Prediction of swelling pressure of soil using artificial intelligence techniques

Environmental Earth Sciences

Volumn 61, Issue 2, 2010, Pages 393-403

  Das, Sarat Kumar ^a   Samui, Pijush ^b   Sabat, Akshaya Kumar ^c   Sitharam, T G ^d  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

^b TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

^c KIIT UNIVERSITY   (India)

^d INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

Artificial neural network; Expansive soil; Support vector machine; Swelling pressure

Indexed keywords

ARTIFICIAL INTELLIGENCE TECHNIQUES; ARTIFICIAL NEURAL NETWORK; ATTERBERG; CLAY CONTENT; CLAY FRACTIONS; COMPUTATIONAL INTELLIGENCE TECHNIQUES; DEGREE OF SATURATIONS; DRY DENSITY; ENVIRONMENTAL FACTORS; EXPANSIVE SOILS; INDEX PROPERTIES; LIQUID LIMITS; MOISTURE CONTENTS; NATURAL MOISTURES; PLASTICITY INDICES; RELATIVE PERFORMANCE; SOIL PARAMETERS; STATISTICAL PERFORMANCE; SWELLING PRESSURES; TRAINING SETS;

CLAY MINERALS; CRYSTALLOGRAPHY; FORECASTING; MINERALOGY; MINERALS; MOISTURE; MOISTURE DETERMINATION; NEURAL NETWORKS; SOILS; SUPPORT VECTOR MACHINES; SWELLING;

GEOLOGIC MODELS;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; ATTERBERG LIMIT; CLAY SOIL; DRY DENSITY; ENVIRONMENTAL FACTOR; EXPANSIVE SOIL; LIQUID LIMIT; MOISTURE CONTENT; PLASTICITY; PRESSURE EFFECT; SWELLING;

EID: 77953966803     PISSN: 18666280     EISSN: 18666299     Source Type: Journal    
DOI: 10.1007/s12665-009-0352-6     Document Type: Article

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