📊 Multivariate Soil/Rock Property Databases

Simultaneously Measured Properties for Correlation Studies

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Leader: Yu Wang (City University of Hong Kong)

These databases are for soil/rock samples with simultaneously measured properties (e.g., two clay samples at the same depth in the same local site are tested, one to obtain Atterberg limits and the other to obtain undrained shear strength). Some databases are genuinely multivariate, and some are only partially multivariate. These simultaneously measured properties are recorded in the same line (e.g., same excel row).

The names of the databases are in the format of A/B/C: A = material type (CLAY or SAND or ROCK), B = Number of parameters of interest, C = Number of data points.

Please contact Yu Wang if you want to contribute databases.

Soil Databases

CLAY/5/345
Genuinely multivariate | 37 sites worldwide | OCR 1~4 | Sensitive to quick clays

Parameters

LI = Liquidity index

su = Undrained shear strength (kPa)

sure = Remolded undrained shear strength (kPa)

sv = Vertical total stress (kPa)

sp = Preconsolidation stress (kPa)

Database Compiler

J Ching (National Taiwan University)

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Reference

Ching, J. and Phoon, K.K. (2012). Modeling parameters of structured clays as a multivariate normal distribution. Canadian Geotechnical Journal, 49(5), 522-545. (https://doi.org/10.1139/t2012-015)

CLAY/6/535
Genuinely multivariate | 40 sites worldwide | OCR 1~10

Parameters

su/sv = Normalized undrained shear strength

OCR = Overconsolidation ratio

qt1 = Normalized cone tip resistance

qtu = Uncorrected cone tip resistance (MPa)

(u2-u0)/sv = Normalized excess pore pressure

Bq = Pore pressure ratio

Database Compiler

J Ching

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Reference

Ching, J., Phoon, K.K., and Chen, C.H. (2014). Modeling CPTU parameters of clays as a multivariate normal distribution. Canadian Geotechnical Journal, 51(1), 77-91. (https://doi.org/10.1139/cgj-2012-0259)

CLAY/10/7490
Partially multivariate | 251 studies | OCR 1~10 | Insensitive to quick clays

Parameters

LL = Liquid limit (%)

PI = Plasticity index (%)

LI = Liquidity index

sv/Pa = Normalized vertical total stress

sp/Pa = Normalized preconsolidation stress

su/sv = Normalized undrained shear strength

St = Sensitivity

qt1 = Normalized cone tip resistance

qtu = Uncorrected cone tip resistance

Bq = Pore pressure ratio

Database Compiler

J Ching

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Reference

Ching, J. and Phoon, K.K. (2014). Transformations and correlations among some clay parameters - a multivariate model. Canadian Geotechnical Journal, 51(6), 663-685. (https://doi.org/10.1139/cgj-2013-0262)

F-CLAY/7/216
Finland clays | Genuinely multivariate | 24 sites in Finland | OCR 1~6 | Sensitive to quick clays

Parameters

LL = Liquid limit (%)

PL = Plastic limit (%)

w = Natural water content (%)

sv = Vertical total stress (kPa)

sp = Preconsolidation stress (kPa)

su = Undrained shear strength (kPa)

St = Sensitivity

Database Compiler

M D'Ignazio (Norwegian Geotechnical Institute)
TT Länsivaara (Tampere University)

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Reference

D'Ignazio, M., Phoon, K.K., Tan, S.A. & Länsivaara, T.T. (2016). Correlations for undrained shear strength of Finnish soft clays. Canadian Geotechnical Journal, 53, 1628-1645. (https://doi.org/10.1139/cgj-2016-0037)

S-CLAY/7/168
Scandinavia clays | Partially multivariate | 22 sites in Norway & Sweden | OCR 1~5 | Sensitive to quick clays

Parameters

LL = Liquid limit (%)

PL = Plastic limit (%)

w = Natural water content (%)

sv = Vertical total stress (kPa)

sp = Preconsolidation stress (kPa)

su = Undrained shear strength (kPa)

St = Sensitivity

Database Compiler

M D'Ignazio (Norwegian Geotechnical Institute)
TT Länsivaara (Tampere University)

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Reference

D'Ignazio, M., Phoon, K.K., Tan, S.A. & Länsivaara, T.T. (2016). Correlations for undrained shear strength of Finnish soft clays. Canadian Geotechnical Journal, 53, 1628-1645. (https://doi.org/10.1139/cgj-2016-0037)

J-CLAY/5/124
Jiangsu clays (China) | Genuinely multivariate | 16 sites in Jiangsu Province, China | Soft to stiff clayey soils and silty clay soils

Parameters

Mr = Resilient modulus (MPa)

qc = Cone tip resistance (MPa)

fs = Sleeve friction (kPa)

w = Water content (%)

ρd = Dry density (g/cm³)

Database Compiler

Guojun Cai (Southeast University, China)

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Reference

Liu, S., Zou, H., Cai, G., Bheemasetti, B.V., Puppala, A.J. & Lin, J. (2016). Multivariate correlation among resilient modulus and cone penetration test parameters of cohesive subgrade soils. Engineering Geology, 209, 128-142. (https://doi.org/10.1016/j.enggeo.2016.05.018)

SH-CLAY/11/4051
Shanghai clays (China) | Partially multivariate | 51 sites in Shanghai, China

Parameters

LL = Liquid limit (%)

PI = Plasticity index (%)

LI = Liquidity index

e = Void ratio

K0 = Coefficient of earth pressure at rest

s'v/Pa = Normalized effective vertical stress

su(UCST)/s'v = Normalized undrained shear strength from UC test

St(UCST) = Sensitivity from UC test

su(VST)/s'v = Normalized undrained shear strength from vane test

St(VST) = Sensitivity from vane test

ps/sv = Normalized cone resistance

Database Compiler

Doming Zhang (Tongji University)

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Reference

Zhang, D., Zhou, Y., Phoon, K.K., and Huang, H. (2020). Multivariate probability distribution of Shanghai clay properties. Engineering Geology, 105675. (https://doi.org/10.1016/j.enggeo.2020.105675)

FI-CLAY/14/856
Finland clays | Partially multivariate | 33 sites in Finland | OCR 0.3~41 | Medium sensitive to quick clays, organic soils (8%), and clayey silts (3%)

Parameters

w = Water content (%)

e = Void ratio

LL = Liquid limit (%)

F = Fall cone strength (kPa)

PL = Plastic limit (%)

γ = Unit weight (kN/m³)

Org = Organic content (%)

Cl = Clay content (%)

su = Undrained shear strength (kPa)

St = Sensitivity

sp = Preconsolidation stress (kPa)

OCR = Overconsolidation ratio

Cc = Compression index

Cs = Swelling index

Database Compiler

Monica Löfman (Aalto University)
Leena Korkiala-Tanttu (Aalto University)

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Reference

Korkiala-Tanttu, L.K. (2021). Transformation models for the compressibility properties of Finnish clays using a multivariate database. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. (https://doi.org/10.1080/17499518.2020.1864410)

SAND/7/2794
Partially multivariate | 176 studies | 85% reconstituted sands, 15% in-situ sands | Mostly NC clean sands

Parameters

D50 = Median particle size (mm)

Cu = Coefficient of uniformity

Dr = Relative density (%)

s'v/Pa = Normalized effective vertical stress

φ' = Effective friction angle (°)

qt1 = Normalized cone tip resistance

(N1)60 = Normalized SPT blow count

Database Compiler

JR Chen (National Chi Nan University)
J Ching (National Taiwan University)

