Title: DETERMINATION OF RESILIENT MODULUS FOR MAINE ROADWAY SOILS
Abstract: The Maine Department of Transportation commissioned this study to examine methods of obtaining resilient modulus for use in pavement design. Resilient modulus is a measure of soil layer stiffness and is highly subjective to density, moisture content, soil fabric structure, compaction method, laboratory equipment compliance, and technician skill. As a result, several alternative test methods have been proposed. These alternative test methods include resilient modulus correlation to results from torsional shear and resonant column tests, a modified gyratory test machine normally used for testing asphalt concrete specimens, and a small-scale falling weight deflectometer (FWD) device. The study used resilient modulus test data of 14 Maine soils published by Law Engineering (1992). Soil index property data and FWD data were obtained from the Strategic Highway Research Program's Long Term Pavement Performance (LTPP) database. Three methods for determining resilient modulus were examined: (1) backcalculation of resilient modulus using computer software, (2) determination of the K sub n constants for various constitutive resilient modulus equations by linear regression analysis, and (3) correlations between resilient modulus and soil property data and stress state. Computer backcalculation was done using MODCOMP 4 and MODULUS 5.1. The backcalculated resilient moduli did not compare well with the laboratory moduli when the programs automatically estimated the depth to hard layer and outliers were neglected. The K sub n constants for 7 common constitutive relationships were developed for 14 Maine soils using linear regression. Two equations correlating resilient modulus to dry density, water content, grain size distribution and stress state were also generated from linear regression techniques. California bearing ratio (CBR) does not correlate well with resilient modulus, therefore, no correlations involving CBR were examined.
Publication Year: 1999
Publication Date: 1999-12-10
Language: en
Type: article
Access and Citation
Cited By Count: 4
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot