overset Grid Generation (OGG) Method 47

3.7.3 Lorentz Transformation of the 2D-FDTD Method 49

3.8 Computational of the Integration of FBTS technique in OGG-FDTD

Method Algorithm 50

3.9 Concluding Remark 55

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CHAPTER 4 : RESULTS AND DISCUSSION 56

4.1 Introduction 56

4.2 Forward Backward Time Stepping (FBTS) Technique Utilizing Finite

Difference Time Domain (FDTD) Method Only 57

4.2.1 Numerical modelling of FBTS Utilizing FDTD without Any Object 57 4.2.2 Numerical Modelling of FBTS Utilizing FDTD with a 2D Object 60 4.2.2.1 Image Reconstruction for FBTS with FDTD Method of 2D Object 61 4.3 Forward Backward Time Stepping (FBTS) Technique Utilizing

OGG-FDTD Method 64

4.3.1 Numerical Modelling of FBTS Utilizing OGG-FDTD without Any Object 65

4.3.2 Validation of Accuracy by Ratio of Grid Size 68

4.3.3 Numerical Modelling of FBTS with FDTD and OGG Method of a 2D

Object 72

4.3.3.1 Image Reconstruction of FBTS with FDTD and OGG Method of a 2D

Object 74

4.3.4 Comparison between FBTS-FDTD Method and FBTS Utilized

OGG-FDTD Method 76

4.4 Image Reconstruction of FBTS with FDTD and OGG Method:

Single Sub-Mesh as a 2D Object 78

4.4.1 Image Reconstruction for Case A 80

4.4.2 Image Reconstruction for Case B 82

4.4.3 Image Reconstruction for Case C 84

4.4.4 Image Reconstruction for Case D 85

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4.5 Image Reconstruction of FBTS with FDTD and OGG Method for Single

Multi Sub-Meshes as 2D Object 87

4.5.1 Image Reconstruction for Case A 88

4.5.2 Image Reconstruction for Case B 91

4.5.3 Image Reconstruction for Case C 93

4.5.4 Image Reconstruction for Case D 95

4.6 Implementation of FBTS Utilized OGG-FDTD Method in Object and

Crack Detection Application 97

4.6.1 General Setup for Object Detection in SHM Application 97

4.6.1(a) Reconstruction for a Single Concrete Object 98

4.6.1(b) Reconstruction for a Hole and a Concrete Object 102

4.6.1(c) Reconstruction for Two Concrete Objects 105

4.6.2 Numerical Model for Crack Detection 108

4.6.2(a) Image Reconstruction for Crack Detection 109

CHAPTER 5 : CONCLUSION AND RECOMMENDATIONS 113

5.1 Conclusion 113

5.2 Limitations and Recommendations 115

REFERENCES 117

APPENDICES 125

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LIST OF TABLES

Page

Table 4.1 Setup for Material Properties 58

Table 4.2 Measurement Setup of the Numerical Modelling 59 Table 4.3 Measurement Setup of the Numerical Modelling 61

Table 4.5 Error Analysis of Actual Results and Simulated Results for Relative

Permittivity and Conductivity 63

Table 4.6 Measurement Setup of the Numerical Modelling 66

Table 4.9 Measurement Setup of the Numerical Modelling 69 Table 4.10 Number of grids for sub-mesh with various ratio of grid size 70 Table 4.11 Measurement Setup of the Numerical Modelling 73

Permittivity and Conductivity 76

Table 4.14 Comparison of Relative Permittivity between FBTS with FDTD

Method and FBTS with OGG and FDTD Method 78

Table 4.15 Comparison of Conductivity between FBTS with FDTD

Method and FBTS with OGG and FDTD Method 78

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Permittivity Value 101

Permittivity Value 112

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LIST OF FIGURES

Page

Figure 1.1 Overview of the Research Flow 4

Figure 1.2 Aftermath of the Sabah’s Earthquake in June, 5^th 2015 5 Figure 2.1 (a) FDTD Computational Model for the Detection of the Underground

