American Society of Civil Engineers

Quantitative Estimation of Clay Mineralogy in Fine-Grained Soils

by Bhaskar Chittoori, Ph.D., (Faculty Associate-Research, Dept. of Civil Engineering, The Univ. of Texas at Arlington, Arlington, TX 76019. E-mail: and Anand J. Puppala, Ph.D., P.E., (corresponding author), M.ASCE, (Professor, Dept. of Civil Engineering, The Univ. of Texas at Arlington, Arlington, TX 76019. E-mail:

Journal of Geotechnical and Geoenvironmental Engineering, Vol. 137, No. 11, November 2011, pp. 997-1008, (doi:

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Document type: Journal Paper
Discussion: by K. Prakash E-mail: and et al.    (See full record)
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Abstract: Stabilization design guidelines based on soil plasticity properties have certain limitations. Soils of similar plasticity properties can contain different dominant clay minerals, and hence, their engineering behavior can be different when stabilized with the same chemical additive and dosage. It is essential to modify stabilizer design guidelines by including clay mineralogy of the soil and its interactions with chemical additives used. Chemical properties of a soil including cation exchange capacity (CEC), specific surface area (SSA) and total potassium (TP) are dependent on clay mineral constituents, and an attempt is made in this study to develop a rational and practical methodology to determine both clay mineralogy distribution and dominant clay mineral in a soil by using three measured chemical soil properties and their analyses. This approach has been evaluated by determining and evaluating clay minerals present in artificial and natural clayey soils of known and unknown clay mineralogy. A total of twenty natural and six artificial soils were considered and used in the chemical analyses. Test results and subsequent analyses including the development of artificial neural network (ANN) based models are evaluated and described in this paper.

ASCE Subject Headings:
Expansive soils
Neural networks
Fine-grained soils
Soil properties

Author Keywords:
Clay mineralogy
Expansive soil
Mineral quantification
Artificial neural networks