MR 댐퍼를 이용한 지진하중을 받는 지진격리 벤치마크 구조물의 신경망제어

MR 댐퍼를 이용한 지진하중을 받는지진격리 벤치마크 구조물의 신경망제어

MR 댐퍼를 이용한 지진하중을 받는지진격리 벤치마크 구조물의 신경망제어

Heon-Jae Lee*: Ph.D. CandidatePh.D. Candidate, KAIST, Korea

Sang-Won Cho: Post Doctoral Fellow, UWO, Canada

Ju-Won Oh: Professor, Hannam University, Korea

In-Won Lee: Professor, KAIST, Korea

2006 2006 춘계 지진공학회 학술발표회춘계 지진공학회 학술발표회17~18, Mar, 200617~18, Mar, 2006

22Structural Dynamics & Vibration Control Lab., KAIST, KoreaStructural Dynamics & Vibration Control Lab., KAIST, Korea

2006 한국지진공학회 학술발표회한국과학기술원 창의학습관

• IntroductionIntroduction

• Benchmark ProblemBenchmark Problem

• Proposed MethodProposed Method

• Training Neuro-Control SystemTraining Neuro-Control System

• Performance EvaluationPerformance Evaluation

• ConclusionConclusion

OUTLINEOUTLINE


2006 한국지진공학회 학술발표회한국과학기술원 창의학습관Introduction

• Base Isolation– One of the most widely implemented seismic

protection system– Mitigates the effects of an earthquake by isolating

the structure from ground motion

Kodiak, Alaska San Francisco



• Nonlinear Devices for Base Isolation

Benefit– Restoring force and adequate damping

capacity can be obtained in one device

Drawbacks– Strongly nonlinear

– Poor adaptability for a wide range of ground motion

– Especially strong impulsive ground motions generated at near-source location

Lead Rubber Bearing

Friction Pendulum Bearing

Introduction



• Hybrid Control Strategies– Consisting of a base isolation system combined

with actively controlled actuators

– Advantages• High performance in reducing vibration• High adaptability for different ground excitation• Ability to control of multiple vibration modes

– Drawbacks• Requirement of a large external power supply• Active systems may have the risk of instability

Introduction



• Semi-Active Base Isolation System– Consisting of a base isolation system that

employs semi-active control devices (e.g. MR

dampers, controllable friction dampers)

– Similar high adaptability to the active system

– No requirement of large power supplies

Introduction


2006 한국지진공학회 학술발표회한국과학기술원 창의학습관Introduction

• Semi-Active Neuro-Controller– Improved neuro-controller (Kim et al., 2000, 2001)

• New training algorithm based on cost function• Sensitivity evaluation algorithm to replace an emulator neural

network

– Clipped algorithm• Clips the control force that cannot be achieved by MR dampe

r



• Purpose– To provide systematic and standardized means – By making direct comparisons between

competing control strategies, including devices, algorithms, sensors, etc.

– To allow researchers in structural control to test their algorithms and devices

– This benchmark problem is about a smart base isolation system.

Benchmark Problem



• Benchmark Structure– A base-isolated eight-story,

steel-braced framed building– Similar to existing building in

Los Angeles, California

– In this investigation, only the linear elastomeric isolation system which consists of 92 bearings is considered.

– Modeled using three master degrees of freedom per floor

– 16 MR dampers (8 along the x-axis and 8 along the y-axis) are also installed

Benchmark Problem



• Sample Earthquakes– Both the fault-normal (FN) and fault-parallel (FP) c

omponents of Newhall, Sylmar, El Centro, Rinaldi, Kobe, Ji-ji, Erzinkan

• Control Cases– Case I : x-direction (FP), y-direction (FN)– Case II: x-direction (FN), y-direction (FP)

Benchmark Problem



STRUCTURE

Neural Network

xx ,gx

fMR Damper

Clipped

Algorithmdf

v

Clipped Neuro-Controller

Block diagram of the proposed method

Proposed Method



1I

2I

1nI 23no

22o

21o

1ihW

2jiW

• Structure of the neural network• Structure of the neural network

Inputlayer

Hiddenlayer

Outputlayer

Proposed Method



• Improved Neuro-Controller– New training algorithm

• The neuro-controller is trained by minimizing a cost function

1

0k

T

k1k

T

1 RuuyQy2

1ˆN

kkJ

where, is specific state, is control signal and

are weighting matrices

y u

R,Q

Proposed Method



• Improved Neuro-Controller– The update of weights and biases between the output lay

er and hidden layer can be simply expressed as

T

iji oW 122 22 jb

where,

21T

1

2 1Ruu

yQy fT

k

k

kk

:learning rate

:sensitivity

:unit vector

:activation function of the output layer

kk uy 1

12f

Proposed Method



• Improved Neuro-Controller– In the same manner, update of those between the hid

den layer and input layer can be obtained as

T

hihW I11 11 ib

1221 fWT

ji where,

Proposed Method


2006 한국지진공학회 학술발표회한국과학기술원 창의학습관Proposed Method

• Clipped Algorithm– Desired force (by neural network):– Generated force (by MR damper):

df

f

df

f

0v0v

0v

maxVv

maxVv maxVv

maxVv

0v



• Training Data– Filtered artificial earthquake record– Magnitude: scaled to match the maximum acceleration

of the given earthquakes

– Shaping filter

22 2

4)(

ggg

gg

ss

ssF

where,

3.0,2 gg

0 5 10 15 20 25 30-10

-5

0

5

10

Time (sec)

