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Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17

Editor
C. H. Dagli
C. H. Dagli
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ISBN-10:
0791802655
No. of Pages:
650
Publisher:
ASME Press
Publication date:
2007

Resilient modulus (MR) is an important material property in pavement design. Although generally determined by cyclic triaxial testing, the MR values can be estimated from correlations with other material properties. Consequently, a combined laboratory and modeling study was undertaken to develop artificial neural network (ANN) models for subgrade soils. Sixty-three soil samples from different sites were tested for Atterberg limits, wet sieving, dry sieving, moisture-density, MR, and unconfined compressive strength. The following parameters were used in the development of the ANN models: moisture content, dry density, plasticity index, percent passing No. 200 sieve, and compressive...

Abstract
Introduction
Overview of Selected Previous Studies
Neural Network Modeling
Model Evaluation
Concluding Remarks
Nomenclature
Acknowledgement
References
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