석 사 학 위 논 문

Master Thesis

구조물 건선성 모니터링을 위한 가속도계와

RTK-GPS 센서를 융합한 6자유도 동적 응답

계측 시스템 개발

Development of a 6-DOF Dynamic Responses Measurement System Fusing RTK-GPS Sensor and Accelerometer for

Structural Health Monitoring

2017

구 건 희 (具 建 喜 Koo, Gun Hee)

한 국 과 학 기 술 원

Korea Advanced Institute of Science and Technology

석 사 학 위 논 문

구조물 건선성 모니터링을 위한 가속도계와

RTK-GPS 센서를 융합한 6자유도 동적 응답

계측 시스템 개발

2017

구 건 희

한 국 과 학 기 술 원

건설 및 환경공학과

구조물 건전성 모니터링을 위한 가속도계와

RTK-GPS 센서를 융합한 6자유도 동적 응답

계측 시스템 개발

구 건 희

위 논문은 한국과학기술원 석사학위논문으로

학위논문 심사위원회의 심사를 통과하였음

2017년 06월 20일

심사위원장 손 훈 (인 )

심 사 위 원 김 아 영 (인 )

심 사 위 원 공 승 현 (인 )

Development of a 6-DOF Dynamic Responses Measurement System Fusing RTK-GPS Sensor and

Accelerometer for Structural Health Monitoring

Gunhee Koo

Advisor: Hoon Sohn

A dissertation/thesis submitted to the faculty of

Korea Advanced Institute of Science and Technology in

partial fulfillment of the requirements for the degree of

Master of Philosophy in Civil and Environmental Engineering

Daejeon, Korea

June 20, 2017

Approved by

Hoon Sohn

Professor of Civil and Environmental Engineering

The study was conducted in accordance with Code of Research Ethics1).

1) Declaration of Ethical Conduct in Research: I, as a graduate student of Korea Advanced Institute of Science

and Technology, hereby declare that I have not committed any act that may damage the credibility of my research.

This includes, but is not limited to, falsification, thesis written by someone else, distortion of research findings, and

plagiarism. I confirm that my dissertation contains honest conclusions based on my own careful research under the

guidance of my advisor.

초 록

본 연구에서는 구조물 건전성 모니터링을 위한 가속도계와 RTK-GPS 센서를 융합한 6자유도 동적

응답 계측 시스템을 소개한다. 모니터링 시스템은 센서모듈과 GPS 기지점 모듈과 컴퓨터 모듈로

구성된다. 센서 모듈은 측정 대상 지점에 부착되어 가속도(100Hz), 속도(10Hz), 변위(10Hz)를

계측하고 컴퓨터 모듈로 전송하며, GPS 기지점 모듈은 고정 지지점에 부착되어 센서모듈의 계측을

보조한다. 컴퓨터 모듈은 모든 계측 데이터를 모아서 신뢰도 평가 필터, IIR필터, 각변위 계산

함수, 이단계 칼만필터으로 구성된 데이터 처리 기법으로 처리하여, 정밀하고 정확한 6자유도

동적 응답을 산출한다. 계측 성능 검증을 위해 1Hz 사인 진동 실험, DC-10Hz의 랜덤 진동 실험,

회전 실험을 수행했으며, 현장 적용성 검증을 위해 말레이시아 페낭 2교에서 상용 RKT-GNSS

센서와 비교 검증 실험을 수행했다.

핵 심 낱 말 가속도계, RTK-GPS 센서, 구조물 건전성 모니터링, 동적 응답, 칼만필터

Abstract

This thesis proposes a 6-DOF dynamic response measurement system incorporating a low-cost RTK-GPS sensor

and a force feedback accelerometer for structural health monitoring. The proposed system consists of (1) a sensor

module integrating a RTK-GPS sensor and a force feedback accelerometer into a single unit, (2) GPS base

module for transmitting observation messages to sensor modules, and (3) computational module for estimating 6

DOF structural responses in real-time. The sensor module measures acceleration, velocity and displacement of

target structure with sampling rates of 100 Hz, 10Hz, and 10Hz, respectively, and transmits the measurements to

the computational module. While sensor module measures, GPS base module transmits observation messages to

sensor module for guaranteeing RTK technique application at sensor module. Computation module processes the

measurement data from sensor module to estimate the precise and accurate 6-DOF dynamic responses using a

combination of data process techniques such as a reliability assessment filter, IIR filter, inclination function, and

a two-stage Kalman estimator. For verifying measurement performance, a 1Hz sinusoidal vibration test, DC to

10Hz random vibration test, and a rotation test were implemented. Additionally, for verifying field applicability,

a field test at Penang Second Bridge, Malaysia was implemented by comparing the proposed system with a

commercial RTK-GNSS sensor.

Keywords Accelerometer, RTK-GPS sensor, Structural health monitoring, Dynamic responses, Kalman filter

MCE

20154323

구건희. 구조물 건전성 모니터링을 위한 가속도계와 RTK-GPS 센

서를 융합한 6자유도 동적 응답 계측 시스템 개발. 건설 및 환

경공학과. 2017년. 46쪽. 지도교수: 손훈. (영문 논문)

Gunhee Koo. Development of a 6-DOF Dynamic Responses

Measurement System Fusing RTK-GPS Sensor and Accelerometer for

Structural Health Monitoring. Department of Civil and Environmental

Engineering. 2017. 46 pages. Advisor: Hoon Sohn. (Text in English)

6

Contents

Contents ........................................................................................................................................................................... 6 List of Figure and Tables ................................................................................................................................................. 7

Chapter 1. Introduction .................................................................................................................................................... 8

1.1. Motivation ................................................................................................................................................ 8

1.2. Literature Review ..................................................................................................................................... 9

1.3. Objective, Uniqueness, and Thesis Organization .................................................................................... 12

Chapter 2. Theoretical Background ................................................................................................................................ 14

2.1. IIR Filter ................................................................................................................................................. 14

2.2. Kalman Filter for displacement estimation ............................................................................................. 17

Chapter 3. Development of 6-DOF Dynamic Responses Measurement System ............................................................ 22

3.1 Sensor Module ......................................................................................................................................... 22

3.1.1. Force Feedback Accelerometer ......................................................................................... 23

3.1.2. ADC Board ........................................................................................................................ 24

3.1.3. GPS Rover .......................................................................................................................... 25

3.1.4. MCU Board ......................................................................................................................... 27

3.2 GPS Base Module .................................................................................................................................... 28

3.3 Computation Module ............................................................................................................................... 29

3.3.1. Reliability Assessment Algorithm ....................................................................................... 30

3.3.2. IIR High-pass Filter and IIR Low-pass Filter ..................................................................... 31

3.3.3. Inclination Function ............................................................................................................ 32

3.3.4. Kalman Filter ...................................................................................................................... 33

Chapter 4. Experimental Validation ............................................................................................................................... 36

4.1. Sinusoidal Vibration Test ..................................................................................................................... 36

4.2. Random Vibration Test ......................................................................................................................... 39

4.3. Rotation Test ........................................................................................................................................... 41

4.4. Penang Second Bridge Test .................................................................................................................... 41

Chapter 5. Conclusion .................................................................................................................................................... 44

Bibliography ................................................................................................................................................................... 45

Acknowledgments in Korean ......................................................................................................................................... 46

Curriculum Vitae ............................................................................................................................................................ 47

7

List of Figure and Tables

Figure 1.1. Examples of bridge inspection target ................................................................................................... 8

Figure 1.2. LVDT application to bridge displacement measurement ................................................................. 10

Figure 1.3. GNSS application to bridge displacement measurement .................................................................. 11

Figure 1.4. Development of 6-DOF dynamic responses measurement system .................................................... 12

Figure 2.1. Configuration of IIR filter ................................................................................................................ 14

Figure 2.2. Amplitude comparison of high-pass filter using 3 orders ................................................................. 15

Figure 2.3. Phase comparison of high-pass filter using 3 orders ........................................................................ 16

Figure 2.4. Pole point comparison of high-pass filter using 3 orders ................................................................. 17

Figure 2.5. Summary of Kalman filter for displacement estimation ................................................................... 21

Figure 3.1. Bridge application of 6-DOF dynamic responses measurement system .......................................... 22

Figure 3.2. Configuration of sensor module ......................................................................................................... 23

Figure 3.3. Principle of force feedback accelerometer operation ....................................................................... 24

Figure 3.4. Primary components in ADC board ................................................................................................. 25

Figure 3.5. Operation process of power supply to ADC and force feedback accelerometer .............................. 25

Figure 3.6. Primary components of MCU board ................................................................................................ 27

Figure 3.7. Operation process for time synchronization and data communication ............................................. 28

Figure 3.8. Antenna of GPS base module ........................................................................................................... 29

Figure 3.9. Observation message broadcasting from GPS base module ............................................................. 29

Figure 3.10. Example of computation module .................................................................................................... 30

Figure 3.11. Summary of data processing for estimating the precise 6-DOF dynamic responses ...................... 30

Figure 3.12. Response of IIR high-pass filter ....................................................................................................... 31

Figure 3.13. Description of inclination function ................................................................................................... 33

Figure 3.14. State-space model of stage 1 Kalman filter in a multi-rate two-stage Kalman filter ........................ 33

Figure 3.15. State-space model of stage 2 Kalman filter in a multi-rate two-satge Kalman filter ...................... 34

Figure 3.16. Summary of multi-rate two-stage Kalman filter ............................................................................... 35

Figure 4.1. Schematic configuration of vibration test ........................................................................................... 36

