2020年 日本建築学会 優秀卒業論文賞・優秀修士論文賞打越 凛太朗...
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CFDLES
PVA
2020-10__.indd 113 2020/09/22 14:20:29
P-Δ
QOL
2020-10__.indd 114 2020/09/22 14:20:29
3
10
CFD
2020-10__.indd 115 2020/09/22 14:20:29
Breathability
2016
1960 1970
2020-10__.indd 116 2020/09/22 14:20:29
1920
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Fig.1
E ud ,max
Fig.2
Fig.2
1 2 w c
dv 3
1
2
w
(s) (mm) (Hz)
12,000
Case
7.07
F Viscoelastic
0z lz 1
2
1C 2C
(4)Excel
=0.45[W/m/K] 3l [mm] 250 [], 10a [] 2001 [W/m2/K] 10002 [W/m2/K] w=5000 [kw/m3]
ABAQUSver2017
/K]=7800[kg/m3]
Fig.5
3)
1 (22)-(26)
a
θ25
Fig.4
0 5
G
Method 6 SDP Short Duration Prediction Method
SSP
(22)-(26) dK ( j )d ( j )
(4) SDP
Short Duration Prediction-Method
SDP SSP
7 SSP SDP C Table 3 1)
= 0.558G = 0.0392
N/mm2aref = 5.6 x 10-3bre = 2.10p1 = 14.06p2 = 97.32
ref = 20 oC= 0.188 N/s/oC SSP
n 0.99<( n )/( n+1 )<1.01
10
TEST
n max
[ (21)]
j SDP
jj SDP
3L 3H 6L 6H
Test SSP c,out c,in
(N/s/mm/oC) (N/s/mm/oC) A-3L 0.021 0.018 A-3H 0.027 0.024 A-6L 0.050 0.047 A-6H 0.040 0.037
Case
Fig.12
2
pp.61-692006.1
pp571-5812015.4
3
Edition 2008.12.25
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a b c d e f g h i j k l m
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Class5 0 0 0 0 99 0
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14
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Nokogiri_Sensor_Y Nomi_Sensor_m6
3
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600
500
400
300
220
120
200
180
160
140
0 50 100 150 200 0 50 100 150 200 Experienced Inexperience Y axis : Time
X axis : Amplitude
15
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Arduino
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Output Gestures with Less Calibration Time, Proceedings of the 29th
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Sensors,ICECC2019,pp.12-15,2019
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-
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BCP
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1
mm 10 m/s
3
200 mm 1:1:2 ( Case1M)
(P0)
3. PIV ( 34 )
PIV
1 2 1) 2)
4 PIV 3 [mm]
Turbulent Intensity/Velocity Ratio[-]
Target Building Model
Dimenssions in [mm]
Camera Frame Size
Pass1 : 32 [pixel]×32 [pixel] Pass2-30 : 16 [pixel]×16 [pixel]
1500 [fps] 1 [s]
(Y) 5, 10, 20, 30 mm
1PIVDavis 8.3
4. ( 34 )
4.1 ( 3 )
5 Case1M
50mm
0.15H(Y=30mm) 2
(Vref)
0.025H
Vref
0.15H -30 X 0Z= ± 30
100
100
Camera Frame Size
Pass1 : 32 [pixel]×32 [pixel] Pass2-30 : 16 [pixel]×16 [pixel]
1500 [fps] 1 [s]
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
Case1M_P0 Case1M_P1
Velocity Contour of Horizontal Section Velocity Vector of Horizontal Section
Reference Vector (5 m/s)
50 40 30 20 10
-40 -30 -20 -10 0 10 20 30 40 X coordinate [mm]
Y c
oo rd
in at
e [m
50 40 30 20 10
-40 -30 -20 -10 0 10 20 30 40 X coordinate [mm]
Building
50-50
50 40 30 20 10
-40 -30 -20 -10 0 10 20 30 40 X coordinate [mm]
Y c
oo rd
in at
e [m
50 40 30 20 10
-40 -30 -20 -10 0 10 20 30 40 X coordinate [mm]
Building
50-50
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(Y=5mm)
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0°
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Line BC
P0
X=2,300 mm,
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3
Smagorinsky 0.1 k- ε
0.0005 sec.
Approach flow
-50
50
30
Pr ob
ab ili
ty d
en si
18 00
Pr ob
ab ili
ty d
en si
18 00
Pr ob
ab ili
ty d
en si
18 00
Pr ob
ab ili
ty d
en si
18 00
Pr ob
ab ili
ty d
en si
18 00
Case1M_P0 0.025H
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9 (Case1M_P0 ) 11 (CFD) 10
Line B (-50 , 0)
-50
-30
-30
-10
-10
0
0
10
10
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Turbulent Intensity
Velocity Ratio
steps (10.0 sec.)
4)
6. ( 6 )
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5)A. Smirnov, S. Shi, I. CelikRandom Flow Generation Technique for Large Eddy Simulations and Particle-Dynamics Modeling, Journal of Fluids Engineering, Vol.123, Issue2, pp359-371, 2001.6
77.7
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75.0
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Two Layer Model of Linear-Log Law Boundary Condition
Total Number of Cells
Outlet Walls
22,000 time steps (11 sec.) 2,000 time steps (1 sec.)
CFD Code
Pre-conditioning Term
1)
212 μmSF 2.2
g/cm3 0.15 μmFAII 2.23 g/cm3 4170 cm2/g
GB 2.91 g/cm3 4230 cm2/g
SP 1.05
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C CO
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3
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10 mm× 20 mm 0.25 mm
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2
3
4
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0 2 4 6 8 10
0 2 4 6 8 10
[m m
0 2 4 6 8 10
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CO SF FA GB TE
CO, GB
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1) FRCC
Vol. 76No. 667pp. 1547-15522011 2)
Vol. 47pp. 6-102019 3) ASTM C 1585-04: Standard Test Method for Measurement
of Rate of Absorption of Water by Hydraulic-Cement Concretes.
4) X.F. Wang et al.: Evaluation of the mechanical perfor- mance recovery of self-healing cementitious materials – its methods and future development: A review, Construction and Building Materials, Vol. 212, pp. 400-421, 2019.
24
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11/15
11/15
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11/16
11/17
11/17
11/18
11/18
11/19
11/19
11/20
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11
2013 10
, pp.95-105, 2017.
[1]fling- step 1
Kojima and Takewaki [2] 1
DI DI
Kojima and Takewaki[3]
DI
2.1
NSDOFm k
yd =y yf kd 2
1 u f
yV = 1 yd 1
0
CASE-A, B, C 3 1
1 2
1
V− 0 O
0t = 1
1( ) ( / ) sin{2 ( / )}a y yu t V V d t T= − (1)
1( ) cos{2 ( / )}au t V t T= − (2) CASE-B 1
1
1(b) OA CASE-A (1), (2) 1 A OAt ( )OAu t t= yd= −
1/ {arcsin( / )} / (2 )OA yt T V V = (3) 1(b)
0 arcsin( / ) / 2yV V (4) A 0t =
yd−
yf−
1pu
max1u−
37
( 0) yu t d= = − 2( 0) ( / ) 1y yu t V V V= = − −
AB 2
d t d
( ) 0b ABu t t= = 2
1 2
y AB
0t =
( 1( 0) , ( 0) 0y pu t d u u t= = − − = = )
1 1 1( ) ( )cos( ) (1 )c y p pu t d u t u = − + − − (8)
1 1( ) {1 ( / )} sin( )c p y yu t u d V t = + (9) 0 C
BCt C ( )c BCu t =
1(1 ) pu− −
1/ 0.25BCt T = (10) C 1T CASE-C 1
0
0
1(c) OA CASE-A, B
1
(A ) (1)-(3)
CASE-B
0 (D ) ADt
( )b ADu t = {1 (1/ )}dy − − 2
1 2
y AD
0 0
2
2 1
1 A , B OAt , OBt
2.3 2 2 0t V
DI 2
CASE - 4 CASE- 1 2
CASE- 1
2 CASE- 1
2
CASE - 3 CASE -
2.2 1
2
2
2 *u
3
2
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
to/T1
0
1
2
3
4
5
CASE-
CASE- CASE-
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
to/T1
0
1
2
3
4
5
1/ODt T 1 1/ , /OP OQt T t T
38
*v
0( )au t 0( )au t
0( )b OAu t t− 0( )b OAu t t−
0( ( ))c OA ABu t t t− + 0( ( ))c OA ABu t t t− +
0t DI
Collapse Pattern 1’-4’ 4
[2]
4 2
1 2
1, 2p pu u
DI
2
m v V ku
+ +
= + + (12)
4(a) 2 (1/ )p yu d= − Collapse Pattern 2’
Collapse Pattern 2’ 1
2 0
2 0.4 = −
2
CASE-CASE-
( ) / 2 { 1 } / 2
{ ( ) / 2} ( ) ( / 2) p
k d u f ku u ku
+ + − −
= − + − +
2 2 2
1 1/ 2 ( / 2) ( / 2)y y p pmV kd kd u ku= + + (14) (14) 1 1pu
2
1 / (1 / )[ 1 1 {1 ( / ) }]p y yu d V V = − + − − (15) 2 2pu 4(b)
2 1/ (1/ ) ( / )p y p yu d u d= − + (16) Collapse Pattern 3’
Collapse Pattern 3’ 1
2
2
2
0
CASE-, 2
2
(a) Collapse Pattern 1’ (b) Collapse Pattern 2’ (c) Collapse Pattern 3’ (d) Collapse Pattern 4’
4 1 2
B
OO
P
f
u
f
u
1pu
O
f
u
1( ) / 2y pk d u− 2
[3] DI
DI
DI
5 -
DI
7
-
4
2 [3]
DI
[1] Kalkan, E. and Kunnath, S.K. (2006). Effects of fling
step and forward directivity on seismic response of buildings, Earthquake Spectra, 22(2), 367–390.
[2] Kojima, K and Takewaki, I. (2015). Critical earthquake response of elastic-plastic structures under near-fault ground motions, Frontiers in Built Environment, 1: 12.
[3] Kojima, K and Takewaki, I. (2016). Closed-form dynamic stability criterion for elastic-plastic structures under near-fault ground motions, Frontiers in Built Environment, 2: 6.
[4] Jennings, P. C., and Husid, R. (1968). Collapse of yielding structures during earth-quakes, J. Eng. Mech., 94, 1045-1065.
0 4 = − .
Pattern 1’
Pattern 4’
1 1/ , /OP OQt T t T
Collapse Pattern 1’ Collapse Pattern 2’ Collapse Pattern 3’ Collapse Pattern 4’
Non-collapse
Collapse
-1
-0.5
0
0.5
1
-1 -0.5 0 0.5 1 u/dy
-1
-0.5
0
0.5
1
-2 -1.5 -1 -0.5 u/dy
-1
-0.5
0
0.5
1
-1
-0.5
0
u/dy
-1
0
1
2
zzzz
y
-1
-0.5
0
0.5
1
zzzz -1 0 1 2
u/dy
-1
-0.5
0
0.5
1
0 4 = − .