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Reference

Ching, J., Lin, G.H., Chen, J.R., and Phoon, K.K. (2017). Transformation models for effective friction angle and relative density calibrated based on a multivariate database of coarse-grained soils. Canadian Geotechnical Journal, 54(4), 481-501. (https://doi.org/10.1139/cgj-2016-0318)

SAND-Small/9/939
Partially multivariate | 15 studies | Reconstituted clean sands

Parameters

D50 = Median particle size (mm)

Cu = Coefficient of uniformity

emin = Minimum void ratio

emax = Maximum void ratio

s'3 = Effective confining pressure (kPa)

s'1p = Peak deviator stress (kPa)

ec = Void ratio at critical state

Gmax = Small-strain shear modulus (MPa)

φ' = Effective friction angle (°)

Database Compiler

MK Lo (The Hong Kong Polytechnic University)
Xiao Wei (Zhejiang University)

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Reference

Lo, M.K., Wei, X., Chian, S.C., & Ku, T. (2021). Bayesian Network Prediction of Stiffness and Shear Strength of Sand. Journal of Geotechnical and Geoenvironmental Engineering, 147(5), 04021020. (https://doi.org/10.1061/(ASCE)GT.1943-5606.0002505)

FG-KSAT/6/1358
Permeability for fine-grained soils | Partially multivariate (mostly genuinely multivariate) | 33 studies | 31% lean clays, 5% silts, 38% fat clay, 20% elastic silts

Parameters

e = Void ratio

k = Saturated hydraulic conductivity (m/s)

LL = Liquid limit (%)

PL = Plastic limit (%)

PI = Plasticity index (%)

Gs = Specific gravity

Database Compiler

Shuyin Feng (University of Bristol)
Paul Vardanega (University of Bristol)

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References

Feng, S. and Vardanega, P.J. (2019a). Correlation of the hydraulic conductivity of fine-grained soils with water content ratio using a database. Proceedings of the Institution of Civil Engineers - Geotechnical Engineering, 173(4). (https://doi.org/10.1680/jenge.18.00166)

Feng, S. and Vardanega, P.J. (2019b). A database of saturated hydraulic conductivity of fine-grained soils: probability density functions. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 13(4): 255-261. (https://doi.org/10.1080/17499518.2019.1652919)

CLAY-Cc/6/6203
Partially multivariate | 429 studies | 85% of the records are undisturbed clays, 15% are reconstituted clays

Parameters

LL = Liquid limit (%)

PI = Plasticity index (%)

w = Water content (%)

e = Void ratio

Cc = Compression index

Cur = Unloading-reloading index

Database Compiler

Jianye Ching (National Taiwan University)

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Reference

Ching, J., Phoon, K.K., and Wu, C.T. (2022). Data-centric quasi-site-specific prediction for compressibility of clays. Canadian Geotechnical Journal. (http://doi.org/10.1139/cgj-2021-0658)

CG/KSAT/7/1278
Hydraulic conductivity for saturated granular materials | Partially multivariate (mostly genuinely multivariate) | 53 studies | Property ranges provided in database

Parameters

Gs = Specific gravity

k = Hydraulic conductivity (mm/s)

D10 = Effective particle size (mm)

D50 = Median particle size (mm)

CU = Coefficient of uniformity

CZ = Coefficient of gradation

e = Void ratio

Database Compiler

Shuyin Feng (Birmingham City University)
Paul J. Vardanega (University of Bristol)

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Reference

Feng, S., Barreto, D., Imre, E., Ibraim, E. & Vardanega, P.J. (2023). Hydraulic conductivity of saturated granular materials: a new database and hyperbolic model. Géotechnique. (https://doi.org/10.1680/jgeot.22.00127)

SOIL/2/2433
Genuinely bivariate | 16 regions worldwide | N 0.9~147.9, Vs (m/s) 55.0~1135.5 | 35% sandy soils, 11% silty soils, 18% clayey soils, 11% Sandy silt/silty sand, 25% of the data points do not contain soil type information.