Figure 4.2. Installation of 6-DOF dynamic responses measurement system for vibration test ............................. 37

Figure 4.3. Precision comparison of the proposed system and LDS for sinusoidal vibration .............................. 37

Figure 4.4. 1Hz sinusoidal vibration by accelerometer and RTK-GPS and by proposed system ......................... 38

Figure 4.5. Z-axis random vibration by accelerometer and RTK-GPS and by proposed system ......................... 40

Figure 4.6. Precision comparison of the proposed system and LDS for random vibration .................................. 40

Figure 4.7. Precision comparison of the proposed system and the rotary encoder for 0.24Hz rotation ................ 41

Figure 4.8. Field application test .......................................................................................................................... 42

Figure 4.9. Comparison of the bridge responses measured by proposed system and by RTK-GNSS sensor ....... 43

Table 1. RMSE and noise reduction results of dynamic responses in X and Y axis ............................................ 39

8

Chapter 1. Introduction

1.1. Motivation

For structural health monitoring of civil structure, the measurement of dynamic responses is important,

since it results in the numerical data for deciding whether the target civil structure is safe or dangerous. Usually,

dynamic properties of civil structure are derived from the response measurements, and the change of dynamic

properties could be considered as change of the structural state . Especially bridge is one of the persnickety civil

structures, and there are some examples for inspecting structural state using response measurement data. Bridge

expansion joints which connects a series of decks are relatively vulnerable is required to be inspected, and the

inspection could be implemented using measured displacement and temperature data (Figure 1.1.a) [2]. For

cable-stayed bridges, the cables which connects a main foundation tower and a middle deck is also required to

be analyzed its dynamic behavior, so the analysis could be implemented using acceleration measurement (Figure

1.1.b) [3, 4].

(a) Bridge expension joint (b) accelerometer attached bridge cable

Figure 1.1. Examples of bridge inspection target

Among the dynamic responses including acceleration, velocity, and displacement, displacement is the

most useful physical parameter. That is because it could be converted acceleration and velocity using simple

differentiation. Different from integral calculation, differentiation is free from an unknown initial value problem.

For example, when displacement is calculated from acceleration, there are the two unidentified initial values

which are initial velocity and initial displacement. Inadequate assumption of those initial values would generate

the unreliable displacement results. However, when displacement is given, and both acceleration and velocity

need to be derived from displacement, the reliable results could be acquired without initial velocity and

acceleration values. Moreover, displacement is a direct criterion for judging the current bridge state. By

deciding the limit allowable displacement of structure, the target structure could be not only designed but also

monitored [5].

9

1.2. Literature Review

As efforts to measuring the precise and accurate displacement of civil structures, different types of

sensors are developed. Linear variable differential transformer (LVDT) is, one of preferred traditional

displacement sensors, produces an alternating current (AC) voltage as an output signal which represents the

amount of displacement by using electromagnetic induction. The displacement measured by output voltage of

electromagnetic induction between solenoid coils and a ferrous core shows a great performance with a

theoretically infinite accuracy and a strong linearity as around 0.5%, and a wide measurement range as around

50 mm. In spite of the great performance of LVDT, the installation requirement of LVDT restricts the

application in civil structures. Since the head of LVDT needs to be connected to target structure and the other tip

should be fixed to a rigid ground support like as figure 1.2, the sensor application to the challenging structures

including bridges crossing waterways and skyscrapers is significantly constrained [6].

As a state of the art noncontact laser sensor, laser Doppler vibrometer (LDV) shows a great reliability

in measuring displacement with high resolution as 0.38 nm, wide bandwidth as up to 2,000 kHz and maximum

distance as 300 m. LDV applies the principle of Doppler effect in displacement measurement using sinusoidal

laser beams. First, sinusoidal laser beam is separated into incident laser and reference laser, and the launched

incident laser from LDV to a moving target surface is reflected back into LDV. When the reflection of laser

beam on the moving target surface happens, the frequency change which is proportional to the speed of moving

target toward the laser path occurs in reflected laser beam. By comparing the frequencies of reflected laser beam

and reference laser beam, the amount of phase shift can be calculated, so that highly reliable displacement which

is proportional to the amount of phase shift could be calculated. With this measurement performance and

properties, LDV could be an alternative of LVDT and geophone for bridge dynamic tests and structural health

monitoring [7]. However, the high cost of LDV hinders the multiple node measurement application to large civil

structures. Additionally the installation prerequisite of LDV that the sensor head should be supported by the

rigid ground where any vibration could not happen and the incident laser could reach the target obstructs the

sensor application to water way crossing structures and skyscrapers.

With a development of camera technologies and image processing techniques, displacement

measurement using vision sensor has been conducted in order to monitor civil structures [8, 9]. Single vision

sensor head located at the reference place measures the multiple targets where each recognition panel is attached

on the multiple target points on structures. From the acquired images, every pixel of images are extracted and

processed to estimate displacement from the pixel coordinate variation. With this measurement scheme, the

highly cost effectiveness is achieved, because a single sensor can cover a wide range of point measurements and

the cost of single sensor is also low compared to that of other

displacement sensor such as laser displacement sensors, LDV, and etc. Despite the benefits of vision

displacement sensor, there are some limitations for sensor application to large structures. First, the displacement

measurement resolution depends on the distance between target structures and vision sensor head, because the

pixel resolution of target panels decreases if the measurement distance increases. Also, similar to the installation

10

of LDV, vision displacement sensors are also required to be supported by a rigid ground which is a vicinity of

target structure, so that the types of applicable civil structures are limited.

(a) Scaffold under bridge for LVDT installation (b) Installed LVDT

Figure 1.2. LVDT application to bridge displacement measurement

Real-time kinematic global navigation satellite system (RTK-GNSS) sensors use a method of carrier phase

differential positioning, and the RTK-GNSS is composed of GNSS rover and GNSS base. GNSS base which is

installed on the reference ground, a vicinity of target structure, measures the zero response of reference ground

and transmits the measurement to GNSS rover at the same time. GNSS rover which is installed on the

measurement target point of structure receives the GNSS base measurement and measures the accurate and

precise relative displacement using carrier phase differential positioning technique. Assuming that both errors in

GNSS rover and GNSS base measurements are significantly similar, GNSS rover measurement error could be

eliminated by subtracting GNSS base measurement from GNSS rover measurement. Simultaneously, the

estimation of the number of carrier cycles is implemented, so that RTK-GNSS sensor shows the fine

performance in measuring displacement as centimeter level of accuracy. The ease installation of RTK-GNSS

sensor as a contact type sensor and the wide range of measurement coverage using multiple GNSS rovers and a

GNSS base have led to a variety of application examples of civil structures such as waterway crossing bridges,

dams, and skyscrapers. Additionally, no error accumulation property of RTK-GNSS sensor allows the RTK-

GNSS sensor to be applied for long term displacement monitoring of civil structures (Figure 1.3).

However, the low sampling rate under 20 Hz of RTK-GNSS sensors limits the wide spectrum analysis of

structural vibration. Cycle slip error of RTK-GNSS sensor due to multi path of satellite signals, low elevation of

satellite and sparsely low signal noise ratio interrupts the stable displacement measurement in civil structure

applications. In addition to cycle slip error, the sparse inaccuracy in estimating carrier cycle numbers results in

unreliable displacement measurement, which is called “float mode” of RTK-GNSS sensor measurement. So

only when the “fixed mode” which means the estimation of carrier cycle is in accurate state is available, the

11

displacement measurement can be used with high reliability. Recently, for a broad-range monitoring application

of large civil structures, RTK-GNSS sensors has been applied for structure inspection and monitoring [10-12].

(a) GNSS on Yeongjong Grand Bridge (b) GNSS on Jiangying Bridge

Figure 1.3. GNSS application to bridge displacement measurement

Contrary to the mentioned direct displacement measurement sensors including LVDT, LDV, vision

displacement sensor, and RTK-GNSS sensor, the indirect displacement measurement techniques using

accelerometers allow a solution which overcomes the dependency on a zero-response reference ground when

measuring displacement of long span bridge and high-rise building. As a contact type displacement sensor,

accelerometer can be used for estimating displacement from measured acceleration with the convergence of

double integration method. Compared to installation of other displacement sensors, the installation of

accelerometer to civil structures is simple, because what accelerometer requires is to be attached at the target

point without any rigid supports. Several integration methods which support accelerometers to estimate

displacement have been reported and showed some reliable performance results [13, 14]. With an assumption

that the average of velocity would be zero, a double integration technique with initial velocity estimation was

proposed and it showed reliable results with field tests [13]. However, if the bias in acceleration is not stable due

to temperature or other environmental effects, the double integration technique proposed by Park et al. (2005)

could not estimate the optimal initial velocity, and the estimation result could not incorporate the high accuracy

and precision. As an alternative method, a state-space modeling technique proposed by Gindy et al. (2008)

implemented the low frequency noise removal before any integration of acceleration, so that the precision and

accuracy increased [14]. However, the state-space modeling technique requires the whole measured acceleration

to build model, so it restricts the real time application of technique in monitoring civil structures.

In order to increase the reliability of displacement measurement of civil structures, data fusion

techniques have been developed using Kalman filter theory. Smyth and Wu (2007) have proposed a multi-rate

Kalman estimator using high-sampled acceleration and low-sampled displacement [15]. The technique allows

the fusion of displacement sensor and accelerometer, and result in the much reliable estimated displacement.

During the estimation, the technique also incorporates an additional smoothing technique. The necessity of

smoothing technique for eliminating low frequency error hinders the real-time estimation, so that the technique

12

could not be optimal to long term real-time monitoring civil structures. Kim et al (2014) proposed a precision-

enhanced multi-rate Kalman estimator using acceleration and displacement, considering the integration error

evolution model in system dynamics of Kalman estimator [16]. The technique also use a displacement sensor

and accelerometer for raw data acquisition. By adopting acceleration bias in system model, the precision of

estimated displacement could increase when compared to the data fusion technique proposed by Smyth and Wu

(2007) [15]. A two-stage Kalman filter proposed by Kim et al (2016) where stage 1 is for estimating bias-

affected displacement and stage 2 is for estimating displacement error due to bias in acceleration measurement

shows the great performance in Kalman gain convergence rate and discontinuity reduction compared to the

techniques proposed by both Smyth and Wu (2007) and Kim et al (2014) [15-17].

1.3. Objective, Uniqueness and Thesis Organization

This study aims to propose a novel 6-DOF dynamic responses measurement system (Figure 4). The

following details from next chapter would be described in order to verify the performance of 6-DOF dynamic

responses measurement system. The applicability verification of the proposed measurement system to existing

civil structure is also aimed in this study.

Figure 1.4. Development of 6-DOF dynamic responses measurement system

The 6-DOF dynamic responses measurement system contains three uniqueness when compared to a

commercial RTK-GNSS sensor for civil structure application. The first uniqueness is a price competition. By

adopting a low cost RTK-GPS sensor and a force feedback accelerometer at the proposed measurement system,

a single sensor module in the proposed system could be manufactured by consuming about 10,000 USD.

Considering the commercial RTK-GPS sensor whose precision is almost 20mm costs about 30,000 USD, the

cost of sensor module in the proposed measurement system is only 33% of that of commercial RTK-GPS sensor.

The second uniqueness is high accuracy and high sensitivity. The accuracy of the proposed measurement system

13

is about 3mm, so even a small vibration could be detected and measured. This is also an advantage, when

compared to a commercial RTK-GNSS sensor measurement performance. The third uniqueness is high

sampling rate. In the proposed system, the final estimated responses are sampled with 100Hz sampling rate. On

the other hand the sampling rate of commercial RTK-GNSS sensor is limited under 20Hz. By using the

proposed measurement system, structural vibration analysis in broad band could be utilized.

The following chapters are composed of four chapters. In chapter 2, the theoretical backgrounds such

as IIR filter, and Kalman filter are described. In chapter 3, a 6-DOF dynamic responses measurement system is

described. The chapter 4 describes the experimental validation results. Finally the thesis is concluded in chapter

5.

14

Chapter 2. Theoretical Background

Filters are used for eliminating the undesired signal components which incorporate the unnecessary

frequency components different from the frequency of desired signal; however, the complete undesired signal

elimination using realistic filtering strategies including a finite impulse response (FIR) filtering and an infinite

impulse response (IIR) filtering is impossible in real situations. So the conditions and parameters of filters need

to be determined optimally, considering the output performance after filtering. The output signal after filtering

shows 1) Amplitude distortion, and 2) Phase distortion, when compared to desired signal. Since these distortions

are inevitable, the optimal trade-off between the amounts of distortions is demanded in filter design. In the

proposed 6-DOF dynamic responses measurement system, an IIR filter for low frequency noise reduction and a

data fusion technique based on a Kalman filter are adopted in order to estimate the precise and reliable dynamic

responses of civil structures. So the basic properties of IIR filter and Kalman filter would be discussed at next

subchapters.

2.1. IIR Filter

By adopting both input signals and filtered output signals, IIR filter could be established as figure 2.1.

First, only the input signals are filtered by , and result in the output signals, y . As next step the output

signals are fed back into , and are filtered by together with the next input signal. IIR filter is

composed of both and , different from FIR filter which is composed of only by , so that IIR

could be recursive filter and incorporate the different properties from those of FIR filter.

Figure 2.1. Configuration of IIR filter

IIR filter which relates the filtered output signal to input signals and the past filtered output signals is

also described in the difference equation form as

y n y n x n (2.1)

15

where is a coefficient for the past filtered output signal, and is a coefficient for the input signal. For

computation of the filtered signal using N+1 number of input signals, totally N+M+1 number of signals

including both input and output signals need to be processed, and this results in more computational cost than

that of FIR filter using the equal N number of input signals.

(a) IIR high-pass filter (b) FIR high-pass filter

Figure 2.2. Amplitude comparison of high-pass filters using 3 orders

It seems that IIR filter spends higher computation costs; however, IIR filter could generate the filtered

output only allowing a little amplitude distortion in frequency domain. When the similar performance of IIR

filter is required using FIR filter, FIR filter would require more input variables and coefficients than those of IIR

filter. This property of IIR filter results in low computation cost, satisfying the filtering requirements decided by

filter designers. The high performance of IIR filter in amplitude is causes by its recursive equation form. By

adopting recursive equation form, all the past input signals from initial state could be reflected in filtering

process. Figure 2.2 shows the high-pass filter response graphs from IIR filter and FIR filter, respectively only

using 3 orders. The cut-off frequency was designed as 1Hz, so that the ideally desired filter should show the

magnitude as 1 after 1Hz and 0 before 1Hz. Then, IIR could be designed as a filter similar to the ideally desired

filter. For the similar performance of ideally desired filter using FIR filter, 278 orders are required for filter

design, which demands high computation costs.

In the aspect of phase distortion, IIR filter reveals a nonlinear property, so the amount of phase shifts

of each frequency components could not be identical. If the input signal contains a variety of frequency

components, then the output signal would be contaminated, so the filtered output signal is far from the desired

output signal. On the other hand, FIR filter always shows a linear property in phase distortion, so the output

signal is only shown as a time-delayed version of desired output signal. The phase distortion properties from

both IIR filter and FIR filter could be found at the examples of 3 order high-pass filter (Figure 2.3). In figure 2.3,

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Frequency (Hz)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ma

gn

itud

e

0 10 20 30 40 50Frequency (Hz)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Mag

nitu

de

16

at IIR filter case, the phase response in frequency domain is nonlinear, and the linear trend changes into

nonlinear trend when the interest frequency domain is large. But the large frequency domain could not

deteriorate the linear phase response property of FIR filter. So it is recommended that the minimization of

nonlinear phase distortion in the interested frequency domain need to be reflected for optimal IIR filter

performance. Additionally, the nonlinear phase response property in IIR filter could be reduced when the target

frequency domain is narrow. In figure 2.3 (a), if only the interested frequency domain is narrowed to from 15Hz

to 40Hz, then the phase distortion property could be close to linear property.

(a) IIR high-pass filter (b) FIR high-pass filter

Figure 2.3. Phase comparison of high-pass filters using 3 orders

In stability aspect, IIR filter incorporates a risk to generate the output signal to be dispersive. This

probability results from the recursive form of an IIR filter equation. The equation 2.1 could be transformed in z

domain as

Y z z Y z z X z (2.2)

where z is a complex frequency domain.

0 10 20 30 40Frequency (Hz)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Ph

ase

(ra

d)

0 10 20 30 40Frequency (Hz)

-6

-5

-4

-3

-2

Pha

se (

rad)

17

(a) IIR high-pass filter (b) FIR high-pass filter

Figure 2.4. Pole point comparison of high-pass filters using 3 orders

Then the transfer function is found as

H zY zX z

∑ z

1 ∑ z (2.3)

In the transfer function, equation 2.3, the denominator equation could be zero, so the filtered output

signal could gradually diverge. The complex z value which results in zero at the denominator equation is called

pole point, and the position of pole point in z plane is a determinant parameter in distinguishing between stable

IIR filter and unstable IIR filter. In z plane, the unit circle whose radius is 1 could be a boundary for the position

of pole point. Only when the pole point is located inside the unit circle, the IIR filter could be stable. So in IIR

filter design, the position of pole points should be considered for compensating the stability of filter. When it

comes to the example of high-pass filter with 1Hz cut-off frequency and 3 order, the pole point and zero point

which results in the zero at numerator could be plotted in z plane as figure 2.4, and pole. The example IIR filter

could be decided as a stable filter, because all the three pole points are inside the unit circle. As the example FIR

filter, pole points does not exist in FIR filters, so that stability consideration is not required in FIR filter design.

2.2. Kalman Filter for Displacement Estimation

Kalman filter is a recursive filter which estimates the state of a linear dynamic system with noise using

measurements. The state space model should be determined for implementing Kalman filter, and the state space

model is composed of transition equation and observation equation.

For estimating displacement, the transition equations are usually established based on the integral

process using acceleration and velocity [15-17]. The integral process is defined by a truncated Taylor series or a

Taylor theorem. In Taylor theorem, the differentiable function with n orders could be defined as

-1 -0.5 0 0.5 1Real

-1

-0.5

0

0.5

1

Imag

inar

y

3

-1.5 -1 -0.5 0 0.5 1Real

-1

-0.5

0

0.5

1

Imag

inar

y

3

18

∆ ∆2!

∆ ⋯!

∆ (2.4)

where ∆ is a small amount of time increase. If the ∆ is very small, then the high order terms in equation 2.4

would be almost zero. So the truncated form of the Taylor series could be expressed as

∆ ∆2!

∆ (2.5)

In equation 2.5, could be assumed as displacement, could be assumed as velocity, could be

assumed as acceleration. So the equation 2.5 reveals the relation among displacement, velocity, and acceleration.