4) B C 2
2007
1 2 a)
0.0
0.4
Subject B Subject C
-5
0
-0.4
0
0.4
-0.4
0
0.4
-1.5
0
1.5
0.4
-0.4
0
0.4
-0.4
0
1.5
-1.5
0
-5
0
5
Ac cel
era tio
n ( m/
1
Force plate (1mx1m) Shaking table
X Z
C B CoP
B, C CoP
1.0Hz
0.7Hz B 1.2Hz
B
2
0()()
2 1 2 1 2
1 2 1 2 1 2
1 2 1() 2() t 1 2
+ 32() cos 2() − 21()2 sin 1()
− 32()2 sin 2() + ()
(1)
+ 52() cos1() − 2() − 7 sin 1()
+ 52()2 sin(1() − 2())
(2)
+ 51() cos1() − 2() + 62()
(3)
2 1~8
42
- 3 -
C
ξ()
2
-0.4
0
0.4
-1.5
0
1.5
-0.4
0
0.4
-0.4
0
0.4
-1.5
0
1.5
Analysis Experiment
Analysis Experiment
Analysis Experiment
Analysis Experiment
Analysis Experiment
Analysis Experiment
c) Velocity of Head
b) Displacement of Head
a) Displacement of CoP
c) Velocity of Head
b) Displacement of Head
a) Displacement of CoP
0 10 20 30 40 0 10 20 30 40 Time (s) Time (s)
- B C 3000 7190 -78200 -37400 -20100 -16000 -11300 -14500 -17800 -15100 -6510 -4600 -54.6 -61.6 -89.0 19.3 119 183 -16.8 -24.1 -27.8 -25.4 28.7 32.9
5 B, C
7
B: 2 a 8 C: 2 a
6 TVR
2011
9
1 ~ 4 10 Site 1
1.0Hz
C
.
2016
3) : 28 (2016 )
. 2016
5) Winter, D. A. : Biomechanics and Motor Control of Human
Movement, 4th Edition, 2009. 10
6) D. Gordon E. Robertson, Graham E. Caldwell, Joseph Hamill,
Gary Kamen, Saunders N. Whittlesey: Research Methods in
BIOMECHANICS, 2nd Edition, 2013
“http://www.kyoshin.bosai.go.jp/kyoshin/”, (2020-3-6 )
Maximum head velocity of subject B (m/s)
The Western Tottori prefecture earthquake in 2000 The 2011 off the Pacific coast of Tohoku Earthquake The Niigataken Chuetsu-oki Earthquake in 2007 The 2016 Kumamoto Earthquake
Site 1 (FKS020)
Site 2 (KMM007)
Site 3 (FKS018)
Site 4 (NIG025)
m /s)
Frequency (Hz)
a) Site 1 (FKS020) NS EW b) Site 2 (KMM007) NS
EW
9 10 Site 1 ~ 4
44
- 1 -
1
5 250 1)
10m 2 2018 5 5 1)
2020
2)
40%
4-2
3 5 HM 4HM HM 2 5 HM HM
100
5-2
7 24
2
4 HM
2
5
271 33% 130 16% 88 10%
483 392 81%
50
- 3 -
5No1No 2 3No9No 10No13 1 5
No9No10 No13 3/4 No 11No12 1/2 5
6 2)
53 1 121 3792 255 3 112 746 92%
134 107 104 91 47
3
25 20%
6-2
7 65 686 60
3
6
7
5 HM
HM
5
2) 5 HM
https://www.city.koto.lg.jp/057101/bosai/bosai- top/topics/documents/honpen.pdf,2018.8
0.0m1 0.5m 1
52
- 1 -
1_
408,501 33 10
4)
5 39 12 36
5 10 6)
41 7 6
10
41 9
(2 )(1 )
3
26
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3_
3
34 35 5 1
36 5
1112 37
3 2 37
1
40
40 6 10 41 2
2
40 11 41 2
37 38 5
41 6
4
3
54
- 3 -
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11 ( 17-N)
10 ( 17-V
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15
1.
1 12
37 4 48 31 12 28% 3% 37% 23% 9%
C 10
8~9
ABCD 4~7
0~3
3m
3m
B-1 B-3
C
23(17%)
8
7
B-2 B-3
X
Z
A-1 A-2 A-3
37 4 48 31 12 28% 3% 37% 23% 9%
C 10
8~9
ABCD 4~7
0 100M
B-1 B-3
C
Z3
×
3 6 9 12 14
3 6 9 12 14
0 3 6 9 12 14
3 6 9 12 14
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3 6 9 12 14
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3 6 9 12 14
r=0.72 r=0.85
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N
C1
C2
C3
C4
C7
C8
C9
C10
C11
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C6
1.2 1.4
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
0.8 0.8
FL+600
FL+1200 FL+600 FL+1200 FL+600 FL+1200 FL+600 FL+1200
FL+600
FL+1200 FL+600 FL+1200 FL+600 FL+1200 FL+600 FL+1200
[ m in /m
3 ]
0
200
400
600
800
1000
1200
1400 [ m in /m
3 ]
(m m
(m m
Ce
Cu
Cd
C2
C3
D
Qp(y)h QE(y)l Qu(y)j Qd(y)k
[m2/s] [m3/s] [m] [m] [m] [m] [1/m] [m3/s] [m3/s] [m3/s] [m3/s]
[-] [-] [-] [-] [-] [m2/s]
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(mm) (mm) (mm)
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8 0.8 0.8
6 0.6 0.6
4 0.4 0.4
2 0.2 0.2
0 0 0 50100 200 400 50100 200 400 50100 200 400
(mm) (mm) (mm)
10 1 1
8 0.8 0.8
6 0.6 0.6
4 0.4 0.4
2 0.2 0.2
0 0 0 50100 200 400 50100 200 400 50100 200 400
(mm) (mm) (mm)
2.0 2 2
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150
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0 50 100 200 400 50 100 200 400 50 100 200 400 50 100 200 400 50 100 200 400 50 100 200 400
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16 8 78 83 98 26 3
8
1) ,
, 1974
1
2 SMGA (Rupture Starting Point: RSP) Multi hypocenter model
(Seg.H)
(Seg.I)
10)LMGA
2 3
500 m
1
6 Gauss
11)
K-NET 6 KiK- net 4 2
7 ,JR 2 21
) EW 1
93048 () EWUD 3
NS 1
3
KMM006 (K-NET )
11)
12)
(h=5%)KMMH16 EW
SMGA LMGA
SMGA3 SMGA5
FP Directivity Pulse 93048 EW
LMGA3 SMGA6 PSV
SMGA6 1.1 3
LMGA3 SMGA6
6
13)KMMH16
PSV 600 cm/s
0
100
200
0 10 20 0 10 20 0 10 20 0 100 200 300 400 500
0 100 200 300 400 500 600
0
100
0 10 20 0 10 20 0 10 20 0 100 200 300 400 500
0 100 200 300 400 500 600
0
100
200
300
0 10 20 30 0 10 20 30 0 10 20 30 0
100
200
300
0
100
200
300
0
100
200
0 10 20 0 10 20 0 10 20 0
100
200
300
0
100
0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0
100
0
100
0
50
0 10 20 30 0 10 20 30 0 10 20 30 0
100
200
0
100
200
0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0
100
200
300
0
100
200
300
400
0
100
0 10 20 0 10 20 0 10 20 0
100
200
0
100
200
0
100
0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0
100 200
300
400
0
100
0 10 20 30 0 10 20 30 0 10 20 30 0
100 200 300 400
100 200 300 400
100
200
300
PSV (cm/s) UD
KMMH16
93051
93048
93006
93011
EED
KMM004
KMM006
MYJ
TTN
(a) (b) PSV (h=5%) 4
(b)
32.8
33.0
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5(m)
131.0
131.0
131.0
131.0
32.8
33.0
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5(m)
131.0
131.0
131.0
32.8
33.0
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5(m)
32.8
33.0
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5(m)
131.0
131.0
131.0
20 km
SMGA LMGA
8 Y=-500 m-30 kmX30 km 1 km
X FP Y FN
LMGA WLMGA=2 km
3
X (km) (s)
9 9
SMGA LMGA
FN SMGA
100 200 300 400 500 600 700
PSV (cm/s)
9 PSV
(a) (b) LMGA (c) SMGA
0
1
2
3
4
5
10 0
100200300 400
102
103
0
1
2
3
4
5
100
101
102
103
0
1
2
3
4
5
102
103
0
1
2
3
4
5
100200
400
101
102
103
X (km)X (km)X (km)
Edges of seismic fault model Edges of SMGA and LMGA
5 SMGALMGA (a) KMMH16 EW (b) 93048 EW
(a1) (a2) (a3) PSV (h=5%) (b1) (b2) (b3) PSV (h=5%)
0 10 20 0 10 20 0
100
200
300
400
500
0 1 2 3 4 5 time (s) time (s) period (s)
h=5%
-146
-80
44
125
12
-97
119
106
100
200
300
400
500
600
0 1 2 3 4 5 time (s) time (s)
Obs.
Syn.
LMGA2
LMGA3
SMGA4
LMGA6
SMGA6
Obs.
Syn.
