Resilient Modulus Prediction Models Based on Analysis of LTPP Data for Subgrade Soils and Experimental Verification

2007 ◽  
Vol 133 (9) ◽  
pp. 491-504 ◽  
Author(s):  
Ramesh B. Malla ◽  
Shraddha Joshi
2011 ◽  
Vol 374-377 ◽  
pp. 1796-1799
Author(s):  
Hong Xi Liu ◽  
Liang Zhou

Subgrade resilient modulus (MR) is very important for effective design of pavements. Several methods to estimate the resilient modulus were suggested in the past years. The main objective of this paper was to validate the correlation of MR with other physical properties of the subgrade soils. Cohesive soils representing major soil types in Shanghai were selected. The resilient modulus tests were conducted with UTM. Additional analysis was then performed to develop correlations between the model parameters and other soil properties. To verify the prediction models independently, laboratory MR tests were conducted on new subgrade soils. It was observed that the predicted MR values compared well with the laboratory measured values for the soil samples.


Author(s):  
Cara Fragomeni ◽  
Ahmadreza Hedayat ◽  
William Navidi ◽  
Evan Kuhn ◽  
David Thomas ◽  
...  

Author(s):  
Andrew G. Heydinger

One objective of the FHWA’s Long-Term Pavement Performance (LTPP) program is to determine climatic effects on pavement performance. The LTPP instrumentation program includes seasonal monitoring program (SMP) instrumentation to monitor the seasonal variations of moisture, temperature, and frost penetration. Findings from the SMP instrumentation are to be incorporated into future pavement design procedures. Data from SMP instrumentation at the Ohio Strategic Highway Research Program Test Road (US-23, Delaware County, Ohio) and other reported results were analyzed to develop empirical equations. General expressions for the seasonal variations of average daily air temperature and variations of temperature and moisture in the fine-grained subgrade soil at the test site are presented. An expression for the seasonal variation of resilient modulus was derived. Average monthly weighting factors that can be used for pavement design were computed. Other factors such as frost penetration, depth of water table, and drainage conditions are discussed.


2020 ◽  
Vol 32 (9) ◽  
pp. 06020011
Author(s):  
Behnam Ghorbani ◽  
Arul Arulrajah ◽  
Guillermo Narsilio ◽  
Suksun Horpibulsuk ◽  
Myint Win Bo

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