The fundamental form of transition equation for estimating displacement is now determined from the

truncated form of Taylor series using a defined state vector, t .

∆1 ∆

12∆

0 1 ∆0 0 1

(2.6)

where is a Gaussian white noise vector which represents the uncertainty of transition equation. Note that

the bold character is vector or matrix form. The equation 2.6 shows the linear relation between x and

x ∆ , and this form could be modified by defining different state vectors like Smyth and Wu (2007), and

Kim et al(2016) [15, 17]. For example, if the acceleration measurement is incorporated in transition equation

with an assumption that is where is a white noise from acceleration

measurement and is a bias in acceleration measurement, then the transition equation with a state vector

t would be modified as equation 2.7. For convenient expression in discrete system,

the next state vector x( ∆ ) is expressed as x( 1), and ∆ in matrix A is sampling period.

1

where, 1 ∆ ∆

0 1 ∆0 0 1

, ∆

∆1

, ~ 0,

(2.7)

The observation equation is derived from a relation between measurement and state vector. For

example, if the displacement measurement is incorporated in observation equation, then the observation

equation would be determined as

where, C 1 0 0 , ~ 0, (2.8)

19

where is a measurement vector, C is a coefficient vector, and is a noise vector from measured

displacement. According to the measured physical quantity, the dimension of and changes, and the

factor of coefficient vector C also changes.

With a state space model using equation 2.7 and 2.8, prior prediction step could be implemented,

minimizing the estimation error. At first, denoting a state vector at prior prediction step as which results

from the assumption that the best estimate of is mean of . So the transition equation is expected in a

probabilistic manner as

1 (2.9)

where is a state vector of the prior prediction step and is a state vector of the posterior correction

step. Then the estimation error in prior prediction step, , is

1 1 1 w(t) (2.10)

where 1 is true state vector. This estimation error could not be solved directly since w(t) is unknown. So

the covariance of the error instead of error itself is utilized. Covariance is just a matrix expression of the

variance of error as equation 2.11. Thus the equation (2.9) and (2.11) would be the process of prior prediction

step.

1 1 1

where

(2.11)

In posterior correction step, the state vector is derived from a weighted sum equation of prior

state vector and measurement vector in observation equation like as (Equation 2.12).

1 ′ 1 1 1 1 (2.12)

So determination of both ′ 1 and 1 is core process in posterior correction step. Both could be

considered two unknown variables, so what are required for deriving the both matrixes the two equations. The

expected error could be considered as zero, so that 1 0 is used as the first equation. And the

second equation is that ‖ 1 ‖ should be minimized. From the equation, 1 0 , the

estimation error in posterior correction step is expressed as equation 2.13.

1 1 1 (2.13)

20

1 ′ 1 1 1 1

1 1 1 1 1

And the expectation of error is expressed as

1 1 1 1 0

∴ 1 1

(2.14)

where I is an 3 3 identity matrix. In equation 2.14, 1 is expressed the term of 1 .

Additionally, the estimation error in posterior correction step could be expressed as not only equation 2.13 but

also equation 2.15.

1 1 1 1 1 (2.15)

The error covariance 1 could be expressed simply using equation 2.15 as

1 1 1 1

1 1

(2.13)

Considering the 1 which minimizes ‖ 1 ‖ , 1 could be calculated using

0, so the final 1 is derived as

1 1 1 (2.13)

where is variance of white noise from displacement measurement.

The Kalman filter for estimating displacement is repetitively implemented using prior prediction step

and posterior correction step. The fundamental state-space model and procedures of Kalman filter are

summarized in figure 2.5 (a) and figure 2.5 (b), respectively.

21

(a) State-space model (b) Procedure of Kalman filter

Figure 2.5. Summary of Kalman filter for displacement estimation

22

Chapter 3. Development of 6-DOF Dynamic Responses Measurement System

A 6-DOF dynamic responses monitoring system is composed of sensor module, GPS base module,

and computation module (Figure 3.1). Each component could be easily connected to other components by

adopting additional communication hubs such as switching hub and Ethernet converter. Due to the flexible

network configuration of the proposed system, the proposed system includes a capacity to expand the

monitoring coverage by using multiple sensor modules in a monitoring system. Sensor module measures the

dynamic responses of target location including acceleration with 100Hz sampling rate, velocity with 10Hz

sampling rate, and displacement with 10Hz sampling rate. GPS base module installed at reference location

measures the zero-response and sends the observation message to sensor modules continuously. All the

measurements including 100Hz sampled acceleration, 10Hz sampled velocity and 10Hz sampled displacement

are transmitted to computation module using a user datagram protocol (UDP) communication, so that

computation module estimates the precise 6-DOF dynamic responses for each sensor module location in real

time. The computation module also provides the visual functions for monitoring estimation results in real time.

Sensor module

Computation module

Communication hub

GPS base module

Communication hubBackbone network

UDP communication

UDP communication

Sensor module

Sensor module

Figure 3.1. Bridge application of 6-DOF dynamic responses measurement system

3.1. Sensor Module

Sensor module measures 3-axis dynamic responses of target location including acceleration, velocity,

and displacement, synchronizes, and transmits the measurement data for estimating the precise 6-DOF dynamic

responses. It is composed of GPS rover (GPS antenna and GPS receiver), force-feedback accelerometer (3-axis

accelerometer and accelerometer control board), MCU board, ADC board (Figure 3.2). GPS rover is in charge

of measuring velocity and displacement with 10Hz sampling rate, and force-feedback accelerometer is in charge

of measuring acceleration with 100Hz sampling rate with assistance of ACD board. MCU board collects the

measurement data from both GPS rover and force-feedback accelerometer in order to synchronize the

measurement on the base of GPS time, and transmits the synchronized measurements to the computation

23

module. It is necessary to input +5V as external power in order to guarantee the normal operation of a sensor

module which consumes about 3W electrical power. Following descriptions are about the constitution and

operation of sensor module components.

MC

U b

oar

d

AD

C b

oar

d

Acc

ele

rom

ete

r C

ontr

ol b

oar

d

3-axis accelerometer

GPS receiver

ACC

ACC

AC

C

GPS antenna

(a) External configuraion (b) Internal configuraion

Figure 3.2. Configuration of sensor module

3.1.1. Force Feedback Accelerometer

Force feedback accelerometer is composed of three accelerometers for three orthogonal directional

(x,y,z) measurements, and a control board for generating acceleration output voltages and restoring the

pendulum movement to neutral state. Each accelerometer has a conductive pendulum combined with torque coil

at center, and a pair of permanent magnets and conductors are located at upper part and lower part of

accelerometer. A carrier signal, 8 kHz triangle wave signal, generator in control board continuously transmits

the carrier signal to accelerometer and demodulator for sensitive detection of electric capacity change from two

conductors. Generally, the electric capacity is inversely proportional to the distance between pendulum and

conductor. Before any vibration happens, the pendulum does not oscillate and remains at neutral state. So

capacity difference between two conductors are zero, because the distances between pendulum and each

conductors are equal. As result, only the offset voltage is generated as acceleration output voltage.

When an external vibration excites the accelerometer, pendulum also oscillates, and the vibration

changes the distances between pendulum and each capacitor, resulting in analog voltage output which is

proportional to real acceleration like as figure 3.3. When the pendulum moves upward, the distance between

pendulum and upper conductor decreases, and the distance with lower conductor increases, so that the capacity

difference occurs. The capacity difference is detected by pick-off detector in control board, and the current by

capacity difference is amplified. The amplified current is demodulated in order to exclude carrier signal from

itself, and PID controller transfers the feedback signal to torque coil in order to restore the pendulum position to

24

the initial neutral state. The current from torque coil is also transmitted to a resistance in order to generate an

analogue acceleration output voltage, and the analogue output voltage would be digitized with a 100Hz

sampling rate by 24bits ADC at ADC board. With this process, the force feedback accelerometer could measure

the acceleration range from +2g to -2g in three orthogonal axis.

Vibration direction

Permanentmagnet

Permanentmagnet

Pendulum

Torquer Coil

Pick-off Detector

Instrumentation Amplifier

DemodulatorPID

Controller

Output Amplifier

Acceleration Output

Capacitiy Difference

Carrier Signal Generator

Accelerometer Control board Resistance

Figure 3.3. Principle of force feedback accelerometer operation

3.1.2. ADC Board

The fundamental functions of ADC board are the digitization of the three independent analogue

voltages and the generation of the supply power voltages for operating 24 bits ADC and force feedback

accelerometer. ADC board is composed of four voltage regulators, a power sequencer and a 24 bits ADC like as

figure 3.4.

For normal operation of both ADC board and force feedback accelerometer, the optimal power supply

is necessary using a series of voltage regulators. The operation process of optimal power supply is expressed as

figure 3.5. When the external sensor input voltage, +5V, enters into ADC board, a power sequencer

(ADP5070ACPZ) controls three voltage regulators for generating the optimal voltages for 24 bits ADC

(AD7779ACPZ) and accelerometer control board. Under the control of power sequencer, the different voltage

regulators can operate in order. First, the switching regulator (ADP5070ACPZ) converts the external input

voltage into ±6V. As following step, a pair of ±1.65V regulators (ADP182AUJZ, ADP7118ARDZ) steps down

the ±6V into ±1.65V. In +3.3V regulator (ADP7118ARDZ), +6V from the switching regulator is also

modulated into +3.3V after a step down process of a pair of ±1.65V regulators. The generated +3.3V and

±1.65V are supplied to 24 bits ADC, so that 24 bits ADC could digitize 3-axis acceleration analogue voltages

with a 100 Hz sampling rate at three digitization channels.