LMGA2
LMGA3
SMGA4
LMGA6
SMGA6
258
291
19
91
76
20
202
175
197
28
75
20
24
92
PSV (cm/s)
PGV (cm/s)
PGD (cm/s)
PGV (cm/s)
PGD (cm/s)
PSV
1
PSV (h=5%)
11 11
1
FP
X= -5000 m-10000 m 2 FN
1
1 1
FN
(K-NET KiK-net) GMT 2 (ALOS2)PALSAR-2 JAXA 1) Shirahama et al., Earth, Planets and Space, Vol. 68:191, DOI:10.1186/s40623-016-0559-1, 2016.10 2) 13 26 pp. 451-456 2007.12 3) 17 35 pp. 55-592011.2 4) Okada, Y, Bull. Seismol. Soc. Am., Vol. 75, No. 4, pp. 1135-1154, 1985. 5) Hisada. Y and J. Bielak, Bull. Seismol. Soc. Am. Vol. 93, No. 3, pp. 1154-1168, 2003. 6) 20 1 () pp. 118-1322020.1 7) Ozawa et al., Earth, Planets and Space, Vol. 68: 186, 2016.11 8) Yoshida et al., Earth, Planets and Space, Vol. 69:64, 2017.5 9) 84 757 pp. 373-3832019.3 10) 2Vol. 53No. 1pp. 1-92000.7 11) 47 (2019)pp. 11-242019.11 12) http://maps.gsi.go.jp/#11/32.926284/130.735931/&base=std&ls=std% 7Curgent_earthquake_20160414kumamoto_p023dr_p135ar_qe_25d% 2C0.7%7C20160414kumamoto_epicenter%7Curgent_earthquake_qeg rd_cont10cm_cut10%2C0.7&blend=0&disp=1111&lcd=urgent_earthq uake_20160414kumamoto_p023dr_p135ar_qe&vs=c1j0h0k0l0u0t0z0 r0s0m0f1&d=vl 13) 2016.9
11
(a) FP (b) FN
Lower depth of LMGA
0
1000
2000
0
1000
2000
Depth (m)
VS (m) VS (m) VS (m)
TL M
TL M
Depth (m)
Depth (m)
1
2
3
4
5
0
100
200
300
400
500
600
700
1
2
3
4
5
0
100
200
300
400
500
600
700
1
2
3
4
5
0
100
200
1
2
3
4
5
0
100
200
1
2
3
4
5
0
100
200
X=0 m
X=-5000 m
X=-10000 m
PSV (cm/s)
PSV (cm/s)
Time (s) Period (s) Period (s) Period (s)
Full method Substructure method Outcropped seismic bedrock motion
X=0 m
X=-5000 m
X=-10000 m
PSV (cm/s)
PSV (cm/s)
Time (s) Period (s) Period (s) Period (s)
Transfer function
2)
1 5m×
3m 1(a) 8.0×3.0m× 3.0m 1(d) 8m 4) U 1(b) 1(c)
JMA 50% Y 20gal 2
ARX 2 1
0.0020.017
2
700mm 500mm200mm 3 3 6 4
L 2
90% 40 T1T4
4 TN1TN3 3 p
Mp
M - 4 0.05[rad] 0.10[rad] 10% 90% T2
0.027[rad] 0.056[rad] 20A 0.02[rad] 20A p
2.3 20Ap 2 5) 32A
M -θ
36 0.076
[Hz]
[-] 30.02 0.012 ( 100A) 11.73 0.002 ( 25A) 5.90 0.010 ( 100A) 7.60 0.008 ( 100A) 14.22 0.011
( 40A) 7.36 0.017 ( 25A) 8.41 0.017 ( 20A) 9.15 0.006
( 9.52mm/12.7mm) 4.40 0.005 ( 40A) 10.37 0.003
JMA 50% 5
0% 0.000 0.000
100%75% 0.000
0.001 0.005ARX 0.000 0.001 ARX
0.005
75% 0.005 25A 40A 20A 7
-1000
0
1000
0
1000
-1000
0
1000
0
1000
-500
-250
0
250
500
T1
T2
T3
T4
TN1
TN2
TN3 M [N m ]
[rad] M θ
8 40A 9 A-A’B-B’
10
JMA 100%JMA 4FL50%JMA 4FL 11
1
p
1) 2016 2018
2) 59 pp.65- 682019.6
3) ARX
508pp47-541998.6 4) III917-9182019.9
5)
2020.9
9 Large-eddy Simulation(LES)
() 3)([] )
6)Breathability
Breathability
flux Breathability
K 2
k 3
K 4
k (2)
[]
= ⟨3 ⟩ + ⟨3 ⟩ + ⟨′3 ′⟩⟨⟩ + ⟨′3 ′′⟩ 2⁄ (1) = ⟨1 ⟩ + ⟨1 ⟩ + ⟨′1 ′⟩⟨⟩ + ⟨′1 ′′⟩ 2⁄ (2)
(A-A’)
Case1H
3 (Case2HCase3HCase5H) 4
LES
9)10)
3 ( 1.5m)
Ves ([Ves]) [Ves] Case1H [Ves]
Case1H
Ves
Ves
1 C.V.(Canopy)(Breathability)
K,
( 5(1))
() = ⁄ (8) C.V.(Canopy)
1
A-A’
(1) (2) (1) (2) 4 Case5H 5 A-A’ Case5H
(1) (2)⟨3 ⟩ (3)⟨3 ⟩ (4) ⟨ ′3 ′⟩⟨⟩ (5) ⟨
′3 ′ ′⟩/2
6 C.V.(Canopy) Case1H Evt
(1) (2)⟨3 ⟩ (3)⟨3 ⟩ (4) ⟨ ′3 ′⟩⟨⟩ (5) ⟨
′3 ′ ′⟩/2
- 3 -
6(1)Case1H
1 C.V.(Canopy)
⟨3 ⟩ 11.84 21.0%
39.545 70.1%
38.14 55.8%
36.48 41.8%
39.40 35.5%
′⟩/2 3.791 6.7%
1
2
(Case1H ) Case1H Case2H Case3H Case5H 1.00 1.21 1.55 1.97 1.00 1.37 2.61 7.02 1.00 0.88 0.59 0.28
OCAEL ,
Naoki Ikegaya et al. Theoretical and Applied Climatology, 127, 655-665, 2017.2 ,,,33 ,C08-3,2019 Hideki Kikumoto et al.Journal of Wind Engineering and Industrial Aerodynamics, 173, 91-99, 2018.2 ()- -, , 1993 Yasuyuki Ishida et al.Journal of Wind Engineering and Industrial Aerodynamics, 183, 198-213, 2018.12 Marina K.-A. Neophytou et al.Proceedings of the 5th GRACM Inter Congress on Comput Mech,,967-974,2005.6 Negin Nazarian et al.9th International Conference on Urban Climate jointly with 12th Symposium on the Urban Environment,1-6,2016 Ioannis Panagioyou et al.Science of the Total Environment, 442,466-477,2013 ,25, 211-216, 2018 Tubasa Okaze, Mochida AkashiComputer and
Fluids,159,23-32, 2017.12 k K
(K+k) C.V.
OpenFOAM-ver4.1 SGS Smagorinsky 1500m 20 2 2 (95%) 1 (5%) 2 PISO Slip Spalding 240 150
f, <f> f , f , f’(=f<f>), ui 3 [m/s](i=1:,i=2:,i=3: ),ε[m2/s3], K[m2/s2] (=1/2ui2), k[m2/s2] (=1/2 ui′2), Veseffective speed, Ves = √⟨(⟨ui⟩ + ui′)2⟩ = √2K + 2k [m/s] H[m](=30m) EinUpC.V.(Upper) EoutUpC.V.(Upper) EinCC.V.(Canopy) EoutCC.V.(Canopy) ∫C.V.(Upper) C.V.(Canopy) εUpC.V.(Upper) εCC.V.(Canopy)
0 30 60 90 120 150 180 210
0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012
[m
10
S A B 2
A 1 1
4
1989 1
15
3
/
19741988
708
421 59.5
4
4
1977 22
GS FC 70
1970 -80
S B
2
K F () B
BK BF B
8 1974 ()()()
B 1974
8
1980 THES(Total House Element System)
THES
10 THES ()
3(1986)
STHES
1973
/
.1983 ./2.
... 1980 8 ./ 4..
,, 1999 ./5.
20 ,1983 .
92
- 1 -
1 2
7 6
4)
Classification
AI
2nd system in 2013 Image diagnosis
system
Dweller
Inspector
Quantitative damage assessment
Estimation
Image measurement of residual deformation
Approximately 2×Residual deformation
3)
Segmentation10)
5
2016
SV16 Seg-Net
Dataset Image types Wall types Damage types
RMC Real
DLR50RMC
SV19CMC
SV19RMS
DLR50CMS
DLR50CBC
94
- 3 -
= (4)
Original
Image
Damage
Extraction
result
(%) (%)
rig in
al im
ag e
Ex tra
ct io
24.9 26.0
17.9 44.5
2) : 2016. 5
3) : 2013.6
4) : 2007 No.22, pp.35-38, 2008. 5
5) : Vol.11, pp.12-21, 2014. 4
6) : 15 GO07-01-10, pp.1267-1274, 2018.12
7) : 15GO11- 01-07pp.1874-18822018.12
8) Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton.: Imagenet classification with deep convolutional neural networks., NIPS'12: Proceedings of the 25th International Conference on Neural Information Processing Systems Vol. 1, pp.1097-1105, 2012. 12
9) Girshick, R., J. Donahue, T. Darrell, and J. Malik.: Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation., Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, pp. 580-587, 2014. 6
10) Chen, Liang-Chieh et al.; Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation., ECCV (2018) arXiv: 1802.02611v3, 2018. 8
11)
C-1, pp. 93-94, 2018. 9 12) :
2010. 1 13) :
1 -, pp.581-582, 2014. 7
14) : 3 1
91 pp.167-170, 2018.6
15) : 3 E- 1
pp. 231-232, 2018. 7 16) :
IC 1840, pp.829-834, 2012. 10
Table 6 Evaluation results in the 2019 Yamagata earthquake Camera Original images Extracted damages (%) Inspectors Error rate Drift ratio
Smart phone
1.
-400×400×16 (BCR295)
H-500×200×10×16 (SN490B)
2.3
× × × 1970 0.04 rad1 0.05 rad1 0.03 rad2 0.04 rad2 0.05 rad2 × () × () ()
ΣE [kNmrad] 138.5 286.5 153.0 316.2 565.1 Σθp [rad] 0.185 0.352 0.179 0.312 0.616
η 19.0 36.0 25.1 50.0 72.5
2
ΣE[kNmrad] Σθp[rad] η
1 (3)
1-6 7-12 13-18 19-22 23-24 25-26 27-28 29-30 31
(rad) 0.0035 0.005 0.0075 0.01 0.015 0.02 0.03 0.04 0.05
97
- 2 -
1970
OpenSees
Model) b
(Tuned Bilinear Model)
c (Stiffness
3
c
3.
[rad] 0.060.040.020-0.02-0.04-0.06
4 ( –)
(a) Design Model (b) Tuned Bilinear Model (c) Stiffness Deterioration Model (d) Full Deterioration Model
0 0.2
0 0.2
5
0.5
1.0
1.5
6
Model
a) Design × b) Tu ned Bilinear × c) S tiffness Deterioration
d) Full Deterioration
98
- 3 -
12
13
4
0.1) (model-3)
R2 = 0.008
model-1 model-2
Step
5 [m]
1
13
( 18)
19
pp.19-322015.9
that incorporate strength and stiffness deterioration,
Earthquake Engineering and Structural Dynamics, Vol.34,
pp.1489–1511, 2005.6
-E-
-
W
H
θR
4000
3000
2000
1000
. .
1860
Harrods20 Whiteleys 19 grocer
1894 1912
Joseph Appel 1906
. 19
.
Bazaar 18 19
1851 1862 19
1870 Mail Order
1901-1906 light well 2
. 20 19
Sigfried Giedion
.
.
