0
Development of a Soil Chemical Stabilization Model

Excerpt

Adding chemical agent to stabilize problematic highway subgrade soil is a common engineering practice in the United States. Due to the fact that theoretical accomplishments in soil chemical stabilization lag far behind the engineering practice, laboratory testing, which is expensive and time-consuming, is almost always necessary to determine the effectiveness of the soil stabilizer in enhancing engineering properties of the soil. Over the years, large amount of valuable data from laboratory tests on stabilizing different soils with different chemical stabilizers was accumulated in the literature. Efforts to extract the relationships and associations from the existing test data in order to provide guidance for new soil chemical stabilization cases were carried out for many years, however, due to the technology (statistic regression) limitations, reliable models are still not available. In this paper, Artificial Neural Network (ANN) approach to study soil chemical stabilization was introduced. An ANN model to predict the unconfined compression strength (UCS) of the stabilized soil was built based on the experimental data from stabilizing three representative Kansas embankment soils with five chemical stabilizers. The results showed that the trained ANN model could precisely predict the UCS of stabilized soil. Furthermore, ANN model enables us to study the significance of each input factor, thus providing a powerful tool for optimizing the mixture and construction design.

  • Abstract
  • Introduction
  • ANN Model Development
  • Excel Application
  • Simulations
  • Concluding Remarks
  • References
Topics: Soil

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In