The acceleration sampling time is controlled by a complex programmable logic device (CPLD) whose

timer is synchronized with the precise GPS time, so that each acceleration could incorporated its own sampled

25

time based on GPS time. This synchronization strategy allows a real time data fusion technique to be applied to

acceleration by accelerometer and GPS measurements (velocity, displacement). Additionally ±5V regulators

(ADP7118ARDZ-5.0, ADP7182AUJZ-5.0) provide the stable operating power (±5V) for force feedback

accelerometer measurement.

Figure 3.4. Primary components in ADC board

Figure 3.5. Operation process of power supply to ADC and force feedback accelerometer|

3.1.3. GPS Rover

GPS rover measures 3-axis displacement (North, East, Height) of target structure with 2 cm level of

accuracy, and 3-axis velocity (North, East, Height) of target structure with 0.03m/s of accuracy with 10Hz

sampling rate. To guarantee both the cost effectiveness and the high reliability in GPS rover measurement, Piksi

module (Swift Navigation) which only costs 495 USD and TW3870 (Tallysman) which costs only 326 USD are

adopted as GPS receiver and antenna in sensor module, respectively. TW3870 incorporates a tight phase center

variation and a superior multi-path rejection performance which enhance the measurement accuracy. Piksi

module only utilizes a single-band signal, GPS L1 with 1575.42MHz, for displacement estimation with a

centimeter level of accuracy and for velocity estimation with a centimeter per second (cm/s) level of accuracy.

Switching regulator

External input+5 v

± 5 V regulator

± 6V

± 5 V

+ 3.3 V regulator

± 1.65 V regulator

+ 3.3 V

Accelerometer control board

± 6V

± 1.65 V

24 bits ADC

+ 6 VPower

sequencer

Control

26

The displacement and velocity could compensate the drawbacks of the other, because both

displacement and velocity measurements utilizes the different techniques for each measurement. Displacement

which is measured precisely shows lack of measurement stability due to the dependency on observation

messages from GPS base module. If observation message GPS base module is deteriorated in the middle of

communication, displacement could not be measured, so that 10Hz sampling is guaranteed. On the other hand,

velocity which contains high level noise in measurement shows the strong measurement stability due to the

independency on observation message from GPS base module. Thus, the stability of velocity measurement

could compensate the displacement measurement, and the high precision of displacement measurement could

also compensate the velocity measurement. After measurement, the displacement and velocity are transmitted to

MCU board in real time with 10Hz sampling rate.

For measuring displacement using GPS rover, the observation messages from GPS base module

should be transmitted to GPS rover in real time, so that the GPS rover applies a real-time kinematic (RTK)

technique in order to estimate the precise 3-axis displacements. For guaranteeing the real time observation

message transmission from GPS base module to GPS rover in sensor module, a wire communication or a

wireless radio communication could be applied between GPS rover and GPS base module. The concrete

communication method could be decided by considering environmental property of target civil structure. If

obstacles on civil structure between sensor module and GPS base module are simple enough for stable wireless

communication, the wireless radio communication method could be adopted for guaranteeing real time

observation message transmission. Otherwise, wire communication using user datagram protocol (UDP) could

support the observation message transmission from GPS base module to sensor module. Then GPS receiver

which stably receives the observation message from GPS base module outputs displacement measurements with

respect to GPS base module position, continuously.

In the process of displacement measurement, GPS rover continuously estimates the optimal integer

which represents the distance between satellites and GPS rover after multiplying carrier wavelength (19cm). So

at initial state, the number of hypothesis of optimal integer is 999 as default and starts to converge towards 1.

When the number of hypothesis is one, the mode of displacement measurement changes from “float” to “fixed”.

At “fixed” mode, the reliability of displacement measurement is maximized, so that only displacement data

acquired at “fixed” mode are treated as input of data fusion technique at computation module.

Different from displacement measurement, velocity measurement of GPS rover utilizes Doppler effect

of satellite signal without assistance of GPS base module, so that the velocity of target is measured as

∆

(3.1)

where is a velocity of satellite signal, is a satellite velocity, ∆ is the amount of frequency change, and

is a initial signal frequency. Since the satellite signal is an electromagnetic wave, the velocity of satellite

signal (c) is about 3.0 10 / . Additionally the satellite velocity ( ) and the amount of frequency change

27

(∆ ) are simply acquired from the received satellite signal, and the initial signal frequency ( ) is 1575.42 MHz.

3.1.4. MCU Board

The fundamental functions of MCU board are the timer synchronization between GPS rover and a

complex programmable logic device (CPLD) based on GPS time and the stable data communication of sensor

module with both GPS base module and computation module. The timer synchronization is a necessary

procedure which allows acceleration to be sampled on the basis of GPS time, so that the foundation to fuse the

different measurement data including acceleration, velocity, and displacement could be established.

Additionally, the stable data communication of MCU board forms a foundation of the real time responses

monitoring at computation module.

For accomplishing the functions, a MCU (STM32F767ZIT6), a CPLD (EPM570T100C5), and an

Ethernet controller (W5100) are adopted in MCU board as figure 3.6. The three primary components including

MCU, CPLD and Ethernet controller operates like as figure 3.7. MCU collects GPS time, 3-axis displacement

and velocity from GPS rover, 3-axis digitized acceleration from CPLD and observation message from GPS base

module via Ethernet controller. Simultaneously, MCU transmits the total dynamic responses of target structure

including acceleration, velocity, and displacement with GPS time to computation module via Ethernet controller

in every 1 second and GPS time to CPLD in every 60 seconds. CPLD calibrates its timer in every 60 seconds,

which only allows timer synchronization error lower than 1ms between force feedback accelerometer

measurement and GPS rover measurement.

Figure 3.6. Primary components of MCU board

Since the estimated GPS time only contains about 60 ns error size, the strategy that CPLD calibrates

its timer using the GPS time transmitted from MCU to CPLD is reasonable. When GPS time generation is in

trouble, timer updates and counts 10ms using a built-in internal oscillator crystal with 3.3 MHz frequency until

the GPS time is transmitted to CPLD. With this timer synchronization strategy, CPLD continuously triggers 24

bits ADC in ADC board in every 10ms in order to achieve 100Hz sampling rate of 3-axis acceleration and

allocates sampled time to all each acceleration sample based on GPS time.

The transmitted observation message from GPS base module arrives at GPS rover via Ethernet

controller and MCU. Then the observation message is utilized as source of measuring centimeter level of

28

displacement of target structure using RTK technique at GPS rover. Finally, the synchronized acceleration,

velocity, and displacement based on GPS time whose sampling rates are 100Hz, 10Hz, and 10Hz, respectively

are transmitted to computation module using a wired LAN network which could select a communication

protocol as UDP.

MCUCPLD

Ethernetcontroller

3-channel acceleration

Trigger per 10 msec

3-axis acceleration

Timer Synchronization

GPS time 3-axis displacement & velocity

3-axis displacement, velocity, acceleration

Computation module

Observation message

GPS rover

Observationmessage

Figure 3.7. Operation process for time synchronization and data communication

3.2. GPS Base Module

GPS base module only transmits an observation message to sensor module in order to guarantee

displacement measurement at GPS rover in sensor module. The observation message includes carrier phase

measurement information at reference point. The principal components of observation message are the cycle of

varying carrier wavelength and the phase of carrier wave in a single wave. Note that the distance between a

satellite and GPS antenna is represented by the multiplication of wavelength and three real number including the

cycle of varying carrier wavelength, the phase of carrier wave, and ambiguity integer.

GPS base module is composed of GPS base (GPS receiver, GPS base antenna), and MCU board,

which is simple configuration compared to sensor module. Different from sensor module, GPS base antenna

(VP6000) incorporates higher performances in small phase center variation and multi-path signal rejection than

GPS antenna of sensor module (Figure 3.8). Since the GPS base module continuously broadcasts its observation

messages to multiple sensor modules in a 6-DOF dynamic responses measurement system as source of RTK

technique like as figure 3.9, the measurement accuracy enhancement in GPS base module results in the

displacement measurement accuracy in all independent sensor modules.

29

Figure 3.8. Antenna of GPS base module (VP6000)

MCU

Ethernetcontroller Observation

message

Sensor module 1

GPS base

Observationmessage

Observationmessage

Sensor module 2

Sensor module N

Figure 3.9. Observation message broadcasting from GPS base module

3.3. Computation Module

Computation module collects all dynamic response measurement data including acceleration, velocity,

and displacement from sensor modules, and estimates the precise 6-DOF dynamic responses of all target

locations in real time where sensor modules are installed. So any computer whose CPU performance is similar

to i7-4710 (2.5GHz), and memory capacity is not less than 8 GB could be a computation module of 6-DOF

dynamic responses monitoring system like as figure 3.10. For computing the precise dynamic responses, a series

of data processing techniques are used: Reliability assessment algorithm, IIR high-pass filter, IIR low-pass filter,

Inclination function, Kalman filter.

30

Figure 3.10. Example of computation module

The order of the data processing technique is represented as figure 3.11. First the reliability

assessment algorithm is applied to the GPS measurement such as velocity and displacement in order to filter out

the low quality measurement. In parallel, IIR high-pass filter and IIR low-pass filter are used to eliminate low

frequency noise such as bias and high frequency noise such as measurement noise, respectively.