1922~3 1934
103
- 4 -
5 28 Cubical Extent 75 76
1855 Metropolitan Building Act
. 53 1
55cm
Selfridges LCC(London County Council
:GLC)
Jan Whitaker, The Department Store HistoryDesignDisplay, 2011.
Philip Steadman, The changing department store building, 1850 to
1940, 2014.
Tim Dale, Harrods : a Palace in Knightsbridge, 1995
1990
1997
1
1.1
36.
()
2.2
9
10
2
(2018 9 3)
(SB )( SB )
1 16
2 3.2
35 2.5
4.( 2)
4.1
4.4
A ()A B ()B ()
1 MT MB SB SB ST ST
)
106
- 3 -
4.5
A ()A B ()B ()
4.6
()
()
1920
6
25
pp.188-190, pp.476-468
)
2()
1872 A
()A
()A
()
()A
()A
()A
()A
()A
1890 B ()B
()B
()B
()
()B
1904 A
()
()A
()B
1910 B ()B
()B
()B
1913
()
()B
()B
()
()
()
1.
PSS PSS 1)
150[S/m] PEDOT/PSS( P/P150)PSS P/P 30, P/P 10 PSS 2.2
P/P150 ( 2) P/P10 ( 3)
2.4
.1 (: )
.2 .3
10 (Quantachrome : VSTAR) 1 CASE PSS 4 25,50,75[] 3 11~13 P/P150,30,10,PSS 4
0.150.6 PSS 3 P/PPSS
3.2 2),3),4)
T[K] P[][3][3] L[kg2 2⁄ ](3-1)
Clausius-Clapeyron 14 14
PSS P/P10, PSS 2
.4 SEM .5 SEM
.6 .7
30[S/m] PEDOT/PSS
10[S/m] PEDOT/PSS
PSS
=
18.23
34.29
0
10
20
30
40
[Ω ]
7,638.89
1,072.92
0
2000
4000
6000
8000
10000
[ 2⁄ ], t[] [ ⁄ ], [ 2 ⁄ ],
[]
P/P10 PSS
PEDOT/PSS PSS
(4-1)
.14 .15 .16
0
200
400
600
800
1000
1200
1400
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
g]
[-]
150[S/m] 25 30[S/m] 25 10[S/m] 25 PSS 25
0
200
400
600
800
1000
1200
1400
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
g]
[-]
150[S/m] 50 30[S/m] 50 10[S/m] 50 PSS 50
0
200
400
600
800
1000
1200
1400
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
g]
[-]
150[S/m] 75 30[S/m] 75 10[S/m] 75 PSS 75
40
42
44
46
48
50
0
0.000005
0.00001
0.000015
0.00002
0.000025
k[ kg
/P a
m 2
0
0.00002
0.00004
0.00006
0.00008
k[ kg
/P a
m 2
111
- 4 -
…(4-1)
D[], ρa[ 3⁄ ], Q[3 ⁄ ], t[s] [ ′⁄ ]
3 CASE 22 CASE:A,B CASE:A CASE:B 4.2
18CASE:A,B
(CASE:A-6,9, B-3,6,12) 19 > (CASE:A-3,B-9)
CASE:B CASE:A PSS
,
2)., , , 2002.6 3). 2, 2, , 2010.10 4)., , 1977.8 5). 4, , , 2006.11 6)., S , Vol.26 No.4, pp. 469-479, 2009.7 7). ( 3) , , 2007, pp.1007-1010
.17
.2
2.5 CASE
3 () 28 50 11.8 () 50 15 11.8
.3 CASE
CASE
150 0.14 14 3 3 CASE:A-2 150 0.14 14 3 2 CASE:A-3 150 0.14 14 3 1 CASE:A-4 150 0.14 14 2 2 CASE:A-5 150 0.14 14 2 1 CASE:A-6 150 0.14 14 1 1 CASE:A-7 10 0.14 14 3 3 CASE:A-8 10 0.14 14 2 2 CASE:A-9 10 0.14 14 1 1 CASE:B-1
150 0.42 0 3 3 CASE:B-2 150 0.42 0 2 2 CASE:B-3 150 0.42 0 1 1 CASE:B-4 10 0.42 0 3 3 CASE:B-5 10 0.42 0 2 2 CASE:B-6 10 0.42 0 1 1 CASE:B-7 150 0.42 14 3 3 CASE:B-8 150 0.42 14 3 2 CASE:B-9 150 0.42 14 3 1
CASE:B-10 150 0.42 14 2 2 CASE:B-11 150 0.42 14 2 1 CASE:B-12 150 0.42 14 1 1 CASE:B-13 10 0.42 14 3 3
.18 CASE
.19 CASE
A- 1
A- 2
A- 3
A- 4
A- 5
A- 6
A- 7
A- 8
A- 9
B- 1
B- 2
B- 3
B- 4
B- 5
B- 6
B- 7
B- 8
B- 9
B- 10
B- 11
B- 12
[% ]
112
Tab.1
Fig.2 nac EMR9
Fig.1 2013
1960 1 1.2
1-1 1-2
2 360 3-1 3-2,3-3 3-4 1.3
2 3
1.4 2 2.1 Fig.1
2.2
2019/9/21 24 28 20
2
3-3
Tab.1
Fig.3
0
100
200
300
400
500
600
17
Fig.4 17
0
12
24
36
48
60
72
0
100
200
300
400
500
600
17
Fig.5 17
Fig.6 RICOH THETA S
Fig.7 Tobii Eye Tracker 4C
Fig.8 PC 360 Eye Tracker 4C 1/90
Fig.9 PC360
3.2 Fig.3
1-1 3.3 Fig.4Fig.5
EMR9 1-2 3.4
4 360 4.1 360
RICOH THETA SFig.6 360 PC 4.2 Tobii Eye Tracker 4CFig.7 4.3 4.4 Unreal Engine 4Fig.8
Unreal Engine 4(ver. 4.22.1) PC 3601/60 4.5
Fig.9
4 PC 2 0.2 4.6 4.7 EMR9 AR 2 Python 3.7.3
114
01 02
03 04
05 06
Fig.11 360 01 0601 06
B01 B04 B07 B08 B09 B10 B17
01 6 5 5 6 5 5 6
02 3 4 4 4 1
3
2
04 4 3 2 3 4
4
4
03, 0601, 05
B11 B13 B14 B16 B21 B22 B26
01 3 2 2 3 3 1 1
02 2 1 1 2 2 2 2
03 1 3 6 4 5 4 4
04 6 5 5 5 6 5 6
05 5 6 4 6 4 6 3
06 4 4 3 1 1 3 5
0204
B02 B12 B15 B19 B20 B23 B24
01 3 3 3 1 1 2 1
02 5 2 4 5 4 5 4
03 1 4 1 3 3 3 2
04 2 1 2 2 2 1 3
05 4 6 5 4 5 6 5
06 6 5 6 6 6 4 6
0405, 06
01 5 5 5 6 6 4 2
02 4 3 1 4 4 3 3
03 2 1 4 3 2
2
5
05 3 2 2 1 3
1
1
0506
4
4
Fig.12
B08, B10, B13, B16
04, 05 05, 06
B25
02, 03, 06 01, 04, 05
Tab.4
- 3 -
5 5.1 Fig.10 2019/12/3 28
5.2 28 Tab.2Tab.3 5.3
19995 Fig.12 Tab.4 3-1
Fig.11
01 03 20 01 03
01 06
04 06 20
20
Fig.14 12 12
91.7%
02
08 92.3%
03
09 80.0%
04
10 78.3%
05
11 64.0%
06
12 95.8%
Tab.5
Fig.15
0.00% 1.22% 10.98% 12.80% 16.46% 0.00% 16.46% 7.93% 34.15% 0.00% 0.00% 0.00% 1/3 0.00% 4.35% 6.52% 13.04% 10.87% 0.00% 8.70% 10.87% 45.65% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 13.33% 14.67% 21.33% 0.00% 13.33% 4.00% 33.33% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 7.94% 12.70% 9.52% 0.00% 23.81% 14.29% 31.75% 0.00% 0.00% 0.00%
12
0.00% 2.74% 12.33% 17.81% 17.81% 0.00% 8.22% 5.48% 35.62% 0.00% 0.00% 0.00% 1/3 0.00% 8.33% 12.50% 12.50% 0.00% 0.00% 8.33% 12.50% 45.83% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 15.79% 18.42% 23.68% 0.00% 5.26% 5.26% 31.58% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 0.00% 22.58% 12.90% 0.00% 12.90% 9.68% 41.94% 0.00% 0.00% 0.00%
12
0.00% 0.00% 11.54% 8.97% 17.95% 0.00% 21.79% 8.97% 30.77% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 3.13% 6.25% 28.13% 0.00% 12.50% 9.38% 40.63% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 11.11% 8.89% 22.22% 0.00% 17.78% 6.67% 33.33% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 11.63% 2.33% 16.28% 0.00% 25.58% 11.63% 32.56% 0.00% 0.00% 0.00%
12
Tab.6 12 12
-1.68 5.51 -8.38 0.04 1.30 2.81 -2.42 -1.27 -5.06 1.27 -2.53 1.27 1.27 10.13 0.96 -0.27 -1.10 9.20 -13.05 -7.55 9.20 -0.96 -0.10 -3.15 -9.28 16.07 7.40 -9.70 0.00 4.90 -2.99 6.06 2.40 -3.13 -8.49 0.00 -5.98 -0.91 0.87 1.06 -4.66 9.36 0.00 -0.30 3.81 -10.04 -9.07 1.46 13.10 0.00 0.45 -7.71 1.27 12.86 -2.99 -9.89 1.08 -1.08 5.38 -5.38 -1.08 1.08 -2.15 -1.08 1.51 -8.03 12.06 -7.59 0.51 4.33 0.00 -4.35 6.81 1.62 10.46 4.64 -12.32 0.00 0.00 -5.40 -1.97 -11.81 10.48 -1.59
0.06 0.74 0.59 0.25 -1.50 -0.12 -0.51 -0.55 -1.53 -3.99 0.07 1.97 2.00 0.44
12
Tab.5 3-2,3-3 6.3
Tab.6Fig.14 Tab.7 3-4
Fig.15
1 2
18 39 , 2016 , pp.667-672
3 " " " " 35 , 2012, pp.103-108
4 , 2014 , pp.611-612
5 518 , 1999 , pp.75-80
116
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24 pp.353-3612007.
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0.82 0.82 0.76 0.76
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Existing matching priority Green coverage complementarity priority
0.00
0.25
0.50
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19897 2020 31 12 27 12019123 20203202020 1 2202042 188 5
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Breathability
2016
1960 1970
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Fig.4
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SSP
(22)-(26) dK ( j )d ( j )
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Short Duration Prediction-Method
SDP SSP
7 SSP SDP C Table 3 1)
= 0.558G = 0.0392
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ref = 20 oC= 0.188 N/s/oC SSP
n 0.99<( n )/( n+1 )<1.01
10
TEST
n max
[ (21)]
j SDP
jj SDP
3L 3H 6L 6H
Test SSP c,out c,in
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Fig.12
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1.
