Rakshya Shrestha and Dr Abir Al-Tabbaa Email: rs544@cam.ac.uk

Geotechnical and Environmental Research Group University of Cambridge

Development of Predictive Models for Cement-Stabilized Soils

Overview

Deep Soil Mixing

Factors affecting Strength and Strength Variability

Data collation and Database Development

Artificial Neural Networks (ANNs)

ANN modelling Results and Discussion

Summary

Deep Soil Mixing(DSM)Soil, Binder and mechanical mixing tools.

Wide range of soils; sand, silt, clay, organic soils and peat.

Wide range of conventional and novel binders; Cement, Lime, Slag, Fly Ash.

Different construction strategies in different parts of the world.

Increased strength, Decreased compressibility and permeability.

Large number of variables in wide range and complex interaction.

(Courtesy of Eco Foundation, CDM, Association, LCM and Porbaha et al.)

Factors affecting Strength

SN Influencing factors Details

1. Soil Soil type, Grain size distribution, Soil water content, Atterberg limits, Organic matter content

2. Binder Binder dosage, Binder chemical composition

3. Mixing Conditions Water: binder ratio, Mixing tool type, Mixing time, Mixing speed

4. Curing Conditions Curing time, Curing temperature, Curing stress

5. Sampling and testing Conditions

Sample shape, Sample size, Testing methods, Strain rate

(Source: Terashi,1997)

Variation of the Strength (UCS)

Effect of Soil Type Effect of Binder Type

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Cement Dosage(kg/m3)

Silt and Clay

Sand

Gravel

(Taki and Yang,1991) (Ahnberg et al,2006)

Data CollationDSM projects world-wide.

Haneda Airport Expansion Project, JapanEuroSoilStab Project, SwedenLand Transport Authority Projects, SingaporeSMiRT Project, UK

Construction of DSM database

apan

ProjectsMixing

Sand Silt ClayOrganic

Soil Initial water Clay Organic matter c s f

Binder dosage water:binder Time Temp RH Stress UCS(Mpa FStrain E(Mpa)

Kitazume, 2007Jegandan, 2010

Kawasaki et al., 1981Osman, 2007

Chin, 2006Xiao, 2009Kamruzzaman, 2002

Ahnberg, 2006EuroSoilStab,2000Hernandez-Martinez, 2006

VariablesBinder Curing TestingSoil

Inorganic clays Organic clays Peat

Database Analysis

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Liquid LimitMed stiff kaolin clay Clay 1 Clay 2 Tokyo 2Tokyo 3 Kanagawa 1 Osaka 1 Chiba 1Chiba- 2 Aichi-1 Aichi-2 Hiroshima-1Fukuoka-1 Fukuoka-2 Aomori-1 Mie-1Mie-2 Upper marine clay Rotoclay kaolin Nanticoke clayOttawa clay Hongkong clay Helsinki clay Linkoping clayLoftabro clay Tokyo-1 Tokyo-4 Kanagawa- 2Osaka-2 Akita-1 Ibaragi-1 Ibaragi-2Soft marine clay Silty clay Home Rule Kaolin clay EPK Kaolin claySpeswhite kaolin clay Black cotton clay Brown earth clay Red Earth clayAriake clay Soft Bangkok clay Kupio Finnish clay A-line 50 LL line

CL

CH

Artificial Neural Networks (ANNs)Hidden units are flexible non-linear functions e.g. hyperbolic tangent.Weights and Biases determined through iterative ‘training’ process.Multiple non-linear regression analysis.Minimization of an error function using Bayesian approach.The problem of over-fitting is monitored by using a ‘test set’.