Figure 3.11. Summary of data processing for estimating the precise 6-DOF dynamic responses

3.3.1. Reliability assessment algorithm

Reliability assessment discards low quality GPS displacement and velocity measurements using two

types of standards such as the number of satellite and the state of flag. Generally, velocity measurement requires

at least four satellites and displacement measurement requires at least five satellites. And both measurement

reliability would be increase in proportion to the number of satellites. In carrier phase measurement, the

ambiguity integer is always required to be estimated. If the estimation result is optimal, the state of flag changes

into “fixed”, it means that displacement measurement includes high reliability. This could be the other standard

31

to filter out the low quality displacement measurement after applying the standard of the number of satellites.

The displacement and velocity measurement after reliability assessment algorithm are transmitted to Kalman

filter to be used as input of data fusion.

3.3.2. IIR high-pass filter and IIR low-pass filter

IIR high-pass filter eliminates the low frequency noise in acceleration which includes a capacity to

deteriorate the estimation result. Even with a small size of order, the IIR filter could show high performance in

preventing amplitude distortion, so that instead of FIR filter, IIR filter is designed for low frequency noise

elimination in acceleration. In IIR high-pass filter design, the Butterworth filtering function is selected, because

the amplitude in passband does not oscillate. However, the Butterworth filtering function shows a lack

performance at linear phase response. Since the interesting frequency response band of the civil structure is

narrow enough as from DC to 20Hz, if the nonlinear phase distortion property is minimized as similar to linear

property for the interesting frequency range, then the IIR high-pass filter would be the optimal realistic high-

pass filter. For example, frequency range of earthquake at Tohoku in 2011 was from 0.1 to 10Hz.

(a) Amplitude response (b) Phase response

Figure 3.12. Response of IIR high-pass filter

The optimally designed IIR high-pass filter has a cutoff as 0.25Hz, the order as four, and the pole

points whose magnitude is smaller than 1. In amplitude response, as expected, the amplitude distortion is little,

so the almost all the passband components of input signal would not be amplified and attenuated like as figure

3.12 (a). In phase response like as figure 3.12 (b), the phase delay after cutoff frequency is lower than 1 radian,

which means the time delay is almost 1.5ms for 100Hz sampled acceleration. Additionally, after cutoff

frequency, the phase response is similar to linear with very small slope, so that the group delay is also

uninfluential for application to 100Hz sampled acceleration. The filter assures its stability performance since all

the pole points is inside unit circle in z plane.

0 0.2 0.4 0.6 0.8 1 1.2 1.4Frequency (Hz)

0

0.5

1

1.5

Mag

nitu

de

0 0.2 0.4 0.6 0.8 1 1.2 1.4Frequency (Hz)

0

1

2

3

4

5

6

Ph

ase

(ra

d)

32

IIR low-pass filter eliminates the high frequency measurement noise in acceleration measurement.

Since the inclination function which use accelerations as input data is based on the arctangent calculation, the

angular displacement result is vulnerable to high-frequency noise. For an optimal design of IIR high-pass filter,

Butterworth function and filter order as 4 are used for stable amplitude response output. Additionally a cutoff

frequency as 25Hz is used, considering the target frequency band of civil structure response is limited from DC

to 20Hz. The output as low-pass filtered accelerations are only used for calculating the angular displacement

which represents the inclination of sensor module.

3.3.3. Inclination function

Inclination of sensor module is simply calculated from inclination function only with the small

computation cost. As shown in figure 3.13 (a), the inclinations such as ρ, ∅, and θ is the relative angle

between the ideal X,Y,and Z coordinate system and the sensor module coordinate system composed of X, Y,

and Z. So when the sensor module is in slightly tilted state on target structure, the angle could be detected. Also

when the target structure tilts to any direction, the inclination function could output the amount of inclinations

such as ρ, ∅, and θ. These would be the important physical quantities for long term monitoring of civil

structures. As shown in figure 3.13 (b), the measured acceleration in X direction , the measured

acceleration in Y direction , and the measured acceleration in Z direction are the inputs of the

inclination function. In succession, the inclinations such as ρ, ∅, and θ results from the inclination equations

like as equation 3.4, 3.5, and 3.6.

ρ tan

(3.4)

∅ tan

(3.5)

θ tan (3.6)

33

(a) Three axis of inclination coordinate (b) Block diagram of Inclination function Figure 3.13. Description of inclination function

3.3.4. Kalman filter

A multi-rate two-stage Kalman filter using acceleration, velocity, and displacement is implemented

for data fusion, so that the precise 6-DOF dynamic responses are estimated. The applied Kalman filtering

technique is slightly modified version from the two-stage Kalman filter proposed by Kim et al (2016) [17]. Not

only using both displacement and acceleration data as factor of observation vector, but also velocity data are

used as factors of observation vector. The sampling rate of acceleration is high as 100Hz, but those of velocity

and displacement are almost 10Hz. So the multi-rate concept is necessary in data fusion technique. Also for long

term monitoring, low frequency noise such as bias would be an influential factor which would deteriorate the

quality of estimation. For preventing the estimation result from deterioration, the two-stage Kalman filter

concept which is strong at bias effect correction is necessary in data fusion technique. Additionally, the two-

stage Kalman filter shows a strong performance in reducing the discontinuity due to the multi sampling rate, so

that a smoothing process which could not be applied in real-time process does not need to be applied after two-

stage Kalman filter.

Figure 3.14. State-space model of stage 1 Kalman filter in a multi-rate two-stage Kalman filter

34

A State-space model of stage 1 Kalman filter is developed as three cases for guaranteeing multi-rate

concept like as figure 3.14. Here, the state vector is defined as which are a vector

composed of displacement, velocity, and acceleration. All measurements are updated at posterior step, simple

integral process is applied at transition equation at prior step. The measurement vector is composed of the

acceleration measurement after IIR high-pass filter , the velocity measurement after reliability assessment

filter , and the displacement measurement after reliability assessment filter . In reality, the highest

sampling rate among the three measurements is the sampling rate of acceleration, the second one is that of

velocity, and the lowest sampling rate is that of displacement. So the number of measurement would be a

criterion to decide an observation equation.

A state-space model of stage 2 Kalman filter is specialized in error estimation due to bias or low

frequency noise. The model starts from two assumptions that bias is piecewise constant and that state vector

, and in stage 1 Kalman filter could be corrected by bias vector and sensitivity matrix ,

and as equation 3.2 and 3.3. The final state space model of stage 2 Kalman filter is summarized as figure

3.15.

(3.2)

(3.3)

Figure 3.15. State-space model of stage 2 Kalman filter in a multi-rate two-stage Kalman filter

In figure 3.15, the observation equation at posterior step which is written in a term of bias vector

starts from the assumption that measurement residual is defined as and

converted into . Thus, the data fusion process using a two-stage Kalman

filter is summarized as figure 3.16, and finally the precise acceleration, velocity, and displacement are derived.

35

Figure 3.16. Summary of a multi-rate two-stage Kalman filter

Finally the computation module outputs the precise dynamic responses such as acceleration, velocity,

displacement, and angular displacement with 100Hz sampling rate. For displacement, velocity and acceleration,

the measurable response frequency band is considered as from DC to 50Hz due to Nyquist frequency, but for

angular displacement, the measurable response frequency band is considered as from DC to 25Hz. Since the

input data of inclination filter are low-pass filtered with 25Hz cutoff frequency, the frequency band is reduced

compared to the frequency band of other responses.

36

Chapter 4. Experimental Validation

For verifying the performance of the developed 6-DOF dynamic responses monitoring system, two

vibration tests, a rotation test, and a field application test are conducted. The measurement performance of

sensor module is compared to that of laser displacement sensor (KL3-W400) whose resolution is 10μm and

linearity is ±0.08%. The implemented two types of vibration scenarios are organized as 1) a low frequency

sinusoidal, and 2) a pseudo-seismic vibration with a range of 0 to 10Hz. Since a low frequency vibration

scenario represents the usual response of large structures, and a pseudo-seismic vibration scenario represents the

abrupt response of structures at earthquake, the precise and accurate measurements of those scenarios would

validate the effectiveness of the proposed monitoring system. Especially, rotation test is implemented for

verifying the precision of inclination measurement. Finally, the field application test is implemented at Penang

Second Bridge.

4.1. Sinusoidal Vibration Test

The fundamental experiment set up for vibration test is configured as figure 4.1. For realizing the

designed vibration, a modal shaker (APS 400) which can delicately vibrate as the input signal is utilized. Sensor

module is installed on a modal shaker, and GPS base module is installed on a reference location where vibration

is none. All modules are connected to switching hub, so that UDP communication for real time data

transmission is guaranteed. LDS sensor is also mounted at steel stand and measured the vibration of modal

shaker at the same time as figure 4.2.

+5 VGND+

+5 VGND+

Power Supply

Sensor Module

Shaker

Switching Hub

Computation Module

GPS Base Module

Figure 4.1. Schematic configuration of vibration test

37

Figure 4.2. Installation of 6-DOF dynamic responses measurement system for vibration test

The 1Hz sinusoidal vibration with a short DC component is measured by both sensor module and LDS

simultaneously. The 1Hz sinusoidal signal is excited for 25 seconds and DC signal for 5 seconds. Sensor

module and LDS measured the excited vibration with an identical sampling rate as 100Hz for 30 seconds. The

raw measurement data from sensor module before data processing at computation module are shown as figure

4.4. Since the sinusoidal vibration with 1cm level of amplitude was excited only in Z direction, so that the

effective responses were only measured in Z axis. In other axis, only the measurement noises were acquired.

After the computation module process the raw measurements from sensor module, the precise responses were

estimated as figure 4.5.