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14
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2
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Nokogiri_Sensor_Y Nomi_Sensor_m6
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700
600
500
400
300
220
120
200
180
160
140
0 50 100 150 200 0 50 100 150 200 Experienced Inexperience Y axis : Time
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Arduino
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Output Gestures with Less Calibration Time, Proceedings of the 29th
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BCP
1/200
1/4.8
1
mm 10 m/s
3
200 mm 1:1:2 ( Case1M)
(P0)
3. PIV ( 34 )
PIV
1 2 1) 2)
4 PIV 3 [mm]
Turbulent Intensity/Velocity Ratio[-]
Target Building Model
Dimenssions in [mm]
Camera Frame Size
Pass1 : 32 [pixel]×32 [pixel] Pass2-30 : 16 [pixel]×16 [pixel]
1500 [fps] 1 [s]
(Y) 5, 10, 20, 30 mm
1PIVDavis 8.3
4. ( 34 )
4.1 ( 3 )
5 Case1M
50mm
0.15H(Y=30mm) 2
(Vref)
0.025H
Vref
0.15H -30 X 0Z= ± 30
100
100
Camera Frame Size
Pass1 : 32 [pixel]×32 [pixel] Pass2-30 : 16 [pixel]×16 [pixel]
1500 [fps] 1 [s]
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
-50
-10 -20 -30 -40
Case1M_P0 Case1M_P1
Velocity Contour of Horizontal Section Velocity Vector of Horizontal Section
Reference Vector (5 m/s)
50 40 30 20 10
-40 -30 -20 -10 0 10 20 30 40 X coordinate [mm]
Y c
oo rd
in at
e [m
50 40 30 20 10
-40 -30 -20 -10 0 10 20 30 40 X coordinate [mm]
Building
50-50
50 40 30 20 10
-40 -30 -20 -10 0 10 20 30 40 X coordinate [mm]
Y c
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in at
e [m
50 40 30 20 10
-40 -30 -20 -10 0 10 20 30 40 X coordinate [mm]
Building
50-50
18
(Y=5mm)
P1 Line C Y=30 mm
0°
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0.05H
Line BC
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X=2,300 mm,
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3
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Approach flow
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Pr ob
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Pr ob
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18 00
Pr ob
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18 00
Case1M_P0 0.025H
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8 (PIV)
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0
0
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Turbulent Intensity
Velocity Ratio
steps (10.0 sec.)
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Two Layer Model of Linear-Log Law Boundary Condition
Total Number of Cells
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CFD Code
Pre-conditioning Term
1)
212 μmSF 2.2
g/cm3 0.15 μmFAII 2.23 g/cm3 4170 cm2/g
GB 2.91 g/cm3 4230 cm2/g
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1116
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11/17
11/17
11/18
11/18
11/19
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2013 10
, pp.95-105, 2017.
[1]fling- step 1
Kojima and Takewaki [2] 1
DI DI
Kojima and Takewaki[3]
DI
2.1
NSDOFm k
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V− 0 O
0t = 1
1( ) ( / ) sin{2 ( / )}a y yu t V V d t T= − (1)
1( ) cos{2 ( / )}au t V t T= − (2) CASE-B 1
1
1(b) OA CASE-A (1), (2) 1 A OAt ( )OAu t t= yd= −
1/ {arcsin( / )} / (2 )OA yt T V V = (3) 1(b)
0 arcsin( / ) / 2yV V (4) A 0t =
yd−
yf−
1pu
max1u−
37
( 0) yu t d= = − 2( 0) ( / ) 1y yu t V V V= = − −
AB 2
d t d
( ) 0b ABu t t= = 2
1 2
y AB
0t =
( 1( 0) , ( 0) 0y pu t d u u t= = − − = = )
1 1 1( ) ( )cos( ) (1 )c y p pu t d u t u = − + − − (8)
1 1( ) {1 ( / )} sin( )c p y yu t u d V t = + (9) 0 C
BCt C ( )c BCu t =
1(1 ) pu− −
1/ 0.25BCt T = (10) C 1T CASE-C 1
0
0
1(c) OA CASE-A, B
1
(A ) (1)-(3)
CASE-B
0 (D ) ADt
( )b ADu t = {1 (1/ )}dy − − 2
1 2
y AD
0 0
2
2 1
1 A , B OAt , OBt
2.3 2 2 0t V
DI 2
CASE - 4 CASE- 1 2
CASE- 1
2 CASE- 1
2
CASE - 3 CASE -
2.2 1
2
2
2 *u
3
2
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
to/T1
0
1
2
3
4
5
CASE-
CASE- CASE-
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
to/T1
0
1
2
3
4
5
1/ODt T 1 1/ , /OP OQt T t T
38
*v
0( )au t 0( )au t
0( )b OAu t t− 0( )b OAu t t−
0( ( ))c OA ABu t t t− + 0( ( ))c OA ABu t t t− +
0t DI
Collapse Pattern 1’-4’ 4
[2]
4 2
1 2
1, 2p pu u
DI
2
m v V ku
+ +
= + + (12)
4(a) 2 (1/ )p yu d= − Collapse Pattern 2’
Collapse Pattern 2’ 1
2 0
2 0.4 = −
2
CASE-CASE-
( ) / 2 { 1 } / 2
{ ( ) / 2} ( ) ( / 2) p
k d u f ku u ku
+ + − −
= − + − +
2 2 2
1 1/ 2 ( / 2) ( / 2)y y p pmV kd kd u ku= + + (14) (14) 1 1pu
2
1 / (1 / )[ 1 1 {1 ( / ) }]p y yu d V V = − + − − (15) 2 2pu 4(b)
2 1/ (1/ ) ( / )p y p yu d u d= − + (16) Collapse Pattern 3’
Collapse Pattern 3’ 1
2
2
2
0
CASE-, 2
2
(a) Collapse Pattern 1’ (b) Collapse Pattern 2’ (c) Collapse Pattern 3’ (d) Collapse Pattern 4’
4 1 2
B
OO
P
f
u
f
u
1pu
O
f
u
1( ) / 2y pk d u− 2
[3] DI
DI
DI
5 -
DI
7
-
4
2 [3]
DI
[1] Kalkan, E. and Kunnath, S.K. (2006). Effects of fling
step and forward directivity on seismic response of buildings, Earthquake Spectra, 22(2), 367–390.
[2] Kojima, K and Takewaki, I. (2015). Critical earthquake response of elastic-plastic structures under near-fault ground motions, Frontiers in Built Environment, 1: 12.
[3] Kojima, K and Takewaki, I. (2016). Closed-form dynamic stability criterion for elastic-plastic structures under near-fault ground motions, Frontiers in Built Environment, 2: 6.
[4] Jennings, P. C., and Husid, R. (1968). Collapse of yielding structures during earth-quakes, J. Eng. Mech., 94, 1045-1065.
0 4 = − .
Pattern 1’
Pattern 4’
1 1/ , /OP OQt T t T
Collapse Pattern 1’ Collapse Pattern 2’ Collapse Pattern 3’ Collapse Pattern 4’
Non-collapse
Collapse
-1
-0.5
0
0.5
1
-1 -0.5 0 0.5 1 u/dy
-1
-0.5
0
0.5
1
-2 -1.5 -1 -0.5 u/dy
-1
-0.5
0
0.5
1
-1
-0.5
0
u/dy
-1
0
1
2
zzzz
y
-1
-0.5
0
0.5
1
zzzz -1 0 1 2
u/dy
-1
-0.5
0
0.5
1
0 4 = − .
4) B C 2
2007
1 2 a)
0.0
0.4
Subject B Subject C
-5
0
-0.4
0
0.4
-0.4
0
0.4
-1.5
0
1.5
0.4
-0.4
0
0.4
-0.4
0
1.5
-1.5
0
-5
0
5
Ac cel
era tio
n ( m/
1
Force plate (1mx1m) Shaking table
X Z
C B CoP
B, C CoP
1.0Hz
0.7Hz B 1.2Hz
B
2
0()()
2 1 2 1 2
1 2 1 2 1 2
1 2 1() 2() t 1 2
+ 32() cos 2() − 21()2 sin 1()
− 32()2 sin 2() + ()
(1)
+ 52() cos1() − 2() − 7 sin 1()
+ 52()2 sin(1() − 2())
(2)
+ 51() cos1() − 2() + 62()
(3)
2 1~8
42
- 3 -
C
ξ()
2
-0.4
0
0.4
-1.5
0
1.5
-0.4
0
0.4
-0.4
0
0.4
-1.5
0
1.5
Analysis Experiment
Analysis Experiment
Analysis Experiment
Analysis Experiment
Analysis Experiment
Analysis Experiment
c) Velocity of Head
b) Displacement of Head
a) Displacement of CoP
c) Velocity of Head
b) Displacement of Head
a) Displacement of CoP
0 10 20 30 40 0 10 20 30 40 Time (s) Time (s)
- B C 3000 7190 -78200 -37400 -20100 -16000 -11300 -14500 -17800 -15100 -6510 -4600 -54.6 -61.6 -89.0 19.3 119 183 -16.8 -24.1 -27.8 -25.4 28.7 32.9
5 B, C
7
B: 2 a 8 C: 2 a
6 TVR
2011
9
1 ~ 4 10 Site 1
1.0Hz
C
.
2016
3) : 28 (2016 )
. 2016
5) Winter, D. A. : Biomechanics and Motor Control of Human
Movement, 4th Edition, 2009. 10
6) D. Gordon E. Robertson, Graham E. Caldwell, Joseph Hamill,
Gary Kamen, Saunders N. Whittlesey: Research Methods in
BIOMECHANICS, 2nd Edition, 2013
“http://www.kyoshin.bosai.go.jp/kyoshin/”, (2020-3-6 )
Maximum head velocity of subject B (m/s)
The Western Tottori prefecture earthquake in 2000 The 2011 off the Pacific coast of Tohoku Earthquake The Niigataken Chuetsu-oki Earthquake in 2007 The 2016 Kumamoto Earthquake
Site 1 (FKS020)
Site 2 (KMM007)
Site 3 (FKS018)
Site 4 (NIG025)
m /s)
Frequency (Hz)
a) Site 1 (FKS020) NS EW b) Site 2 (KMM007) NS
EW
9 10 Site 1 ~ 4
44
- 1 -
1
5 250 1)
10m 2 2018 5 5 1)
2020
2)
40%
4-2
3 5 HM 4HM HM 2 5 HM HM
100
5-2
7 24
2
4 HM
2
5
271 33% 130 16% 88 10%
483 392 81%
50
- 3 -
5No1No 2 3No9No 10No13 1 5
No9No10 No13 3/4 No 11No12 1/2 5
6 2)
53 1 121 3792 255 3 112 746 92%
134 107 104 91 47
3
25 20%
6-2
7 65 686 60
3
6
7
5 HM
HM
5
2) 5 HM
https://www.city.koto.lg.jp/057101/bosai/bosai- top/topics/documents/honpen.pdf,2018.8
0.0m1 0.5m 1
52
- 1 -
1_
408,501 33 10
4)
5 39 12 36
5 10 6)
41 7 6
10
41 9
(2 )(1 )
3
26
12)
3_
3
34 35 5 1
36 5
1112 37
3 2 37
1
40
40 6 10 41 2
2
40 11 41 2
37 38 5
41 6
4
3
54
- 3 -
3
15)
13 ( 17-D
12 ( 17-F
11 ( 17-N)
10 ( 17-V
42
3),1938 4
,1973 5)
1908 7 6 61903 5 11
7) 161 ,1900 5 8)
,2008 10) 265 ,1909 1 11)
,1927 12)
,1995 13
,2014 15)
,2014 16)
,2000
16 38 12) 17
15
1.