Gro

und

acce

lera

tion

(m/s

ec2

)

Training Neuro-Controller



• Structure of Neuro-Controller– Two neuro-controllers are employed for x and y-dire

ctions, respectively.

bx

bx

8x

bdx

gx

df

Structure of each direction’s neuro-controller



2006 한국지진공학회 학술발표회한국과학기술원 창의학습관Training Neuro-Controller

• Cost Function– In cost function, are included.– Optimal weighting matrices

1510

)001.0,1.0,1.0(

r

diagQ

bb xxx ,,8



• Cost function vs. epoch

– The cost function converges in both x and y-directions, which means the training is successful.

2 4 6 8 10 12 14 16 18 200

500

1000

1500

2000

2500

3000

3500

epoch

Cos

t fun

ctio

n

x-direction

y-direction




• Evaluation Criteria– Based on both maximum and RMS responses of the

building

J1 - Peak base shear

J2 - Peak structure shear

J3 - Peak base displacement

J4 - Peak inter-story drift

J5 - Peak absolute floor acceleration

J6 - Peak generated force

J7 - RMS base displacement

J8 - RMS absolute floor acceleration

J9 - Total energy absorbed

),(ˆmax

),(max

0

01

qtV

qtVJ

),(ˆmax

),(max

1

12

qtV

qtVJ

),(ˆmax

),(max3

qtd

qtdJ

i

i),(ˆmax

),(max4

qtd

qtdJ

f

f

),(ˆmax

),(max5

qta

qtaJ

f

f

),(max

),(max

06 qtV

qtFJ

k

),(max

),(max

ˆ

7qt

qtJ

d

d

),(max

),(max

ˆ8

qt

qtJ

a

a

dtqtUqtV

dtqtvqtFJ

g

kk

),(),(

),(),(

09

Performance Evaluation


2006 한국지진공학회 학술발표회한국과학기술원 창의학습관Performance Evaluation

• Performance Comparison (Case I)

00.20.40.60.81

1.21.41.6

1 2 3 4 5 6 7 8 9

Sylmar

0

0.5

1

1.5

2

2.5

3

1 2 3 4 5 6 7 8 9

El Centro

00.20.40.60.81

1.21.41.6

1 2 3 4 5 6 7 8 9

Rinaldi

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5 6 7 8 9

Jiji

0

0.2

0.4

0.60.8

1

1.2

1.4

1 2 3 4 5 6 7 8 9

Erzinkan

0

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8 9

Kobe

0

0.5

1

1.5

2

2.5

3

1 2 3 4 5 6 7 8 9

Newhall

Passive (Vmax)

Clipped optimal

Proposed


2006 한국지진공학회 학술발표회한국과학기술원 창의학습관Performance Evaluation

0

0.5

1

1.5

2

1 2 3 4 5 6 7 8 9

0

0.20.4

0.60.8

11.2

1.4

1 2 3 4 5 6 7 8 90

0.51

1.52

2.53

3.54

1 2 3 4 5 6 7 8 9

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8 90

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8 90

0.2

0.4

0.6

0.8

1

1 2 3 4 5 6 7 8 9

0

0.2

0.4

0.60.8

1

1.2

1.4

1 2 3 4 5 6 7 8 9

Sylmar El Centro

Rinaldi

Jiji

Erzinkan

Kobe

Newhall

• Performance Comparison (Case II)

Passive (Vmax)

Clipped optimal

Proposed



• Corner drift (normalized by uncontrolled values)

Case I Case II

Passive Clipped Proposed Passive Clipped Proposed

Newhall 1.50 1.28 1.01 1.21 1.11 0.74

Sylmar 0.76 0.96 0.75 0.86 0.90 0.71

El Centro 1.10 0.93 0.57 2.06 1.30 0.91

Rinaldi 0.91 0.89 0.84 0.89 0.93 0.94

Kobe 1.22 1.07 0.80 1.06 1.10 0.85

Jiji 0.80 0.78 0.77 0.96 0.92 1.01

Erzinkan 0.72 0.73 0.65 0.90 0.65 0.74

Performance Evaluation



• A new semi-active control strategy for seismic response reduction using neuro-controller and MR dampers is proposed.

• The proposed strategy was applied to a benchmark building installed with linear elastomeric isolation system.

• In numerical simulation results, the proposed strategy can significantly reduce the floor acceleration, base shear and building corner drift with only a slight increase of the base displacement.

Conclusion



Thank you for your attention!!!



• Sensitivity evaluation algorithm (Kim et al., 2001)• Sensitivity evaluation algorithm (Kim et al., 2001)

BuAzz (9)

In the discrete-time domain,

kkk HuGzz 1

sTs eT A)G(

BA)I()H( -1A sTs eT

(10)

where

represents the sensitivity, .H

jk

k

,

1

u

z

kkk HuGzz 1

State space equation of structure

Documents

MR 댐퍼를 이용한 지진하중을 받는 지진격리 벤치마크 구조물의 신경망제어