Exploring the mechanical properties of steel- and polypropylene-reinforced ultra-high-performance concrete through numerical analyses and experimental multi-target digital image correlation
Behrooz DADMAND1, Hamed SADAGHIAN2, Sahand KHALILZADEHTABRIZI2, Masoud POURBABA3, Amir MIRMIRAN4()
1. Department of Civil Engineering, University of Razi, Kermanshah 6718773654, Iran 2. Department of Civil Engineering, University of Tabriz, Tabriz 5166616471, Iran 3. Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA 4. University of Texas at Tyler, Tyler, TX 75799, USA
This study presents experimental and numerical investigations on the mechanical properties of ultra-high-performance concrete (UHPC) reinforced with single and hybrid micro- and macro-steel and polypropylene fibers. For this purpose, a series of cubic, cylindrical, dog-bone, and prismatic beam specimens (total fiber by volume = 1%, and 2%) were tested under compressive, tensile, and flexural loadings. A method, namely multi-target digital image correlation (MT-DIC) was used to monitor the displacement and deflection values. The obtained experimental data were subsequently used to discuss influential parameters, i.e., flexural strength, tensile strength, size effect, etc. Numerical analyses were also carried out using finite element software to account for the sensitivity of different parameters. Furthermore, nonlinear regression analyses were conducted to obtain the flexural load-deflection curves. The results showed that the MT-DIC method was capable of estimating the tensile and flexural responses as well as the location of the crack with high accuracy. In addition, the regression analyses showed excellent consistency with the experimental results, with correlation coefficients close to unity. Furthermore, size-effect modeling revealed that modified Bazant theory yielded the best estimation of the size-effect phenomenon compared to other models.
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4:3 mode: 20 M (5152 × 3864)/10 M (3648 × 2736)/5 M (2592 × 1944)/VGA/16:9 mode: 15 M (5152 × 2896)/2 M (1920 × 1080)
recording format
Still images: JPEG (DCF, Exif, MPF Baseline) compliant, DPOF compatible
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2.95 in (3.0 type) (4:3)/460,800 dots/Xtra Fine/ TFT LCD
angle of view
(35-mm format equivalent): 82°-2° 50 min. (25?875 mm)
specifications of laptop
model name
ROG GL553VE
CPU
Intel Core i7-7700HQ
GPU
NVIDIA GTX 1050 (2 GB GDDR5)
display
15.6″, full HD (1920 × 1080), TN
storage
256 GB SSD + 1000GB HDD
RAM
12 GB DDR4
Tab.1
material
3% fiber content (%)
6% fiber content (%)
cement
29.93
29.04
fine sand
41.22
39.95
silica fume
9.51
9.22
quartz powder
8.61
8.35
superplasticizer
1.25
1.20
fiber
3.00 (1% by volume)
6.00 (2% by volume)
water
6.48
6.24
Tab.2
chemical compound
content for cement (%)
content for quartz powder (%)
21.26
99.50
5.30
0.130
3.92
0.013
64.37
0.020
1.94
0.000
2.12
0.033
0.58
0.001
0.43
0.000
loss on ignition (LOI)
1.98
0.030
Tab.3
chemical property
content (%)
physical property
description
SiO2
90.5
moisture
1%
Fe2O3
2
size
less than 1 μm
CaO
1.5
shape
solid spherical particles
Al2O3
1
specific surface area (m2/kg)
14000–20000
MgO
2
density of a batch (kg/m3)
200–300
C
3
specific weight (kg/m3)
400–600
LOI
3.5
Tab.4
type
La) (mm)
D/Wb)
ftc) (MPa)
Ed) (GPa)
RC
30
0.85
2000
200
C
30
0.5 × 2
1800
200
H
30
0.76
1900
200
straight MS
13
0.16
2700
200
PP
15
0.48
400
6.9
Tab.5
specimen ID
slump (mm) (±10 mm)
RC1
240
C1
240
H1
245
MS1
260
PP1
–
RC2
230
C2
235
H2
235
MS2
250
PP2
–
RC1MS1
245
C1MS1
245
H1MS1
250
RC1PP1
–
C1PP1
–
H1PP1
–
Tab.6
Fig.2
Fig.3
Fig.4
specimen
compressive strength (MPa)
modulus of elasticity (GPa)
RC1
183
46
C1
177
45
H1
180
46
MS1
200
48
PP1
169
44
RC2
201
48
C2
190
47
H2
191
47
MS2
213
50
PP2
183
46
RC1MS1
220
51
C1MS1
207
49
H1MS1
209
49
RC1PP1
191
47
C1PP1
190
47
H1PP1
190
47
Tab.7
Fig.5
Fig.6
Fig.7
ID
regression parameter
value
R2
Adj. R2
RC1
a (Std.)