For verifying the performance of the proposed 6-DOF dynamic responses measurement system, the

displacement is representatively selected as a physical quantity for comparing to a reference sensor which is

LDS, and the graphical comparison is shown as figure 4.3. The root mean square error (RMSE) between LDS

measurement and the proposed system measurement was verified as 1.79 mm. The unstable fluctuation exists in

GPS displacement measurement, and it magnifies the probability of error increase. However, the acceleration

and velocity which show the almost zero mean property, so the proposed system could show a superior

performance depending on acceleration and velocity.

Figure 4.3. Precision comparison of the proposed system and LDS for sinusoidal vibration (RMSE = 1.79mm)

38

(a) Measured responses by accelerometer and RTK-GPS sensor

(b) Estimated responses by the proposed system

Figure 4.4. 1Hz sinusoidal vibration by accelerometer and RTK-GPS and by proposed system

39

Displacement measured by GPS rover in sensor module usually includes low frequency noises with

high amplitude. In figure 4.4 when the time is 40s to 70s, the GPS displacement measurement is comparatively

stable and the effect of low frequency noise is almost none, but the low frequency noise in GPS displacement

measurement is apparent after 70s, and it finally deteriorates the quality of displacement measurement of the

proposed system. The displacement measured by proposed system from 70s to 100s fluctuates due to a 0.1Hz

noise with 3.5mm amplitude.

The low frequency noise occurs when the the estimated ambiguity integer is not stable or the multi-

path induced uncertainty increases. To guarantee the stability of ambiguity integer estimation and reduce the

uncertainty of multi-path reduction, not only L1 GPS signal and L2 and L5 GPS signals are required for

estimation process. The multiple frequencies of GPS signal would reduce the instability in GPS displacement

measurement, because they can express the same displacement with different frequencies or wavelengths. As

future work, the proposed system would adopt a RTK-GPS receiver which can use multiple frequencies of GPS

signals in order to the measurement stability.

Table 1. RMSE and noise reduction results of dynamic responses in X and Y axis

RMSE of sinusoidal vibration RMSE of random vibration

Axis Dynamic Response

ACC & GPS sensors

Proposed system

Noise reduction (%)

ACC & GPSsensors

Proposed system

Noise reduction (%)

X Acc mm/ 17.65 17.44 1.19 85.94 85.06 1.02

Vel mm/s 5.43 0.44 91.90 34.86 1.34 96.15

Disp (mm) 5.47 1.93 64.72 3.60 1.32 63.33

Y Acc mm/ 25.50 25.18 1.25 109.69 108.87 0.77

Vel mm/s 39.89 0.73 98.17 30.93 1.48 95.22

Disp (mm) 3.91 1.44 63.17 3.18 1.48 53.46

While the dynamic responses are measured by in Z axis, the zero input responses are measured in both

X and Y axis. So the RMSE values of the measurements in X and Y axis are considered as measurement noises.

Table 1 shows the RMSE values in X and Y axis for sinusoidal vibration test and random vibration test. Also

the noise reduction is quantified by comparing the measurements of accelerometer and GPS sensors and the

measurements of the proposed system. One of the significant effects of the proposed system is noise reduction

in velocity and displacement measurements. This results from the data fusion strategy which treats the

acceleration measured by force feedback accelerometer in sensor module as a significantly reliable measurement

compared to velocity and displacement by GPS.

4.2. Random Vibration Test

For verifying the measurement performance in seismic situation, the random vibration with DC to

10Hz frequency band is simulated by shaker and the dynamic responses are measured proposed system and laser

displacement sensor. The experimental setup is identical to that of sinusoidal vibration test.

Considering that GPS could not inherently measure displacement or velocity whose frequency is

40

higher than 5Hz, the random vibration test tries to verify the outstanding performances of the proposed system

in seismic situation. The random vibration was developed using frequency band from DC to 10Hz, and also the

experiment configuration was identical to that of sinusoidal vibration test like as figure 4.1. The estimated

dynamic responses are shown as figure 4.5 and the comparison result is shown as figure 4.6. Even though the

low reliability of GPS measurement for random vibration, the proposed system could achieve the high precision

as 3.73mm when compared to LDS measurement like as figure 4.6.

Figure 4.5. Z-axis random vibration by accelerometer and RTK-GPS (left) and by proposed system (right)

Figure 4.6. Precision comparison of the proposed system and LDS for random vibration (RMSE = 3.73mm)

The performance of the proposed system in random vibration decreased compared to that of 1Hz

sinusoidal vibration. There are two main reasons for this measurement error increase. The first one is the low

sampling frequency of GPS measurement as 10Hz. So the vibration components whose frequencies are higher

than 5Hz are difficult to be measured. The second one is high-pass filtered acceleration. For the purpose of bias

elimination, the low frequency vibration components whose frequencies are lower than 0.25Hz could not be

included in acceleration input data of Kalman filter.

41

4.3. Rotation Test

The measurement performance of inclination is validated by the rotation test using a low frequency

rotation with 0.24Hz as figure 4.7 (a). The rotation plate is only free from rotation in Y axis, and a reference

rotary encoder (E40H6-1000-3-T-24) whose resolution is 0.36° is fixed at the end of rotation axis. Sensor

module is installed on the rotation plate, so that both the sensor module and the rotary encoder could rotate for Y

axis identically, and measure the identical low frequency rotation with 0.24Hz simultaneously. By comparing

the inclination measurement of the proposed system with that of the rotary encoder using RMSE, the inclination

measurement in X axis includes 0.5266° error and the measurement in Z axis includes 0.5278° error as figure

4.7 (b). For zero-response measurement in Y axis, RMSE value is calculated as 0.0516°.

(a) Configuration of rotation test (b) Estimated responses by the proposed system Figure 4.7. Precision comparison of the proposed system and the rotary encoder for 0.24Hz rotation

4.4. Penang Second Bridge Test

For validating a field applicability, the proposed system is applied on Penang Second Bridge,

Malaysia and measures the dynamic responses of the middle point of main span. Penang Second Bridge

completely built in 2014 is known as the longest bridge in South asia as 24km. For cable-stayed section, the

main span is 250m and the side span is 125m. Since the bridge has been built as one of concrete and cable-

stayed bridges, so that the structural stiffness is comparatively large. Additionally the traffic volume is slow and

small but wind is continuously strong, so that the typical dynamic responses are usually caused by wind load as

0.4Hz. As illustrated on figure 4.8, the sensor module and computaton module are installed on the middle point

of main span where the displacement measurement is challenging, and GPS base module is installed on the

column of bridge where diplacement is almost zero. Next to the sensor module, the Trimble GNSS sensor

(NetR9) which is a high-cost RTK-GNSS sensor has been installed for monitoring the vertical displacement and

deflection. This RTK-GNSS sensor is used for validating the field applicability of the proposed system by

42

comparing the measurement responses by the proposed system and the measurement responses by existing

RTK-GNSS sensor.

(a) Configuration of the proposed system application on Penang

Second Bridge (b) Sensor module and computation module

(c) GPS base module

Figure 4.8. Field application test

The comparison of the proposed system and RTK-GNSS sensor is described as figure 4.9. The

ambient vibration with 0.4Hz is measured by the proposed system in three dynamic response graphs in time

series. Acceleration and velocity show the 0.4Hz vibration clearly, but displacement could not show the 0.4Hz

vibration apparently. Since the vibration of bridge is so ambient, and the amplitude of bridge response is lower

than 5mm, the inherently existing low frequency noise is dominant in GPS measurement. Even though the

displacement plot is contaminated by a low frequency noise, the 0.4Hz ambient vibration could be measured in

displacement plot.

RTK-GNSS sensor shows a superiority in eliminating low frequency noise, and the strong property

results from both the GNSS multiple constellation (GPS, GLONASS, BeiDou and Galileo) and the multiple

types of satellite signals as L1, L2, and L5. However, commercial RTK-GNSS sensor shows the limitation in

measuring the ambient vibration whose amplitude is lower than 5mm. Since the precision of displacement

measurement by commercial RTK-GNSS sensor is about 20mm, the ambient vibration of bridge could not be

measured. In frequency domain, all the measurements by the proposed system show a 0.4Hz frequency

component, identically. But only the white noise appears in frequency domain of RTK-GNSS displacement

measurement.

43

(a) Responses in time domain (b) Responses in Frequency domain

Figure 4.9. Comparison of the bridge responses (Z-axis acceleration, velocity, displacement) measured by proposed system and response (Z-axis displacement) by RTK-GNSS sensor

44

Chapter 5. Conclusion

As an alternative displacement sensor, a 6-DOF dynamic responses measurement system which is

composed of sensor module, GPS base module, and computation module is proposed for monitoring civil

structures, and it validates the measurement performance by three lab-scale tests and a field application.

Especially, for displacement measurement, 1Hz sinusoidal vibration was measured with 1.73mm RMSE and the

random vibration with frequency range as from DC to 10Hz was measured with 3.73mm RMSE. For ration test,

about 0.53 ° level of measurement precision for low frequency rotation is verified by comparing the

measurement by the proposed system and that of rotary encoder.

In field applicability test, the measurement performance of even an ambient vibration whose

amplitude is less than 5mm was verified, and when it comes to measuring the ambient vibration, the proposed

system was superior to RTK-GNSS sensor. As future work, the proposed system would adopt not only L1 GPS

signal but also L2 GPS signal for enhancing the quality of GPS measurement and the quality of total dynamic

responses.