1 12
37 4 48 31 12 28% 3% 37% 23% 9%
C 10
8~9
ABCD 4~7
0~3
3m
3m
B-1 B-3
C
23(17%)
8
7
B-2 B-3
X
Z
A-1 A-2 A-3
37 4 48 31 12 28% 3% 37% 23% 9%
C 10
8~9
ABCD 4~7
0 100M
B-1 B-3
C
Z3
×
3 6 9 12 14
3 6 9 12 14
0 3 6 9 12 14
3 6 9 12 14
0 3 6 9 12 14
3 6 9 12 14
0 3 6 9 12 14
3 6 9 12 14
r=0.72 r=0.85
r=0.55 r=0.76
0 3 6 9 12 14
3 6 9 12 14 r=0.92
4
I J
12
H
( )
12
64 ,
,Vol.51,No.3,pp.328-335
,
62
- 3 -
()36 6 8
‘
64
- 1 -
1.
)
(
(1 1
2019 8
2018 12
2018 7
(3)
1 3 2
37%( 1) 3 7.9ha
41%( 2( 1)
1
41.6%46.7%3 36.5%4
1 2
3 3 6 4 7
(3)
123 0.0%15.9%9.5%
456 33.0%37.1%
28.4%
5.2%
( 3f)
66
- 3 -
8
( 3g)
41.7%
4,5
1 (3)
25%
3) ,Vol.54 No.3 2019.10
4), , ,Vol.311,119-128,1982.1
1) 2)
4.2012 []
5.2012 []
6.2016 []
N
C1
C2
C3
C4
C7
C8
C9
C10
C11
C5
C6
1.2 1.4
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
0.8 0.8
FL+600
FL+1200 FL+600 FL+1200 FL+600 FL+1200 FL+600 FL+1200
FL+600
FL+1200 FL+600 FL+1200 FL+600 FL+1200 FL+600 FL+1200
[ m in /m
3 ]
0
200
400
600
800
1000
1200
1400 [ m in /m
3 ]
(m m
(m m
Ce
Cu
Cd
C2
C3
D
Qp(y)h QE(y)l Qu(y)j Qd(y)k
[m2/s] [m3/s] [m] [m] [m] [m] [1/m] [m3/s] [m3/s] [m3/s] [m3/s]
[-] [-] [-] [-] [-] [m2/s]
Q Q y Q y Q y Q ys p i h E i l l
o
n
j
m
h
N
1 111
Q y Q y Q y Q y C Q yur d p d h E d l
o
n
k
n
)
( ) ( ) ( )
n
k
n
0.0005[m2/s]
A, B, C, D, 4 3
CO2
2
1
CO2
10 1 1
8 0.8 0.8
6 0.6 0.6
4 0.4 0.4
2 0.2 0.2
0 0 0 50100 200 400 50100 200 400 50100 200 400
(mm) (mm) (mm)
10 1 1
8 0.8 0.8
6 0.6 0.6
4 0.4 0.4
2 0.2 0.2
0 0 0 50100 200 400 50100 200 400 50100 200 400
(mm) (mm) (mm)
10 1 1
8 0.8 0.8
6 0.6 0.6
4 0.4 0.4
2 0.2 0.2
0 0 0 50100 200 400 50100 200 400 50100 200 400
(mm) (mm) (mm)
2.0 2 2
1.5 1.5 1.5
1.0 1 1
0.5 0.5 0.5
(1) Case14(
(2) Case13( )
(m )
(m )
(m )
(m )
(m )
(m )
71
(1 )
(2 )
0 0.5 1.0 1.5 2.0
(3 )
1) REHVA:REHVA Guidebook on Displacement ventilation , 2007 2) :
, , 2012
CFD
6
3
1
1
2
2)
1
CFD
200
150
100
50
0 50 100 200 400 50 100 200 400 50 100 200 400 50 100 200 400 50 100 200 400 50 100 200 400
A C A C A C
a b c
(3 )E
4- A
(2 )E
2- A
(1 ) E
1- A
1528 1536
1588 1607 1621 1628 1633 1636 1636 1641 1650 1652 1653 1653 1656 1668 1701 1827
12)
1
1207-1210 1275-1332
1299 1548 1570 1634 1636 1636 1666 1734 1756 1789 1828
6 6
14) 15)
4 7
20) 21)
25.0 0 18.8 81.3 0 6.3 100 25.0 0 100 37.5 12.5
62.8 14.1 5.1 65.4 34.6 0 66.3 2.4 1.2 48.2 13.3 0 95.9 4.1 3.1 36.7 11.2 2.0 50.0 15.4 7.7 69.2 15.4 0 33.3 33.3 0 100 33.3
16 8 78 83 98 26 3
8
1) ,
, 1974
1
2 SMGA (Rupture Starting Point: RSP) Multi hypocenter model
(Seg.H)
(Seg.I)
10)LMGA
2 3
500 m
1
6 Gauss
11)
K-NET 6 KiK- net 4 2
7 ,JR 2 21
) EW 1
93048 () EWUD 3
NS 1
3
KMM006 (K-NET )
11)
12)
(h=5%)KMMH16 EW
SMGA LMGA
SMGA3 SMGA5
FP Directivity Pulse 93048 EW
LMGA3 SMGA6 PSV
SMGA6 1.1 3
LMGA3 SMGA6
6
13)KMMH16
PSV 600 cm/s
0
100
200
0 10 20 0 10 20 0 10 20 0 100 200 300 400 500
0 100 200 300 400 500 600
0
100
0 10 20 0 10 20 0 10 20 0 100 200 300 400 500
0 100 200 300 400 500 600
0
100
200
300
0 10 20 30 0 10 20 30 0 10 20 30 0
100
200
300
0
100
200
300
0
100
200
0 10 20 0 10 20 0 10 20 0
100
200
300
0
100
0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0
100
0
100
0
50
0 10 20 30 0 10 20 30 0 10 20 30 0
100
200
0
100
200
0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0
100
200
300
0
100
200
300
400
0
100
0 10 20 0 10 20 0 10 20 0
100
200
0
100
200
0
100
0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0
100 200
300
400
0
100
0 10 20 30 0 10 20 30 0 10 20 30 0
100 200 300 400
100 200 300 400
100
200
300
PSV (cm/s) UD
KMMH16
93051
93048
93006
93011
EED
KMM004
KMM006
MYJ
TTN
(a) (b) PSV (h=5%) 4
(b)
32.8
33.0
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5(m)
131.0
131.0
131.0
131.0
32.8
33.0
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5(m)
131.0
131.0
131.0
32.8
33.0
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5(m)
32.8
33.0
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5(m)
131.0
131.0
131.0
20 km
SMGA LMGA
8 Y=-500 m-30 kmX30 km 1 km
X FP Y FN
LMGA WLMGA=2 km
3
X (km) (s)
9 9
SMGA LMGA
FN SMGA
100 200 300 400 500 600 700
PSV (cm/s)
9 PSV
(a) (b) LMGA (c) SMGA
0
1
2
3
4
5
10 0
100200300 400
102
103
0
1
2
3
4
5
100
101
102
103
0
1
2
3
4
5
102
103
0
1
2
3
4
5
100200
400
101
102
103
X (km)X (km)X (km)
Edges of seismic fault model Edges of SMGA and LMGA
5 SMGALMGA (a) KMMH16 EW (b) 93048 EW
(a1) (a2) (a3) PSV (h=5%) (b1) (b2) (b3) PSV (h=5%)
0 10 20 0 10 20 0
100
200
300
400
500
0 1 2 3 4 5 time (s) time (s) period (s)
h=5%
-146
-80
44
125
12
-97
119
106
100
200
300
400
500
600
0 1 2 3 4 5 time (s) time (s)
Obs.
Syn.
LMGA2
LMGA3
SMGA4
LMGA6
SMGA6
Obs.
Syn.
