Mr. Mehdi Abbasi, Prof. Gholamreza Lashkaripour, Prof. Naser Hafezi Moghaddas, Dr. Hossein Sadeghi,
Volume 19, Issue 1 (6-2025)
The elastic modulus is considered one of the most essential parameters in the analysing and designing deep foundations and underground structures. Accurate determination of this parameter usually requires expensive and time-consuming in-situ testing, and validating its accuracy poses significant challenges. Therefore, researchers have consistently focused on developing empirical models based on geotechnical parameters. In the present study, multiple linear regression models, including general, coarse-grained soil, and fine-grained soil models, were developed to predict the elastic modulus using data obtained from 180 boreholes totaling 5,783 meters in the Mashhad Metro Line 3 project.. Out of 489 pressuremeter tests, 160 datasets were selected based on the availability of complete geotechnical parameters at the same depth. The analysis incorporated the influence of various parameters, including the percentage of sand, silt, and fine particles; grain size characteristics (D10, D30, D60, uniformity coefficient, and coefficient of curvature); Atterberg limits; moisture content; natural and dry density; specific gravity; and cementation indicators (gypsum, carbonate, and organic matter), as well as depth and in-situ stress. The final regression models were developed using a backward stepwise method, implemented through Python programming. The resulting regression equations were derived, and comparative plots between predicted and actual elastic modulus values were presented. The findings demonstrate that the proposed model offers reliable accuracy in estimating the elastic modulus. To evaluate the accuracy of the proposed models in predicting soil elastic modulus, an independent dataset of 39 pressuremeter test results, including both fine- and coarse-grained soils, was used. Statistical indicators demonstrated that the overall model performed best (R²=0.79, MAPE=9.86%). Additionally, the low values of normalized RMSE confirmed the stability and acceptable accuracy of all models.
