Chapter
Apr 26, 2012
A Radial Basis Function Neural Network Approach for Compressive Strength Prediction of Stabilized Soil
Authors: R. Ali Akbar Heshmati [email protected], Amir Hossein Alavi [email protected], Mohsen Keramati [email protected], and Amir Hossein Gandomi [email protected]Author Affiliations
Publication: Road Pavement Material Characterization and Rehabilitation: Selected Papers from the 2009 GeoHunan International Conference
Abstract
This study considers the use of artificial neural networks (ANNs) to predict the unconfined compressive strength (UCS) of soil-stabilizer mix. Radial basis function (RBF) as one of the most widely used ANN architectures is utilized to construct comprehensive models to relate the UCS of stabilized soil to the properties of natural soil and type and quantity of stabilizing additives. A comprehensive set of data obtained from previously published stabilization test results was used for model development. A subsequent parametric study was carried out and the trends of the results have been confirmed via previous laboratory studies. The RBF based estimates are compared with the experimental and numerical results of other researchers and found to be more accurate.
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© 2009 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Artificial intelligence and machine learning
- Compressive strength
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Foundation construction
- Foundations
- Geomechanics
- Geotechnical engineering
- Laboratory tests
- Material mechanics
- Material properties
- Materials engineering
- Neural networks
- Soil compression
- Soil dynamics
- Soil mechanics
- Soil mixing
- Soil properties
- Soil stabilization
- Soil strength
- Strength of materials
- Tests (by type)
Authors
Affiliations
Assistant Professor, College of Civil Engineering, Iran University of Science & Technology, Tehran, Iran. E-mail: [email protected]
Research Assistant, College of Civil Engineering, Iran University of Science & Technology, Tehran, Iran. E-mail: [email protected]
Research Assistant, College of Civil Engineering, Iran University of Science & Technology, Tehran, Iran. E-mail: [email protected]
Research Assistant, College of Civil Engineering, Tafresh University, Tafresh, Iran. E-mail: [email protected]
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