As future work, the proposed system would adopt not only L1 GPS signal but also L2 GPS signal for

enhancing the quality of GPS measurement and the quality of total dynamic responses. A filtering technique for

eliminating the low frequency noise in displacement measured by GPS in sensor module would be developed

for precision enhancement.

45

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46

Acknowledgments in Korean

2년 6개월간의 연구실 생활을 하면서, 함께 시간을 보낸 지도 교수님과 연구실 동료들, 그리고 항상

힘이 되어주는 가족들에게 감사의 인사를 전하고 싶습니다. 먼저 연구에 대한 열정과 뛰어나신 지성으로 아

낌없는 지도를 해주시는 손훈 교수님께 감사의 말을 전합니다. 바쁜 일정에도 불구하고 논문에 대한 심사와

지도를 해주신 김아영 교수님, 공승현 교수님께 감사의 말을 전합니다. 팀으로서 험난한 현장 실험부터 프

로젝트 진행 업무까지 동고동락한 기영이형, 재묵이형, 준연이에게도 고마움을 전합니다. 또한 본 연구를 수

행할 수 있도록 지원 해주신 국토교통부(U-City 석박사 지원사업, 국토교통기술촉진연구사업), 국민안전처

(소방안전 및 119구조 구급 기술연구개발사업), 미래창조과학부에 (연구성과사업화지원사업) 감사의 말을 전

합니다.

근 2년간 가장 많은 시간을 함께 했던 연구실 동료들에게 한명 한명 모두 감사의 말을 전합니다.

입학 전에 좋은 조언을 많이 해주신 윤규형, 해외에서 열심히 활동하시는 민구형, 축구왕이면서 랩짱인 진

열이형, 모든 걸 다 가진 멋쟁이 준우형, 긍정적이고 모니터 세 개씩 사용하는 승환이형, 잠을 정말 잘 자는

병주, 유통업의 발전에 끊임없이 기여하는 상민이, 최고의 룸메이트였던 성흠이, 독일을 사랑하는 용탁이,

홍콩에서 반갑게 맞이해줬던 Wang Qian, 재묵이형의 영혼의 라이벌 병진이, 형으로서 선배연구자로서 많이

가르쳐주신 기영이형, 한국말 못하는 척하는 Peipei, 많이 혼내고 많이 챙겨줬던 형진이형, 소맥과 풋살을

즐기는 Pouria, 돈 잘 벌고 이것 저것 잘하는 지민이, 먼 나라에서 아이도 키우면서 연구하는 Nazirah, 명대

사 줄줄 외는 스타 천재 수영이, 열화상을 사랑하는 순규, 스타를 좋아하고 예비군 훈련가면 분대장이 되는

재묵이형, 나랑 바보짓 하면서 잘 놀아주는 준이, 얼굴부터 축구 스타일까지 호날두 같은 익근이, 함께 졸업

하는 예비 유부남 Timotius, 조만간 사장되실 준연이, 박사 가긴 할거 같은데 뒤로 넘어져도 코가 깨진다는

지호, 풋살 하자하면 꼭 Yes 하는 진호, 현재까지 우리 랩 한국인 대학원생 여자 얼짱 가영이, 회보다 육류

를 좋아하는 현미 누나, 조카를 정말 좋아하는 은혜 누나, 얼굴에 웃음이 가득한 재신이 누나까지 모든 SSS

Lab 구성원들에게 감사의 말씀을 전합니다.

그리고 함께 있을 때 즐거운 친구들에게도 감사의 말을 전합니다. 중학교부터 고대까지 같이 다니

고, 결국 카이스트도 함께 와서 갈수록 정이 든 민수, 부산 가면 꼭 찾아 만나는 기덕이, 나의 힘든 시기를

함께 해준 철우, 줄기, 하담, 두언, 나랑 게임하고 놀아주고 야식 같이 먹던 시언, 종찬, 그리고 최고의 단합

력을 자랑하는 고대 호연반 09학번 동기들, 같이 술 마시고 비트 코인하는 종욱이형, 종희, 윤수, 영어를 즐

겁게 공부할 수 있게 해 준 타임반 친구들, 마음은 나눌수록 커질 수 있다는 걸 알게 해준 아니시아 수녀님

과 성모의 집 공부방 선생님들, 모두 감사합니다.

마지막으로 언제나 멀리서 가장 큰 믿음으로 힘을 주는 아버지, 어머니, 윤화를 포함한 우리 가족

들 모두에게도 감사의 말을 전합니다. 앞으로도 지금의 감사한 마음을 잊지 않고, 세상에 좋은 영향을 미치

는 사람이 되도록 노력하겠습니다.

2017년 7월 카이스트에서 구건희 올림

47

Curriculum Vitae

Personal Information

Name: Gunhee Koo Place and Date of Birth : Busan, South Korea on April 29, 1989 E-mail: gunhee.koo@gmail.com

Education

2017 August M.S., Dept. of Civil and Environmental Engineering, KAIST, Korea 2015 February B.S., School of Civil, Environmental and Architectural Engineering, Korea

University, Korea

Journal Publication

* The corresponding authors are underlined 1. Kim, K., Choi, J., Koo, G., & Sohn, H. (2016). Dynamic displacement estimation by fusing biased high-

sampling rate acceleration and low-sampling rate displacement measurements using two-stage Kalman estimator. Smart Structures and Systems, 17(4), 647-667.

2. Lim, H. J., Kim, Y., Koo, G., Yang, S., Sohn, H., Bae, I. H., & Jang, J. H. (2016). Development and field application of a nonlinear ultrasonic modulation technique for fatigue crack detection without reference data from an intact condition. Smart Materials and Structures, 25(9), 095055.

Conference Proceedings

* The corresponding authors are underlined 1. Gunhee Koo, Kiyoung Kim, Jaemook Choi, Hoon Sohn, “Estimation of dynamic displacement via data

fusion of an accelerometer and a displacement sensor”, KIBSE, Uiwang, Republic of Korea, November 13, 2015.

2. Jaemook Choi, Gunhee Koo, Kiyoung Kim, Hoon Sohn, “A Displacement Estimation Method based on Data Fusion of Velocity and Acceleration for Safety Assessment of Structure under Fire”, KOSHAM, Seoul, Republic of Korea, February 18~19, 2016.

3. Gunhee Koo, Jaemook Choi, Kiyoung Kim, Hoon Sohn, “A Fire-Induced Displacement Estimation Method for Civil Infrastructures”, SUPDET, Texas, USA, March 1~3, 2016.

4. Gunhee Koo, Kiyoung Kim, Jaemook Choi, Jun Yeon Chung, Hoon Sohn, “Application of Kalman Filter based Displacement Measurement System to Precise Dynamic Response Measurement”, KSMI, Daejeon, Republic of Korea, October 12~14, 2016

5. Gunhee Koo, Kiyoung Kim, Jaemook Choi, Jun Yeon Chung, Hoon Sohn, “Development of a dynamic displacement measurement system by fusing GPS-RTK and accelerometer data”, KKHTCNN Symposium, Hong Kong, China, December 3~5, 2016.

48

6. Gunhee Koo, Kiyoung Kim, Jaemook Choi, Jun Yeon Chung, Hoon Sohn, “Development and Field Application of Integrated Measurement System using RTK-GPS Sensor and Accelerometer for Precise Measurement of Structural Dynamic Displacement”, EESK, Seoul, Republic of Korea, March 24, 2017.

Patent

1. Nam Yeol Kwon, Doo Young Kang, Seung Beom Pack, Min Jae Kim, Jae Min Moon, Jin Seok Kang, Hoon Sohn, Kiyoung Kim, Gunhee Koo, Jaemook Choi, Jun Yeon Chung, “Accurate Measuring System and Its Method of Structure”, Korea Patent (Application# 10-2017-0016270)

2. Hoon Sohn, Kiyoung Kim, Gunhee Koo, Jaemook Choi, Jun Yeon Chung, “Dynamic Displacement Calculation Device and Method of Calculating Dynamic Displacement”, Korea Patent (Application# 10-2017-0061811)

Copyright

1. Gunhee Koo, Kiyoung Kim, Jaemook Choi, Hoon Sohn, “Real-time data acquisition program with multiple sampling rates for both GPS and accelerometer”, Copyright (C-2016-011341)

2. Hoon Sohn, Kiyoung Kim, Jaemook Choi, Gunhee Koo, Jun Yeon Chung, “3-axis dynamic responses monitoring program for 6-DOF dynamic responses measurement sensor module.”, Copyright (C-2017-012419)

Award

2016 February Best paper by presenting paper entitled “A Displacement Estimation Method based on Data Fusion of Velocity and Acceleration for Safety Assessment of Structure under Fire” on the Korean Society of Hazard Mitigation 2016, Sung-kyun-kwan university, Seoul, Korea.

Research Projects

2015-Present Development and commercialization of 6-DOF dynamic response measurement system for civil infrastructure monitoring, Ministry of Land, Infrastructure and Transport of Korea (Funded: 1,500,000 USD from 2015.06.19 to 2018.06.18)

2015-Present Development of a fire-induced structural collapse alarm system using smart ball sensors, Ministry of Public Safety and Security (Funded: 2,375,000 USD from 2015.07.01 to 2018.06.30)

2016-Present Commercialization and global market development of 6-DOF displacement measurement system for civil infrastructure monitoring, Ministry of Science, ICT and Future Planning (Funded: 24,000 USD from 2016.05.04 to 2018.05.03)

49

Educational Experiences

2015 Fall Teaching Assistant, CE202, “Structural Mechanics”, KAIST, 3 unit undergraduate course