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Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2019, Vol. 13 Issue (4) : 787-799     https://doi.org/10.1007/s11709-019-0516-8
RESEARCH ARTICLE
Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression spline
Ali Reza GHANIZADEH1(), Morteza RAHROVAN2
1. Department of Civil Engineering, Sirjan University of Technology, Sirjan 78137-33385, Iran
2. Department of Civil Engineering, Yazd University, Yazd 89195-741, Iran
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Abstract

The recycled layer in full-depth reclamation (FDR) method is a mixture of coarse aggregates and reclaimed asphalt pavement (RAP) which is stabilized by a stabilizer agent. For design and quality control of the final product in FDR method, the unconfined compressive strength of stabilized material should be known. This paper aims to develop a mathematical model for predicting the unconfined compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariate adaptive regression spline (MARS). To this end, two different aggregate materials were mixed with different percentages of RAP and then stabilized by different percentages of Portland cement. For training and testing of MARS model, total of 64 experimental UCS data were employed. Predictors or independent variables in the developed model are percentage of RAP, percentage of cement, optimum moisture content, percent passing of #200 sieve, and curing time. The results demonstrate that MARS has a great ability for prediction of the UCS in case of soil-RAP blend stabilized with Portland cement (R2 is more than 0.97). Sensitivity analysis of the proposed model showed that the cement, optimum moisture content, and percent passing of #200 sieve are the most influential parameters on the UCS of FDR layer.

Keywords full-depth reclamation      soil-reclaimed asphalt pavement blend      Portland cement      unconfined compressive strength      multivariate adaptive regression spline     
Corresponding Authors: Ali Reza GHANIZADEH   
Just Accepted Date: 07 March 2019   Online First Date: 09 May 2019    Issue Date: 10 July 2019
 Cite this article:   
Ali Reza GHANIZADEH,Morteza RAHROVAN. Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression spline[J]. Front. Struct. Civ. Eng., 2019, 13(4): 787-799.
 URL:  
http://journal.hep.com.cn/fsce/EN/10.1007/s11709-019-0516-8
http://journal.hep.com.cn/fsce/EN/Y2019/V13/I4/787
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Ali Reza GHANIZADEH
Morteza RAHROVAN
Fig.1  General flowchart of the present research
soil property standard 1st soil 2nd soil
unified classification ASTM D-2487 SP-SC GW-GC
AASHTO classification AASHTO T-234 A-1-b A-1-a
OMC ASTM D-180 7.79% 7.51%
maximum dry density )g/cm3( ASTM D-180 2.24 2.23
liquid limit ASTM D-4318 NP NP
plasticity index ASTM D-4318 NP NP
sand equivalent ASTM D-2419 28% 30%
soaked CBR ASTM D-1883 30 82
crushed in one face ASTM D-5821 - 72%
Tab.1  Soil material characteristics for first (SP-SC) and second (GW-GC) soil samples
material property subbase base
liquid limit Max. 25 Max. 25
plasticity index Max. 6 Max. 4
sand equivalent Min. 30 Min. 40
soaked CBR Min. 30 Min. 80
crushed in one face (%) - Min. 75
Tab.2  Characteristics of base and subbase according to IHAP
Fig.2  Grain size distribution curve for first (SP-SC) and second (GW-GC) soil samples
Fig.3  Wirtgen recycler WR 2500
Fig.4  Grain size distribution for RAP material
item item value requirement according to ISIRI 389
chemical analysis SiO2
Al2O3
Fe2O3
CaO
MgO
SO3
C3S
C2S
C3A
21.74% Min: 20
5.00% Max: 6
4.00% Max: 6
63.04% -
2.00% Max: 5
2.30% Max: 5
45.50% -
28.00% -
6.50% Max: 8
physical tests initial curing time(minute) 140 Min: 45
final curing time (hour) 3 Max: 6
compressive strength (Kg/cm2) 220(3 day) Min: 100
275(7 day) Min: 175
380(28 day) Min:315
Tab.3  Characteristics of Portland cement type II
RAP CC OMC MDD
(g/cm3)
UCS
(kPa, 7 days)
UCS
(kPa, 28 days)
0% 3% 6.92% 2.23 2761 3210
4% 6.86% 2.20 3055 3503
5% 6.72% 2.19 3438 3890
6% 6.56% 2.19 3841 4225
20% 3% 6.69% 2.25 2552 2958
4% 6.51% 2.20 2979 3341
5% 6.36% 2.20 3193 3710
6% 6.20% 2.18 3620 4153
40% 3% 6.34% 2.21 2262 2868
4% 6.22% 2.20 2689 3132
5% 6.04% 2.20 2814 3471
6% 5.89% 2.19 3227 3809
60% 3% 6.25% 2.22 1648 2548
4% 6.18% 2.20 2431 2928
5% 6.00% 2.19 2558 3379
6% 5.76% 2.19 3045 3784
Tab.4  Results of measured UCS for stabilized samples using SP-SC soil
RAP CC OMC MDD
(g/cm3)
UCS
(kPa, 7 days)
UCS
(kPa, 28 days)
0% 3% 6.87% 2.22 3607 3970
4% 6.78% 2.18 3988 4129
5% 6.64% 2.18 4393 4443
6% 6.49% 2.17 4700 5100
20% 3% 6.55% 2.24 3427 3890
4% 6.40% 2.19 3779 4037
5% 6.24% 2.19 4143 4225
6% 6.08% 2.17 4500 4800
40% 3% 6.20% 2.25 1983 2327
4% 6.12% 2.20 2228 2858
5% 6.00% 2.19 2828 3651
6% 5.89% 2.19 3238 4100
60% 3% 6.07% 2.20 1283 2093
4% 6.02% 2.19 2031 2379
5% 5.88% 2.18 2255 2910
6% 5.71% 2.18 2800 3335
Tab.5  Results of measured UCS for stabilized samples using GW-GC soil
item parameter RAP cement OMC seive #200 curing time (day) UCS (kPa)
training dataset (45 data) minimum 0.00% 3.00% 5.71% 8.00% 7.00 1283.00
maximum 60.00% 6.00% 6.92% 11.00% 28.00 4800.00
mean 32.00% 4.53% 6.26% 9.67% 16.80 3290.93
standard deviation 22.72% 1.16% 0.33% 1.51% 10.60 835.98
median 40.00% 5.00% 6.20% 11.00% 7.00 3379.00
testing dataset (19 data) minimum 0.00% 3.00% 5.76% 8.00% 7.00 1983.00
maximum 60.00% 6.00% 6.92% 11.00% 28.00 5100.00
mean 25.26% 4.42% 6.37% 9.11% 19.16 3282.11
standard deviation 21.95% 1.07% 0.36% 1.49% 10.65 762.99
median 20.00% 4.00% 6.36% 8.00% 28.00 3210.00
Tab.6  Statistical parameters of training and testing datasets
item number of data R2 RMSE MAD MAPE GCV
training dataset 45 0.9744 132.27 98.30 2.98 32666.39
testing dataset 19 0.9727 133.18 107.77 3.28 22142.31
Tab.7  Performance measures of MARS method
Fig.5  MARS predicted UCS against measured UCS. (a) Training data; (b) testing data
Fig.6  Degree of importance of different parameters on the UCS using CAM
Fig.7  Effect of measured error of input parameters on the MAPE of predicted UCS
Fig.8  Parametric study of UCS against OMC
Fig.9  Parametric study of UCS against RAP content
Fig.10  Parametric study of UCS against curing time
Fig.11  Parametric study of UCS against percent passing through No. 200 sieve
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