The effects of deviator stress, molding moisture content, stabilizer type and content, curing period, and soil type on the resilient modulus (Mr) of lime- and cement-stabilized cohesive soils were investigated by using Hydrite R (kaolinite) and sodium bentonite (montmorillonite) blends. It was found that Mr increases with decreasing deviator stress, increasing lime and cement content, and extended curing period. Moisture variations around optimum had little effect on Mr with higher lime contents. Multiple regression analyses and Student's t-tests indicated that all the factors investigated were significant and could be related to Mr by predictive regression equations. For a given stabilizer type and content, the low-plasticity clay (CL) soil produced the best results. The cement-stabilized CL soil normal cured for 28 days produced the highest Mr value. However, cement stabilization was not found to be very effective for the high-plasticity clay (CH) soil. Mineralogical composition has a marked effect on the Mr of lime and cement-stabilized cohesive soils. Kaolinitic CL soils work better than montmorillonitic CH soils with both lime and cement.

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The ability to predict the geotechnical properties of subsurface soils using non-invasive geophysical measurements can be undeniably useful to the geotechnical engineer. Using laboratory data, we assess the potential of artificial neural networks to investigate the relations between geotechnical and electrical parameters characterizing a variety of soils. The geotechnical parameters are: fines content, mean grain size, mean pore size and the specific surface area. The electrical parameters obtained from low-frequency electrical measurements (4 Hz) include the resistivity amplitude, phase shift and the loss tangent. Relations that can be used to predict the geotechnical parameters of a soil given its electrical parameters are developed. The predictive capabilities of the neural networks are compared with traditional multivariate regression models. The performances of the neural network and regression models in predicting (a) the geotechnical parameter given the same electrical parameters as inputs and (b) the electrical parameters given the same geotechnical parameters as inputs are compared. In both cases, the neural network outperforms the multivariate regression as the neural network is able to capture and model the non-linear and complex relationships among the variables. The relative importance of the geotechnical parameters on the overall electrical conduction was examined using the neural networks. The results indicate that mean grain size and fines content are the two geotechnical parameters that influence phase-shift values the most; fines content and mean pore size influence the resistivity amplitude the most, whilst fines content and mean grain size influence the loss tangent the most. It was observed that of the four geotechnical parameters, the mean grain size influences the measured resistivity values the least.