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Application of multi linear regression (MLR) analysis for predicting settlement reduction ratio (Sr) of stone columns reinforced soft ground by Hardik V. Gajjar; Ashish Srivastava; Vinodray D. Ujeniya is a Engineering article available to read on EtoBox.

What is Application of multi linear regression (MLR) analysis for predicting settlement reduction ratio (Sr) of stone columns reinforced soft ground about?

The stability of foundations lying on poor subsoil necessitates settlement predictions. Stone columns (SCs) are well-known as one of the most common soil improvement solutions for increasing bearing capacity and reducing the settlement of structures built on poor subsoil. For estimating the settlement of SC reinforced ground, a variety of estimation approaches have been developed. In the present work, an attempt has been made to develop multi-linear regression model for predicting the settlement reduction ratio (Sr) for stone column reinforced soft soil. For all the cases 2D finite element tool was used to quantify the influence of parameters such as (i) Area Replacement ratio (As), (ii) dt/b (dt = depth of treatment and b is the width of footing), and (iii) Modular Ratio (Ec/Es). Moreover, to confirm the reliability of the MLR model, 5 samples of random soil and stone columns properties were analysed. The results suggest high level of prediction accuracy. Moreover, the observed R2 value of regression model was 0.92.

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Author

Hardik V. Gajjar; Ashish Srivastava; Vinodray D. Ujeniya

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)