LMGA2
LMGA3
SMGA4
LMGA6
SMGA6
258
291
19
91
76
20
202
175
197
28
75
20
24
92
PSV (cm/s)
PGV (cm/s)
PGD (cm/s)
PGV (cm/s)
PGD (cm/s)
PSV
1
PSV (h=5%)
11 11
1
FP
X= -5000 m-10000 m 2 FN
1
1 1
FN
(K-NET KiK-net) GMT 2 (ALOS2)PALSAR-2 JAXA 1) Shirahama et al., Earth, Planets and Space, Vol. 68:191, DOI:10.1186/s40623-016-0559-1, 2016.10 2) 13 26 pp. 451-456 2007.12 3) 17 35 pp. 55-592011.2 4) Okada, Y, Bull. Seismol. Soc. Am., Vol. 75, No. 4, pp. 1135-1154, 1985. 5) Hisada. Y and J. Bielak, Bull. Seismol. Soc. Am. Vol. 93, No. 3, pp. 1154-1168, 2003. 6) 20 1 () pp. 118-1322020.1 7) Ozawa et al., Earth, Planets and Space, Vol. 68: 186, 2016.11 8) Yoshida et al., Earth, Planets and Space, Vol. 69:64, 2017.5 9) 84 757 pp. 373-3832019.3 10) 2Vol. 53No. 1pp. 1-92000.7 11) 47 (2019)pp. 11-242019.11 12) http://maps.gsi.go.jp/#11/32.926284/130.735931/&base=std&ls=std% 7Curgent_earthquake_20160414kumamoto_p023dr_p135ar_qe_25d% 2C0.7%7C20160414kumamoto_epicenter%7Curgent_earthquake_qeg rd_cont10cm_cut10%2C0.7&blend=0&disp=1111&lcd=urgent_earthq uake_20160414kumamoto_p023dr_p135ar_qe&vs=c1j0h0k0l0u0t0z0 r0s0m0f1&d=vl 13) 2016.9
11
(a) FP (b) FN
Lower depth of LMGA
0
1000
2000
0
1000
2000
Depth (m)
VS (m) VS (m) VS (m)
TL M
TL M
Depth (m)
Depth (m)
1
2
3
4
5
0
100
200
300
400
500
600
700
1
2
3
4
5
0
100
200
300
400
500
600
700
1
2
3
4
5
0
100
200
1
2
3
4
5
0
100
200
1
2
3
4
5
0
100
200
X=0 m
X=-5000 m
X=-10000 m
PSV (cm/s)
PSV (cm/s)
Time (s) Period (s) Period (s) Period (s)
Full method Substructure method Outcropped seismic bedrock motion
X=0 m
X=-5000 m
X=-10000 m
PSV (cm/s)
PSV (cm/s)
Time (s) Period (s) Period (s) Period (s)
Transfer function
2)
1 5m×
3m 1(a) 8.0×3.0m× 3.0m 1(d) 8m 4) U 1(b) 1(c)
JMA 50% Y 20gal 2
ARX 2 1
0.0020.017
2
700mm 500mm200mm 3 3 6 4
L 2
90% 40 T1T4
4 TN1TN3 3 p
Mp
M - 4 0.05[rad] 0.10[rad] 10% 90% T2
0.027[rad] 0.056[rad] 20A 0.02[rad] 20A p
2.3 20Ap 2 5) 32A
M -θ
36 0.076
[Hz]
[-] 30.02 0.012 ( 100A) 11.73 0.002 ( 25A) 5.90 0.010 ( 100A) 7.60 0.008 ( 100A) 14.22 0.011
( 40A) 7.36 0.017 ( 25A) 8.41 0.017 ( 20A) 9.15 0.006
( 9.52mm/12.7mm) 4.40 0.005 ( 40A) 10.37 0.003
JMA 50% 5
0% 0.000 0.000
100%75% 0.000
0.001 0.005ARX 0.000 0.001 ARX
0.005
75% 0.005 25A 40A 20A 7
-1000
0
1000
0
1000
-1000
0
1000
0
1000
-500
-250
0
250
500
T1
T2
T3
T4
TN1
TN2
TN3 M [N m ]
[rad] M θ
8 40A 9 A-A’B-B’
10
JMA 100%JMA 4FL50%JMA 4FL 11
1
p
1) 2016 2018
2) 59 pp.65- 682019.6
3) ARX
508pp47-541998.6 4) III917-9182019.9
5)
2020.9
9 Large-eddy Simulation(LES)
() 3)([] )
6)Breathability
Breathability
flux Breathability
K 2
k 3
K 4
k (2)
[]
= ⟨3 ⟩ + ⟨3 ⟩ + ⟨′3 ′⟩⟨⟩ + ⟨′3 ′′⟩ 2⁄ (1) = ⟨1 ⟩ + ⟨1 ⟩ + ⟨′1 ′⟩⟨⟩ + ⟨′1 ′′⟩ 2⁄ (2)
(A-A’)
Case1H
3 (Case2HCase3HCase5H) 4
LES
9)10)
3 ( 1.5m)
Ves ([Ves]) [Ves] Case1H [Ves]
Case1H
Ves
Ves
1 C.V.(Canopy)(Breathability)
K,
( 5(1))
() = ⁄ (8) C.V.(Canopy)
1
A-A’
(1) (2) (1) (2) 4 Case5H 5 A-A’ Case5H
(1) (2)⟨3 ⟩ (3)⟨3 ⟩ (4) ⟨ ′3 ′⟩⟨⟩ (5) ⟨
′3 ′ ′⟩/2
6 C.V.(Canopy) Case1H Evt
(1) (2)⟨3 ⟩ (3)⟨3 ⟩ (4) ⟨ ′3 ′⟩⟨⟩ (5) ⟨
′3 ′ ′⟩/2
- 3 -
6(1)Case1H
1 C.V.(Canopy)
⟨3 ⟩ 11.84 21.0%
39.545 70.1%
38.14 55.8%
36.48 41.8%
39.40 35.5%
′⟩/2 3.791 6.7%
1
2
(Case1H ) Case1H Case2H Case3H Case5H 1.00 1.21 1.55 1.97 1.00 1.37 2.61 7.02 1.00 0.88 0.59 0.28
OCAEL ,
Naoki Ikegaya et al. Theoretical and Applied Climatology, 127, 655-665, 2017.2 ,,,33 ,C08-3,2019 Hideki Kikumoto et al.Journal of Wind Engineering and Industrial Aerodynamics, 173, 91-99, 2018.2 ()- -, , 1993 Yasuyuki Ishida et al.Journal of Wind Engineering and Industrial Aerodynamics, 183, 198-213, 2018.12 Marina K.-A. Neophytou et al.Proceedings of the 5th GRACM Inter Congress on Comput Mech,,967-974,2005.6 Negin Nazarian et al.9th International Conference on Urban Climate jointly with 12th Symposium on the Urban Environment,1-6,2016 Ioannis Panagioyou et al.Science of the Total Environment, 442,466-477,2013 ,25, 211-216, 2018 Tubasa Okaze, Mochida AkashiComputer and
Fluids,159,23-32, 2017.12 k K
(K+k) C.V.
OpenFOAM-ver4.1 SGS Smagorinsky 1500m 20 2 2 (95%) 1 (5%) 2 PISO Slip Spalding 240 150
f, <f> f , f , f’(=f<f>), ui 3 [m/s](i=1:,i=2:,i=3: ),ε[m2/s3], K[m2/s2] (=1/2ui2), k[m2/s2] (=1/2 ui′2), Veseffective speed, Ves = √⟨(⟨ui⟩ + ui′)2⟩ = √2K + 2k [m/s] H[m](=30m) EinUpC.V.(Upper) EoutUpC.V.(Upper) EinCC.V.(Canopy) EoutCC.V.(Canopy) ∫C.V.(Upper) C.V.(Canopy) εUpC.V.(Upper) εCC.V.(Canopy)
0 30 60 90 120 150 180 210
0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012
[m
10
S A B 2
A 1 1
4
1989 1
15
3
/
19741988
708
421 59.5
4
4
1977 22
GS FC 70
1970 -80
S B
2
K F () B
BK BF B
8 1974 ()()()
B 1974
8
1980 THES(Total House Element System)
THES
10 THES ()
3(1986)
STHES
1973
/
.1983 ./2.
... 1980 8 ./ 4..
,, 1999 ./5.
20 ,1983 .
92
- 1 -
1 2
7 6
4)
Classification
AI
2nd system in 2013 Image diagnosis
system
Dweller
Inspector
Quantitative damage assessment
Estimation
Image measurement of residual deformation
Approximately 2×Residual deformation
3)
Segmentation10)
5
2016
SV16 Seg-Net
Dataset Image types Wall types Damage types
RMC Real
DLR50RMC
SV19CMC
SV19RMS
DLR50CMS
DLR50CBC
94
- 3 -
= (4)
Original
Image
Damage
Extraction
result
(%) (%)
rig in
al im
ag e
Ex tra
ct io
24.9 26.0
17.9 44.5
2) : 2016. 5
3) : 2013.6
4) : 2007 No.22, pp.35-38, 2008. 5
5) : Vol.11, pp.12-21, 2014. 4
6) : 15 GO07-01-10, pp.1267-1274, 2018.12
7) : 15GO11- 01-07pp.1874-18822018.12
8) Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton.: Imagenet classification with deep convolutional neural networks., NIPS'12: Proceedings of the 25th International Conference on Neural Information Processing Systems Vol. 1, pp.1097-1105, 2012. 12
9) Girshick, R., J. Donahue, T. Darrell, and J. Malik.: Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation., Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, pp. 580-587, 2014. 6
10) Chen, Liang-Chieh et al.; Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation., ECCV (2018) arXiv: 1802.02611v3, 2018. 8
11)
C-1, pp. 93-94, 2018. 9 12) :
2010. 1 13) :
1 -, pp.581-582, 2014. 7
14) : 3 1
91 pp.167-170, 2018.6
15) : 3 E- 1
pp. 231-232, 2018. 7 16) :
IC 1840, pp.829-834, 2012. 10
Table 6 Evaluation results in the 2019 Yamagata earthquake Camera Original images Extracted damages (%) Inspectors Error rate Drift ratio
Smart phone
1.
-400×400×16 (BCR295)
H-500×200×10×16 (SN490B)
2.3
× × × 1970 0.04 rad1 0.05 rad1 0.03 rad2 0.04 rad2 0.05 rad2 × () × () ()
ΣE [kNmrad] 138.5 286.5 153.0 316.2 565.1 Σθp [rad] 0.185 0.352 0.179 0.312 0.616
η 19.0 36.0 25.1 50.0 72.5
2
ΣE[kNmrad] Σθp[rad] η
1 (3)
1-6 7-12 13-18 19-22 23-24 25-26 27-28 29-30 31
(rad) 0.0035 0.005 0.0075 0.01 0.015 0.02 0.03 0.04 0.05
97
- 2 -
1970
OpenSees
Model) b
(Tuned Bilinear Model)
c (Stiffness
3
c
3.
[rad] 0.060.040.020-0.02-0.04-0.06
4 ( –)
(a) Design Model (b) Tuned Bilinear Model (c) Stiffness Deterioration Model (d) Full Deterioration Model
0 0.2
0 0.2
5
0.5
1.0
1.5
6
Model
a) Design × b) Tu ned Bilinear × c) S tiffness Deterioration
d) Full Deterioration
98
- 3 -
12
13
4
0.1) (model-3)
R2 = 0.008
model-1 model-2
Step
5 [m]
1
13
( 18)
19
pp.19-322015.9
that incorporate strength and stiffness deterioration,
Earthquake Engineering and Structural Dynamics, Vol.34,
pp.1489–1511, 2005.6
-E-
-
W
H
θR
4000
3000
2000
1000
. .
1860
Harrods20 Whiteleys 19 grocer
1894 1912
Joseph Appel 1906
. 19
.
Bazaar 18 19
1851 1862 19
1870 Mail Order
1901-1906 light well 2
. 20 19
Sigfried Giedion
.
.
1922~3 1934
103
- 4 -
5 28 Cubical Extent 75 76
1855 Metropolitan Building Act
. 53 1
55cm
Selfridges LCC(London County Council
:GLC)
Jan Whitaker, The Department Store HistoryDesignDisplay, 2011.
Philip Steadman, The changing department store building, 1850 to
1940, 2014.
Tim Dale, Harrods : a Palace in Knightsbridge, 1995
1990
1997
1
1.1
36.
()
2.2
9
10
2
(2018 9 3)
(SB )( SB )
1 16
2 3.2
35 2.5
4.( 2)
4.1
4.4
A ()A B ()B ()
1 MT MB SB SB ST ST
)
106
- 3 -
4.5
A ()A B ()B ()
4.6
()
()
1920
6
25
pp.188-190, pp.476-468
)
2()
1872 A
()A
()A
()
()A
()A
()A
()A
()A
1890 B ()B
()B
()B
()
()B
1904 A
()
()A
()B
1910 B ()B
()B
()B
1913
()
()B
()B
()
()
()
1.
PSS PSS 1)
150[S/m] PEDOT/PSS( P/P150)PSS P/P 30, P/P 10 PSS 2.2
P/P150 ( 2) P/P10 ( 3)
2.4
.1 (: )
.2 .3
10 (Quantachrome : VSTAR) 1 CASE PSS 4 25,50,75[] 3 11~13 P/P150,30,10,PSS 4
0.150.6 PSS 3 P/PPSS
3.2 2),3),4)
T[K] P[][3][3] L[kg2 2⁄ ](3-1)
Clausius-Clapeyron 14 14
PSS P/P10, PSS 2
.4 SEM .5 SEM
.6 .7
30[S/m] PEDOT/PSS
10[S/m] PEDOT/PSS
PSS
=
18.23
34.29
0
10
20
30
40
[Ω ]
7,638.89
1,072.92
0
2000
4000
6000
8000
10000
[ 2⁄ ], t[] [ ⁄ ], [ 2 ⁄ ],
[]
P/P10 PSS
PEDOT/PSS PSS
(4-1)
.14 .15 .16
0
200
400
600
800
1000
1200
1400
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
g]
[-]
150[S/m] 25 30[S/m] 25 10[S/m] 25 PSS 25
0
200
400
600
800
1000
1200
1400
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
g]
[-]
150[S/m] 50 30[S/m] 50 10[S/m] 50 PSS 50
0
200
400
600
800
1000
1200
1400
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
g]
[-]
150[S/m] 75 30[S/m] 75 10[S/m] 75 PSS 75
40
42
44
46
48
50
0
0.000005
0.00001
0.000015
0.00002
0.000025
k[ kg
/P a
m 2
0
0.00002
0.00004
0.00006
0.00008
k[ kg
/P a
m 2
111
- 4 -
…(4-1)
D[], ρa[ 3⁄ ], Q[3 ⁄ ], t[s] [ ′⁄ ]
3 CASE 22 CASE:A,B CASE:A CASE:B 4.2
18CASE:A,B
(CASE:A-6,9, B-3,6,12) 19 > (CASE:A-3,B-9)
CASE:B CASE:A PSS
,
2)., , , 2002.6 3). 2, 2, , 2010.10 4)., , 1977.8 5). 4, , , 2006.11 6)., S , Vol.26 No.4, pp. 469-479, 2009.7 7). ( 3) , , 2007, pp.1007-1010
.17
.2
2.5 CASE
3 () 28 50 11.8 () 50 15 11.8
.3 CASE
CASE
150 0.14 14 3 3 CASE:A-2 150 0.14 14 3 2 CASE:A-3 150 0.14 14 3 1 CASE:A-4 150 0.14 14 2 2 CASE:A-5 150 0.14 14 2 1 CASE:A-6 150 0.14 14 1 1 CASE:A-7 10 0.14 14 3 3 CASE:A-8 10 0.14 14 2 2 CASE:A-9 10 0.14 14 1 1 CASE:B-1
150 0.42 0 3 3 CASE:B-2 150 0.42 0 2 2 CASE:B-3 150 0.42 0 1 1 CASE:B-4 10 0.42 0 3 3 CASE:B-5 10 0.42 0 2 2 CASE:B-6 10 0.42 0 1 1 CASE:B-7 150 0.42 14 3 3 CASE:B-8 150 0.42 14 3 2 CASE:B-9 150 0.42 14 3 1
CASE:B-10 150 0.42 14 2 2 CASE:B-11 150 0.42 14 2 1 CASE:B-12 150 0.42 14 1 1 CASE:B-13 10 0.42 14 3 3
.18 CASE
.19 CASE
A- 1
A- 2
A- 3
A- 4
A- 5
A- 6
A- 7
A- 8
A- 9
B- 1
B- 2
B- 3
B- 4
B- 5
B- 6
B- 7
B- 8
B- 9
B- 10
B- 11
B- 12
[% ]
112
Tab.1
Fig.2 nac EMR9
Fig.1 2013
1960 1 1.2
1-1 1-2
2 360 3-1 3-2,3-3 3-4 1.3
2 3
1.4 2 2.1 Fig.1
2.2
2019/9/21 24 28 20
2
3-3
Tab.1
Fig.3
0
100
200
300
400
500
600
17
Fig.4 17
0
12
24
36
48
60
72
0
100
200
300
400
500
600
17
Fig.5 17
Fig.6 RICOH THETA S
Fig.7 Tobii Eye Tracker 4C
Fig.8 PC 360 Eye Tracker 4C 1/90
Fig.9 PC360
3.2 Fig.3
1-1 3.3 Fig.4Fig.5
EMR9 1-2 3.4
4 360 4.1 360
RICOH THETA SFig.6 360 PC 4.2 Tobii Eye Tracker 4CFig.7 4.3 4.4 Unreal Engine 4Fig.8
Unreal Engine 4(ver. 4.22.1) PC 3601/60 4.5
Fig.9
4 PC 2 0.2 4.6 4.7 EMR9 AR 2 Python 3.7.3
114
01 02
03 04
05 06
Fig.11 360 01 0601 06
B01 B04 B07 B08 B09 B10 B17
01 6 5 5 6 5 5 6
02 3 4 4 4 1
3
2
04 4 3 2 3 4
4
4
03, 0601, 05
B11 B13 B14 B16 B21 B22 B26
01 3 2 2 3 3 1 1
02 2 1 1 2 2 2 2
03 1 3 6 4 5 4 4
04 6 5 5 5 6 5 6
05 5 6 4 6 4 6 3
06 4 4 3 1 1 3 5
0204
B02 B12 B15 B19 B20 B23 B24
01 3 3 3 1 1 2 1
02 5 2 4 5 4 5 4
03 1 4 1 3 3 3 2
04 2 1 2 2 2 1 3
05 4 6 5 4 5 6 5
06 6 5 6 6 6 4 6
0405, 06
01 5 5 5 6 6 4 2
02 4 3 1 4 4 3 3
03 2 1 4 3 2
2
5
05 3 2 2 1 3
1
1
0506
4
4
Fig.12
B08, B10, B13, B16
04, 05 05, 06
B25
02, 03, 06 01, 04, 05
Tab.4
- 3 -
5 5.1 Fig.10 2019/12/3 28
5.2 28 Tab.2Tab.3 5.3
19995 Fig.12 Tab.4 3-1
Fig.11
01 03 20 01 03
01 06
04 06 20
20
Fig.14 12 12
91.7%
02
08 92.3%
03
09 80.0%
04
10 78.3%
05
11 64.0%
06
12 95.8%
Tab.5
Fig.15
0.00% 1.22% 10.98% 12.80% 16.46% 0.00% 16.46% 7.93% 34.15% 0.00% 0.00% 0.00% 1/3 0.00% 4.35% 6.52% 13.04% 10.87% 0.00% 8.70% 10.87% 45.65% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 13.33% 14.67% 21.33% 0.00% 13.33% 4.00% 33.33% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 7.94% 12.70% 9.52% 0.00% 23.81% 14.29% 31.75% 0.00% 0.00% 0.00%
12
0.00% 2.74% 12.33% 17.81% 17.81% 0.00% 8.22% 5.48% 35.62% 0.00% 0.00% 0.00% 1/3 0.00% 8.33% 12.50% 12.50% 0.00% 0.00% 8.33% 12.50% 45.83% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 15.79% 18.42% 23.68% 0.00% 5.26% 5.26% 31.58% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 0.00% 22.58% 12.90% 0.00% 12.90% 9.68% 41.94% 0.00% 0.00% 0.00%
12
0.00% 0.00% 11.54% 8.97% 17.95% 0.00% 21.79% 8.97% 30.77% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 3.13% 6.25% 28.13% 0.00% 12.50% 9.38% 40.63% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 11.11% 8.89% 22.22% 0.00% 17.78% 6.67% 33.33% 0.00% 0.00% 0.00% 1/3 0.00% 0.00% 11.63% 2.33% 16.28% 0.00% 25.58% 11.63% 32.56% 0.00% 0.00% 0.00%
12
Tab.6 12 12
-1.68 5.51 -8.38 0.04 1.30 2.81 -2.42 -1.27 -5.06 1.27 -2.53 1.27 1.27 10.13 0.96 -0.27 -1.10 9.20 -13.05 -7.55 9.20 -0.96 -0.10 -3.15 -9.28 16.07 7.40 -9.70 0.00 4.90 -2.99 6.06 2.40 -3.13 -8.49 0.00 -5.98 -0.91 0.87 1.06 -4.66 9.36 0.00 -0.30 3.81 -10.04 -9.07 1.46 13.10 0.00 0.45 -7.71 1.27 12.86 -2.99 -9.89 1.08 -1.08 5.38 -5.38 -1.08 1.08 -2.15 -1.08 1.51 -8.03 12.06 -7.59 0.51 4.33 0.00 -4.35 6.81 1.62 10.46 4.64 -12.32 0.00 0.00 -5.40 -1.97 -11.81 10.48 -1.59
0.06 0.74 0.59 0.25 -1.50 -0.12 -0.51 -0.55 -1.53 -3.99 0.07 1.97 2.00 0.44
12
Tab.5 3-2,3-3 6.3
Tab.6Fig.14 Tab.7 3-4
Fig.15
1 2
18 39 , 2016 , pp.667-672
3 " " " " 35 , 2012, pp.103-108
4 , 2014 , pp.611-612
5 518 , 1999 , pp.75-80
116
7
2020
7
(2013)
.
CL1
1960
19881873 33 46 54 56
6462 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 06 08 10 12 14 16 182000 02 04
[20,000] 0 2 4 6 8
10 12 14
7
4
3
2
(
5 5 5
7 (1994)
5 5
(2003) ParkPFI
(2016) 4
A B C D E F G H
118
.
3
Rhinoceros
Grasshopper
5-2
4 6
( 2
3) 4)
A B1
69
15 6 14
: : : : : : : : :
13
1
100m9)
2
1~4
100m 10) 1 =2
119
0.8
.
2010.
,2016.04.
:
Architecture 67 5 pp.709-7122004. 03/31.
4) :
5) :
24 pp.353-3612007.
6) :
0.88
0.48
0.82 0.82 0.76 0.76
1.00 0.85
Existing matching priority Green coverage complementarity priority
0.00
0.25
0.50
0.75
1.00