Subsurface Layers and Buried Objects by using GPR

(b) Result of FDTD Simulation for the GPR B-scan Response 12

Figure 2.2 3D of Yee Cell 16

Figure 2.3 Sinusoidal Modulated Gaussian Pulse 19

Figure 2.4 The PML Technique 21

Figure 2.5 Upper-Right of the FDTD Grid 22

Figure 2.6 Types of Grid Distribution Approaches 26

Figure 2.7 Two Inertial Frames with Relative Motion 29

Figure 3.1 Configuration of the Problem in 2D View 42

Figure 3.2 Interpolation Model 47

Figure 3.3 Two Inertial Frames with Relative Motion 48

Figure 3.4 Computational FBTS and OGG with FDTD Algorithm 52 Figure 3.5 Algorithm of Time and Space for FDTD Method and Lorentz

Transformation by Overset Grid Generation Method 53 Figure 4.1 Numerical Model of FBTS Utilizing FDTD without Object 58 Figure 4.2 RxMEAS at Antenna Position Rx2 for Ratio, R=1.010⁻³m 59 Figure 4.3 Numerical Model of FBTS Utilizing FDTD with Simple

Rectangle Object 60

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Figure 4.4 Image Reconstruction of 2D Object of FBTS with FDTD Method 62 Figure 4.5 Numerical Model of FBTS Utilizing OGG-FDTD without an Object 65 Figure 4.6 RxMEAS at Antenna Position Rx2 for Ratio, R=1.010⁻³m 67 Figure 4.7 Comparison of RxMEAS between FBTS Utilized FDTD Method with

FBTS Utilizing OGG-FDTD Method for R=1.010⁻³m 68 Figure 4.8 RxMEAS at Antenna Position Rx2 at for (a) R=0.410⁻³m,

10 3

5 . 0  ⁻

=

R m,R=0.810⁻³m and R=1.410⁻³m 70 Figure 4.9 Ratio of the Grid Size, R versus Absolute Error of the Amplitude

of E_z[V/m] 72

Figure 4.10 Numerical Model of FBTS Utilizing OGG-FDTD in 2D 72 Figure 4.11 Image Reconstruction of 2D Object of FBTS with OGG and FDTD

Method 74

Figure 4.12 Comparison of FBTS with FDTD Method and FBTS with OGG

and FDTD Method 76

Figure 4.13 Image Reconstruction of Case A 80

Figure 4.14 Image Reconstruction of Case B 82

Figure 4.15 Image Reconstruction of Case C 84

Figure 4.16 Image Reconstruction of Case D 86

Figure 4.17 Image Reconstruction of Case A 89

Figure 4.18 Image Reconstruction of Case B 91

Figure 4.19 Image Reconstruction of Case C 93

Figure 4.20 Image Reconstruction of Case D 95

Figure 4.21 Numerical Model of Buried Concrete by using FBTS with FDTD and

OGG Method 99

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Figure 4.22 Image Reconstruction of Case 4.6.1(a) 99

Figure 4.23 Numerical Model of Buried Concrete and a Hole by using FBTS

with FDTD and OGG Method 102

Figure 4.24 Image Reconstruction of Case 4.6.1(b) 103

Figure 4.25 Numerical Model of Two Concrete Blocks by using FBTS with

FDTD and OGG Method 105

Figure 4.26 Image Reconstruction of Case 4.6.1(c) 106

Figure 4.27 Numerical Setup for Crack Modelling 108

Figure 4.28 Image Reconstruction of Case 4.6.2(a) 110

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LIST OF ABBREVIATIONS

2D Two-Dimensional

3D Three-Dimensional

ABC Absorbing Boundary Condition

CFL Courant-Fredrichs-Lewy

CFS Complex Frequency Shifted

CG Conjugate Gradient

CPML Convolutional Perfectly Matched Layer

CT Computed Tomography

EM Electromagnetic

FBTS Forward Backward Time Stepping

FDTD Finite-Difference Time-Domain

GPR Ground Penetrating Radar

LADAR Laser Detection and Ranging

MPI Message Passing Interface

MRI Magnetic Resonance Imaging

NDT Non-Destructive Testing

OGG Overset Grid Generation

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xviii PDE Partial Differential Equation

ROI Region of Interest

SHM Structural Health Monitoring

TE Transverse Electric

TM Transverse Magnetic

TWRI Through-the-wall Imaging

UWB Ultra-Wideband

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1 CHAPTER 1

INTRODUCTION

1.1 Motivation of Research

Object detection and recognition is a significant task in image processing and computer vision. It is used to identity of an object that obtained in an image regardless of the object’s location, shapes and sorts of the material. This kind of technology allowed humans to know any kind of object in the real world simply without any efforts. However, the computerize recognition of object in image is not an easy task. During the past few decades, objects and crack detection have gained attention among the researchers and has application in many areas. It has been widely used in military and criminology, archaeology, medical imaging, geophysical exploration and structural health monitoring [1–6].

