Neural Modeling of Kansas Soil Swelling

Abstract

Damage due to soil swelling is very noticeable in a wide spectrum of structures such as roads, buildings, canal linings, and landfill liners. To control or overcome any damage, swelling soils are commonly chemically stabilized (e.g., by lime treatment). To evaluate the severity of swelling and design for the best and most economical stabilization strategy, an accurate assessment of the swell potential is acquired. In this study, a huge data base representing 413 soils retrieved from 45 different projects covering 28 counties in Kansas was used to develop prediction models. Neural network-based models and a statistical model were developed. It is shown that neural models provide significant improvements in prediction accuracy over statistical models.