UCS

Organic content

Clay water content

Sand content

Cement content

Water/cement

Curing time

Silt Content

Clay Content

Input layer

Hidden layer

Output layer

Liquid Limit

Plastic Limit

PI

LI

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Complexity of the model

Sample DataSetReferences Given clay name Clay type Claywater LL PL PI LI Sand Silt Clay Organicmatter Cement w/c Age UCSAichi-1 Clayey silt 99.3 83.4 23.4 60 1.3 5 61 34 0.6 20 7.4 28 1980Aichi-1 Clayey silt 99.3 83.4 23.4 60 1.3 5 61 34 0.6 10 14.2 7 578Aichi-1 Clayey silt 99.3 83.4 23.4 60 1.3 5 61 34 0.6 30 5.1 60 2940Aichi-1 Clayey silt 99.3 83.4 23.4 60 1.3 5 61 34 0.6 30 5.1 7 1180Aichi-1 Clayey silt 99.3 83.4 23.4 60 1.3 5 61 34 0.6 20 7.4 7 956Aichi-1 Clayey silt 99.3 83.4 23.4 60 1.3 5 61 34 0.6 10 14.2 60 969Aichi-1 Clayey silt 99.3 83.4 23.4 60 1.3 5 61 34 0.6 10 14.2 28 890Ariake clay Silty clay 106 120 57 63 0.8 1 44 55 0 10 11 7 513Ariake clay Silty clay 130 120 57 63 1.2 1 44 55 0 10 13 7 345Ariake clay Silty clay 106 120 57 63 0.8 1 44 55 0 10 11 14 760Ariake clay Silty clay 130 120 57 63 1.2 1 44 55 0 10 13 60 600Ariake clay Silty clay 130 120 57 63 1.2 1 44 55 0 15 9 120 2169Ariake clay Silty clay 160 120 57 63 1.6 1 44 55 0 15 11 60 1213Ariake clay Silty clay 106 120 57 63 0.8 1 44 55 0 20 5 60 3188Black cotton clay Silty clay 145.5 97 35 62 1.8 2 37 61 0 7 20 56 119Black cotton clay Silty clay 145.5 97 35 62 1.8 2 37 61 0 10 15 56 223Black cotton clay Silty clay 97 97 35 62 1.0 2 37 61 0 10 10 14 630Black cotton clay Silty clay 194 97 35 62 2.6 2 37 61 0 10 20 56 105Black cotton clay Silty clay 194 97 35 62 2.6 2 37 61 0 19 10 14 132Linkoping clay HP clay 78 70 24 46 1.2 0 0 63 1 10 6.8 360 740Linkoping clay HP clay 78 70 24 46 1.2 0 0 63 1 10 6.8 7 372Linkoping clay HP clay 78 70 24 46 1.2 0 0 63 1 10 6.8 28 516Loftabro clay HP clay 89 66 23 43 1.5 0 0 72 1 10 7.2 360 1025Loftabro clay HP clay 89 66 23 43 1.5 0 0 72 1 10 7.2 7 286Loftabro clay HP clay 89 66 23 43 1.5 0 0 72 1 10 7.2 90 750Loftabro clay HP clay 89 66 23 43 1.5 0 0 72 1 10 7.2 28 543

Kawasaki et al., 1981

Miura et al., 2001Narendra et al., 2006

Ahnberg,2006

Variable Statistics

Input variable Min Max MeanStandard DeviationClay water content (%) 38.0 305.1 94.2 36.9Liquid Limit (%) 32.0 230.0 77.1 26.6Plastic Limit (%) 15.0 72.6 32.0 10.4Plasticity Index (%) 8.0 157.4 45.1 18.7Liquidity Index 0.4 3.7 1.5 0.7Sand content (%) 0.0 44.0 10.8 12.7Silt content (%) 0.0 82.0 41.3 17.8Clay content (%) 5.0 100.0 47.2 20.4Organic matter content (%) 0.0 3.0 0.4 0.7Cement content (%) 1.0 60.0 17.0 11.9Water to cement ratio 2.3 45.0 9.2 5.9Curing time (days) 7.0 420.0 44.4 64.7

UCS(kPa) 15 10356 1247 1608

Model Development and ValidationSoftware- Neuromat Model Manager (Sourmail,2004). 100 models with differing complexity- trained and tested. 9 models in the committee- final model. Performed well in the entire dataset and the validation set.

0.300.320.340.360.380.400.420.440.46

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Model Predictions

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Brown Earth Indian clay Water content = 60 % LL = 60 %, PL = 23 %, PI = 37 %, LI =1 Sand = 20 %, Silt = 34 %, Clay = 46 %

SummaryLarge number of variables in wide range and complex interaction. Data collection and collation utilizes the existing data and facilitates investigation of potential correlations.

ANNs are feasible alternatives to soil mix technology data mining.

• Known trends emulated and reasonable predictions of strength obtained. • Predictions accompanied by error bars which quantify the uncertainty of fitting. • Developed models only valid for conditions found in the dataset (e.g. clay soil type)

Ongoing work to make database well populated. Experimental validation.