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Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2018, Vol. 12 Issue (4) : 474-489     https://doi.org/10.1007/s11709-017-0442-6
Research Article |
The effect of micro-structural uncertainties of recycled aggregate concrete on its global stochastic properties via finite pixel-element Monte Carlo simulation
Qingpeng MENG, Yuching WU(), Jianzhuang XIAO
Department of Structural Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
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Abstract

In this paper, the effect of micro-structural uncertainties of recycled aggregate concrete (RAC) on its global stochastic elastic properties is investigated via finite pixel-element Monte Carlo simulation. Representative RAC models are randomly generated with various distribution of aggregates. Based on homogenization theory, effects of recycled aggregate replacement rate, aggregate volume fraction, the unevenness of old mortar, proportion of old mortar, aggregate size and elastic modulus of aggregates on overall variability of equivalent elastic properties are investigated. Results are in a good agreement with experimental data in literature and finite pixel-element method saves the computation cost. It is indicated that the effect of mesoscopic randomness on global variability of elastic properties is considerable.

Keywords RAC      Monte Carlo analysis      stochastic      finite pixel-element method      homogenization      coefficient of variation     
Corresponding Authors: Yuching WU   
Online First Date: 01 December 2017    Issue Date: 20 November 2018
 Cite this article:   
Qingpeng MENG,Yuching WU,Jianzhuang XIAO. The effect of micro-structural uncertainties of recycled aggregate concrete on its global stochastic properties via finite pixel-element Monte Carlo simulation[J]. Front. Struct. Civ. Eng., 2018, 12(4): 474-489.
 URL:  
http://journal.hep.com.cn/fsce/EN/10.1007/s11709-017-0442-6
http://journal.hep.com.cn/fsce/EN/Y2018/V12/I4/474
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Qingpeng MENG
Yuching WU
Jianzhuang XIAO
Fig.1  RAC model at fine scale. (a) Five-phase model; (b) three-phase model
Fig.2  Simulation of recycled aggregate. (a) 10 random points; (b) 20 random points; (c) 100 random points; (d) one of recycled aggregate samples
Fig.3  An example of 100 mm × 100 mm single-graded recycled aggregate concrete model with volume fraction of 40%
Fig.4  The boundary conditions used in the homogenization process
Fig.5  The pixel element method. (a) 30 ´ 30; (b) 50 ´ 50; (c) 80 ´ 80; (d) 100 ´ 100
Fig.6  The equivalent elastic moduli and Poisson’s ratio calculated by different mesh elements. (a) The mean value and COV of the elastic moduli; (b) the mean value and COV of Poisson’s radio
natural aggregateold mortarnew mortar
elastic modulus (GPa)702530
Poisson’s ratio0.160.220.22
Tab.1  Parameters used in numerical experiments
3024025026027028029021002
time (s)10101945353660929617148462273833614
Tab.2  Time cost of each control group calculating 1000 samples
Fig.7  The equivalent elastic moduli and Poisson’s ratio calculated by different mesh elements in case 2. (a) The mean value and COV of the elastic moduli; (b) the mean value and COV of Poisson’s radio
volume fraction of aggregate
(%)
elastic modulus of aggregate
(GPa)
elastic modulus of mortar
(GPa)
experimental value [9]
(GPa)
series model [10]
(GPa/err)
parallel model [10]
(GPa/err)
cube model [10]
(GPa/err)
present study
(GPa/err)
074.513.413.413.4
0.00%
13.4
0.00%
13.4
0.00%
13.4
0.00%
2074.513.415.816.0
1.45%
25.6
62.15%
17.6
11.54%
17.2
8.86%
4074.513.423.219.9
14.04%
37.8
63.10%
24.5
5.64%
23.5
1.29%
6074.513.430.726.4
14.06%
50.1
63.06%
34.8
13.32%
32.7
6.51%
42.55.240.818.610.4
43.89%
25.7
38.01%
21.5
15.53%
16.9
9.14%
42.518.240.830.226.7
11.57%
31.2
3.29%
29.7
1.77%
28.7
4.97%
42.55640.849.546.1
6.83%
47.3
4.53%
46.7
5.63%
46.5
6.06%
42.55440.851.345.5
11.25%
46.4
9.53%
46.0
10.34%
45.8
10.72%
42.57240.852.850.0
5.28%
54.1
2.39%
51.9
1.62%
51.4
2.65%
42.521040.869.962.0
11.24%
112.7
61.24%
76.2
9.07%
73.2
4.72%
Tab.3  A comparison is made among results of the present study and ones in literature [9,10].
RNA
(mm)
ROM
(mm)
volume fractionENA
(GPa)
EOM
(GPa)
nNAnOMR-rate
(%)
test 11012.540%70250.160.2250
test 21012.540%70250.160.22100
test 31012.540%60250.160.22100
test 410.51550%70250.160.22100
Tab.4  The parameters of four control groups with different material and geometric parameters
Fig.8  The convergence of Monte Carlo simulation. (a) The mean value of equivalent elastic moduli; (b) the COV of equivalent elastic moduli; (c) the mean value of equivalent Poisson’s ratio; (d) the COV of equivalent Poisson’s ratio
Fig.9  The histogram of the mean value and COV of the equivalent elastic moduli and Poisson’s ratio. (a,b) Test 1; (c,d) test 2; (e,f) test 3; (g,h) test 4
test 1test 2test 3test 4
EnEnEnEn
0.03580.04150.02130.02960.04000.03130.02130.0464
Tab.5  The data obtained from the Kolmogorov-Smirnov test
mean valuestandard deviationCOVR2
test 1E37.761620.168140.0044530.94472
v0.205830.001820.0088420.98195
test 2E35.153490.079110.002250.9909
v0.210170.001160.0055190.98553
test 3E34.13520.050810.0014880.96102
v0.209170.0007710.0036850.99193
test 4E34.102670.050740.0014880.98
v0.211330.000820.0038810.97762
Tab.6  The mean value, standard deviation, COV and the goodness of fit obtained by the Monte Carlo simulation
Fig.10  The effect of replacement rates. (a) The equivalent elastic moduli; (b) the equivalent Poisson’s ratio
Fig.11  The effect of volume fraction of RA. (a) The equivalent elastic moduli; (b) the equivalent Poisson’s ratio
Fig.12  The unevenness covering of the old mortar
Fig.13  The effect of unevenness of old mortar. (a) The equivalent elastic moduli; (b) the equivalent Poisson’s ratio
Fig.14  The effect of volume fraction of old mortar. (a) The equivalent elastic moduli; (b) the equivalent Poisson’s ratio
Fig.15  The effect of aggregate size. (a) The equivalent elastic moduli; (b) the equivalent Poisson’s ratio
Fig.16  The effect of elastic moduli of natural aggregate. (a) The equivalent elastic moduli; (b) the equivalent Poisson’s ratio
Fig.17  The coarse grid of the large specimen
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