?1.612 (0.069)
0.981
0.980
b (Std.)
13.980 (0.225)
c (Std.)
?0.247 (0.013)
d (Std.)
0.571 (0.006)
Tab.8
Fig.8
ID
regression parameter
value
R2
Adj. R2
RC1
V (Std.)
0.0468 (0.0017)
0.9997
0.9996
k (Std.)
5.4196 (0.3222)
η (Std.)
1.3884 (0.0599)
Tab.9
Fig.9
Fig.10
Fig.11
ID
regression parameters
value
R2
Adj. R2
RC1
a (Std.)
0.201 (0.052)
0.950
0.946
b (Std.)
1.712 (0.335)
c (Std.)
?0.922 (0.159)
d (Std.)
1.875 (0.269)
Tab.10
Fig.12
ID
regression parameter
value
R2
Adj. R2
RC1
V (Std.)
0.0171
0.9988
0.9982
k (Std.)
2.8723
η (Std.)
1.3778
Tab.11
Fig.13
Fig.14
Fig.15
specimen
Bazǎnt and Chen [43]
Kim and Yi [45]
Carpinteri and Chiaia [44]
B
d0
R2
B
d0
α
R2
A
B
R2
RC1
2.5913
1196.4156
0.9875
1.4235
353.4483
1.2534
0.9975
205.3700
10958.3546
0.8859
C1
2.4580
854.7118
0.9868
1.5130
245.9200
1.0768
0.9995
150.7662
11244.0481
0.9108
H1
2.3313
547.0210
0.9868
1.8304
269.5340
0.5980
0.9913
107.5547
12238.4629
0.9043
MS1
2.4136
812.5002
0.9860
1.5121
232.2485
1.0407
0.9988
170.9870
13424.1550
0.9154
PP1
2.2996
553.4770
0.9524
1.6722
170.0815
0.8191
0.9650
37.1828
4236.5930
0.9027
RC2
2.6269
1037.6250
0.9903
1.6290
389.2028
1.0743
0.9965
305.7560
18693.2272
0.8856
C2
2.3319
727.5640
0.9819
1.5071
192.3460
0.9943
0.9975
176.5017
15472.1197
0.9238
H2
2.5440
981.4006
0.9841
1.4443
244.8308
1.2350
0.9994
250.2988
16359.7600
0.9085
MS2
2.5530
991.5350
0.9775
1.4496
253.0938
1.2337
0.9923
554.5480
35852.1720
0.9007
PP2
2.1745
725.6210
0.9267
1.6313
41.2942
1.2145
0.9723
55.7160
5106.7930
0.9484
RC1MS1
2.5436
1032.3370
0.9802
1.3859
230.9188
1.3008
0.9985
603.5687
37652.8805
0.9093
C1MS1
2.4735
891.5532
0.9803
1.4344
195.2640
1.2118
0.9998
463.6898
33492.5806
0.9218
H1MS1
2.5639
1068.8903
0.9784
1.3451
206.4720
1.3806
0.9986
499.3130
30349.0358
0.9189
RC1PP1
2.5619
991.9143
0.9908
1.5522
330.9495
1.1030
0.9997
148.4976
9500.6060
0.8924
C1PP1
2.3509
756.6840
0.9824
1.4962
199.5728
1.0183
0.9967
99.8760
8443.4768
0.9231
H1PP1
2.4352
851.9240
0.9808
1.4438
188.3077
1.1687
0.9995
118.6230
8965.8380
0.9253
Tab.12
Fig.16
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