In military and criminology area, the object detection application is very important for the Military and Law Enforcement [3, 7]. One of the techniques that always been used in military applications is through-the-wall radar imaging (TWRI) [8]. Many researchers show interest in TWRI with many kinds of significant civilians and military application.

TWRI technology is implemented for surveillance and humans’ detection and interior objects in urban environments. Moreover, it is also used for search and rescue operations in military situations. In TWRI technology, ultra-wide band (UWB) radar and compact Doppler radar show the potential for real time imaging, and giving non-invasive detection and observing the cardiopulmonary activity for several subjects.

In medical imaging, the object detection application is commonly used in cancer or tumour detection [6, 9–11]. The early detection and diagnosis of cancer is important as it can increase the chances of successful treatment. Besides, treatments for early cancer are

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often less complex and cheaper than treatments for more-advanced disease, sparing patients and families’ greater hardship. There are various types of imaging for cancer’s detection and monitor the cancer’s condition and its spread. The first common method is computed tomography (CT) scan, which produces cross-sectional imaging by computer [12]. CT scan is using x-rays and a computer to produce detailed images of the inside of human body. It generates image from different angles. Then, the computer will put the images together to make a 3 dimensional (3D) image [12]. Secondly, magnetic resonance imaging (MRI) is a non-invasive and not a painful technique. A strong magnetic field and radio waves used in MRI so that it can provide thorough images of the organs and tissues in the body [13, 14].

Besides, the object detection is also widely applied in structural health monitoring application especially in the civil structure to detect the buried object and also cracks [15–

17]. The structural health monitoring (SHM) is a method that needs to be applied in the detection of the damages. The infrastructures, including bridges, lands and buildings, begin to weaken once they are built and used for a long time. It is very important for preserving safety and reliability of the civil infrastructures for daily use especially to public. In addition, the health monitoring should also be done for the infrastructures that have been affected by the nature disaster such as tornadoes, earthquakes and hurricanes. These scenario can cause some of the collapsed buildings are buried underground during the disaster happened. The buried concrete can result the structural failures and collapses in the future [5, 16, 18].

The damage of the building and civil structures can be identified at an early stage to avoid any failures that will result in a devastating fatality [18, 19]. There are few methods that have been used to detect the buried object and cracks. One of the methods is by using

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ground penetrating radar (GPR) tomography [20]. The GPR tomography is a method with high resolution ratio, real-time display and flexible to apply for detecting the buried object.

The second method is by using non-destructive microwave radar to inspect buried object in concrete structures [18]. In this research, the microwave inverse scattering technique;

Forward Backward Time Stepping (FBTS) technique is proposed with the integration of Overset Grid Generation (OGG) method and Finite Difference Time Domain (FDTD) method into the new proposed algorithm. The proposed method will be used for the object and crack detection in structural health monitoring (SHM) in civil application especially for post-hazard assessment after the natural disaster.

1.2 Problem Statement

Figure 1.1 illustrates the overview of this whole research about. Structural health monitoring (SHM) is the process of applying a damage detection and characterization strategy for engineering structures such as bridges and buildings. SHM is frequently used to detect damages at early stage. SHM can be regarded as a kind of natural hazard management. Therefore, SHM will be applied in this research to detect the extent of damages and hazards after-effect the Earthquake disaster. Earthquake is an unexpected trembling movement that happened at the earth’s surface. It is also referred as a quake, trembler or tremor. There are some common effects of earthquakes such as infrastructural damage (building, roads, bridges), landslides, liquefaction, and tsunami [23, 24].

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Develop the new algorithm; Overset Grid Generation (OGG) method in Forward Backward Time Stepping (FBTS) technique utilizes Finite Difference Time Domain (FDTD) method for image reconstruction of object and crack detection

Apply the proposed method in SHM application for image reconstruction of object and crack detection

End

Figure 1.1: Overview of the Research Flow Start

Post hazard assessment for after-effect Earthquake

Implement Structural Health Monitoring (SHM) for the early damage detection

Validation of accuracy of the proposed method