Parameters

N = SPT blow count

Vs = Shear wave velocity (m/s)

Database Compiler

Jie Zhang (Tongji University)
Shihao Xiao (Tongji University)

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Reference

Xiao, S.H., Zhang, J., Ye, J.M., and Zheng, J.G. (2021). Establishing region-specific N-Vs relationships through hierarchical Bayesian modeling. Engineering Geology, 106105. (https://doi.org/10.1016/j.enggeo.2021.106105)

Rock Databases

ROCK/9/4069
Partially multivariate | 184 studies | 17% igneous, 37% sedimentary, 26% metamorphic cases. 20% of the cases do not contain rock class information.

Parameters

n = Porosity (%)

γ = Unit weight (kN/m³)

RL = Los Angeles abrasion loss (%)

BPI = Point load strength index (MPa)

σbt = Brazilian tensile strength (MPa)

Is50 = Point load strength (MPa)

Vp = P-wave velocity (km/s)

σci = Uniaxial compressive strength (MPa)

Ei = Young's modulus (GPa)

Database Compiler

J Ching (National Taiwan University)

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Reference

Ching, J., Li, K.H., Phoon, K.K., & Weng, M.C. (2018). Generic transformation models for some intact rock properties. Canadian Geotechnical Journal, 55(12), 1702-1741. (https://doi.org/10.1139/cgj-2017-0537)

ROCK/10/4025
Partially multivariate | 95 case studies | 23 countries | Intact rocks: 35.4% igneous, 54.8% sedimentary, and 9.2% metamorphic, 0.6% unclassified samples

Parameters

n = Porosity (%)

γ = Unit weight (kN/m³)

RL = Los Angeles abrasion loss (%)

BPI = Point load strength index (MPa)

σbt = Brazilian tensile strength (MPa)

Is50 = Point load strength (MPa)

Vp = P-wave velocity (km/s)

σci = Uniaxial compressive strength (MPa)

Ei = Young's modulus (GPa)

mi = Hoek-Brown material constant

Database Compiler

Maria Ferentinou (Liverpool John Moores University)

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Reference

Muzamhindo, H. and Ferentinou, M. (2023). Generic compressive strength prediction model applicable to multiple lithologies based on a broad global database. Probabilistic Engineering Mechanics, 71, 103400. (https://doi.org/10.1016/j.probengmech.2022.103400)

ROCKMass/9/5876
Partially multivariate | 225 studies covering 67 countries/regions | 17% igneous, 37% sedimentary, and 26% metamorphic cases. 20% of the cases do not contain rock class information.

Parameters

RQD = Rock quality designation (%)

RMR = Rock mass rating

Q = Q-system (Barton)

GSI = Geological strength index

Em = Rock mass deformation modulus (GPa)

Eem = Em Rock mass deformation modulus from empirical equations (GPa)

Edm = Rock mass deformation modulus from direct measurements (GPa)

Ei = Intact rock deformation modulus (GPa)

σci = Uniaxial compressive strength (MPa)

Database Compiler

J Ching (National Taiwan University)

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Reference

Ching, J., Phoon, K.K., Ho, Y.H., and Weng, M.C. (2020). Quasi-site-specific prediction for deformation modulus of rock mass. Canadian Geotechnical Journal.

Disclaimer and Restrictions

The data presented here are for informational use only; no warranties, either expressed or implied, regarding the accuracy or reliability of the data are provided. Use of this data releases the database owner and TC304 of any liability of any kind. The databases provided here are intended to serve the profession for educational purposes or for benchmarking of analyses within professional environments (e.g., public agencies, engineering firms, etc.). Use of this data requires acknowledgement as described above. Furthermore, this data may not be used for direct profit, e.g., within proprietary software, without explicit agreement between the database owner and the entity. The use of this data within an unauthorized manner, as described herein, shall result in the forfeit of the right to use the data and the associated monetary gross revenues. Breach of this restriction shall result in prosecution.