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Frontiers of Structural and Civil Engineering

ISSN 2095-2430

ISSN 2095-2449(Online)

CN 10-1023/X

邮发代号 80-968

2019 Impact Factor: 1.68

Frontiers of Structural and Civil Engineering  2018, Vol. 12 Issue (3): 361-371   https://doi.org/10.1007/s11709-017-0425-7
  本期目录
A multi-attribute decision making approach of mix design based on experimental soil characterization
Amit K. BERA1, Tanmoy MUKHOPADHYAY2(), Ponnada J. MOHAN1, Tushar K. DEY3
1. Faculty of Science & Technology, The ICFAI University, Dehradun, India
2. College of Engineering, Swansea University, Swansea, UK
3. Department of Civil Engineering, National Institute of Technical Teachers’ Training and Research (NITTTR) Kolkata, India
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Abstract

The clay mineral composition is one of the major factors that governs the physical properties of silty sand subgrade. Therefore, a thorough knowledge of mineral composition is essential to predict the optimum engineering properties of the soil, which is generally characterized by different indices like maximum dry density (MDD), California bearing ratio (CBR), unconfined compressive strength (UCS) and free swelling index (FSI). In this article, a novel multi-attribute decision making (MADM) based approach of mix design has been proposed for silty sand – artificial clay mix to improve the characteristic strength of a soil subgrade. Experimental investigation has been carried out in this study to illustrate the proposed approach of selecting appropriate proportion for the soil mix to optimize all the above mentioned engineering properties simultaneously. The results show that a mix proportion containing approximately 90% silty sand plus 10% bentonite soil is the optimal combination in context to the present study. The proposed methodology for optimal decision making to choose appropriate combination of bentonite and silty sand is general in nature and therefore, it can be extended to other problems of selecting mineral compositions.

Key wordssilty sand    bentonite soil    soil mix design    multi-attribute decision making
收稿日期: 2016-11-27      出版日期: 2018-05-22
Corresponding Author(s): Tanmoy MUKHOPADHYAY   
 引用本文:   
. [J]. Frontiers of Structural and Civil Engineering, 2018, 12(3): 361-371.
Amit K. BERA, Tanmoy MUKHOPADHYAY, Ponnada J. MOHAN, Tushar K. DEY. A multi-attribute decision making approach of mix design based on experimental soil characterization. Front. Struct. Civ. Eng., 2018, 12(3): 361-371.
 链接本文:  
https://academic.hep.com.cn/fsce/CN/10.1007/s11709-017-0425-7
https://academic.hep.com.cn/fsce/CN/Y2018/V12/I3/361
judgment of preferencenumerical rating
extremely preferred9
very strongly to extremely preferred8
very strongly preferred7
strongly to very strongly preferred6
strongly preferred5
moderately to strongly preferred4
moderately preferred3
equally to moderately preferred2
equally preferred1
Tab.1  
criterionC1C2C3C4
C1a11a12a13a14
C2a21a22a23a24
C3a31a32a33a34
C4a41a42a43a44
total∑ = A∑ = B∑ = C∑ = D
Tab.2  
criterionA1A2A3A4
C1C2C3C4relative priority vectorA1×A2A3/A2
C1a11a12a13a14EIM
C2a21a22a23a24FJN
C3a31a32a33a34GKO
C4a41a42a43a44HLP
total∑ = A∑ = B∑ = C∑ = D---
Tab.3  
size of matrixrandom consistency
10
20
30.58
40.9
51.12
61.24
71.32
81.41
91.45
101.49
Tab.4  
C1L1L2L3
L1a1a2a3
L2a4a5a6
L3a7a8a9
total∑ = S∑ = T∑ = U
Tab.5  
C1C2C3C4
L1a11a12a13a14
L2a21a22a23a24
L3a31a32a33a34
Tab.6  
alternativescorerank
L1P1
L2Q3
L3R2
Tab.7  
Fig.1  
Fig.2  
propertiesvalues
optimum moisture content (OMC) (%)10.2
maximum dry density (MDD) (g/cc)2.021
specific gravity G [24]2.62
average grain size D50 (mm)0.37
coefficient of uniformity Cu2.35
coefficient of curvature Cc1.15
classification as per Indian standard [25]SM
typical soil classificationsilty sand
Tab.8  
propertiesvalues
optimum moisture content (OMC) (%)25
maximum dry density (MDD) (g/cc)1.241
specific gravity G1.93
liquid limit (%)68.0
plastic limit (%)39.5
plasticity index28.5
unified soil classificationCL
AASHTO soil classificationA-6
type of soilclay of low compressibility
Tab.9  
Sl. Nosymbolmix designation
1M1100% bentonite soil
2M250% silty sand+50% bentonite soil
3M360% silty sand+ 40% bentonite soil
4M470% silty sand+ 30% bentonite soil
5M580% silty sand+ 20% bentonite soil
6M690% silty sand+ 10% bentonite soil
7M7100% silty sand
Tab.10  
Fig.3  
mix designationsymbolMDD (gm/cc)CBR (%)UCS (kg/cm2)FSI (%)
100% bentonite soilM11.2410.770.55165
50% silty sand+50% bentonite soilM21.5870.890.8157
60% silty sand+ 40% bentonite soilM31.6800.900.7550
70% silty sand+ 30% bentonite soilM41.7942.500.5630
80% silty sand+ 20% bentonite soilM51.8023.300.3628
90% silty sand+ 10% bentonite soilM61.7654.900.1211
100% silty sandM71.6908.600.000
Tab.11  
Fig.4  
attribute/alternativeMDDCBRUCSFSI
M21.5870.890.8157
M51.8023.30.3628
M61.7654.90.1211
weights0.150.50.250.1
Tab.12  
attribute/alternativeMDDCBRUCSFSIscore (SAW)rank
M20.88060.1816110.49223
M510.67340.44440.39280.63712
M60.979410.14810.19290.78391
Tab.13  
attribute/alternativeMDDCBRUCSFSIscore (WPM)rank
M20.88060.1816113.65653
M510.67340.44440.39284.05062
M60.979410.14810.19295.16171
Tab.14  
criterionMDDCBRUCSFSI
MDD10.250.52
CBR4144
UCS20.2512
FSI0.50.250.51
Tab.15  
criterionA1A2A3A4
MDDCBRUCSFSIrelative priority vectorA1×A2A3/A2
MDD10.250.520.145440.5857142864.027285
CBR41440.553972.3380952384.22063
UCS20.25120.19960.8309523814.163022
FSI0.50.250.510.100990.4120039684.079568
Tab.16  
MDDCBRUCSFSI
M2M5M6M2M5M6M2M5M6M2M5M6
M210.16670.1428610.166670.12516810.250.14286
M5716610.166670.1666666714410.25
M660.1666718610.1250.251741
Tab.17  
MDDCBRUCSFSI
M20.072142860.062232220.739050.07784
M50.672857140.222855710.191550.23443
M60.2550.714912060.069390.68773
Tab.18  
alternativescorerank
M20.20033
M50.28322
M60.51641
Tab.19  
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