Efficient surrogate model based probabilistic analysis of helical soil nailed wall under seismic conditions

Abstract

This study demonstrates an efficient and precise surrogate model to probabilistically analyze the helical soil nailed walls (HSNWs) under seismic conditions. The modified pseudo-dynamic (MPD) approach is employed which produces realistic results in comparison to its former counterparts. The unit weight (γ), internal friction angle of soil (ϕ), and shear wave velocity (V_s) are chosen as random variables. The influence of the normalized frequency, damping ratio, and size of the helix on the reliability of HSNW is presented in detail. The plots reveal that the proposed surrogate model is an accurate method with the value of coefficient of determination (R²) as 0.99 for the present study. In addition to it, it is 99 times more efficient when compared to the direct Monte-Carlo Simulation (MCS) (50000 iterations). For the same input parameters, the minimum number of helical nails required for a safe reinforced wall are less than the grouted and the driven ones, given their larger pull-out capacity. Moreover, the required number of helical soil nails (HSNs) computed from probabilistic analysis are 42.85% more than the deterministic one for a given set of input parameters which lay a strong base for performing the probabilistic analysis for a precise measure of stability.

Keywords:

• Helical soil nails
• surrogate modelling
• probabilistic analysis
• pullout capacity
• soil nailing

CRediT authorship contribution statement

Ekansh Agarwal: Methodology, Writing - Original draft preparation, Investigation, Software, Data curation, Validation. Mahesh Sharma: Conceptualization, Writing - review & editing, Resources, Supervision. Anindya Pain: Conceptualization, Writing - review & editing, Resources, Supervision.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research has been supported by CSIR (Council of Scientific and Industrial Research), India, CSIR-Central Building Research Institute (CSIR-CBRI), Roorkee, India, and Himachal Pradesh University (HPU), Shimla. EA acknowledges the financial support from CSIR, India (File No.: 31/0024(11097)/2021-EMR-I), MS from HPU, and AP from CSIR-CBRI, Roorkee, India, during the period of this work.