A Numerical Study on Laterally Loaded Piles in Fiber-Reinforced Soil Under Static and Dynamic Loading.
Published In: Sādhanā: Academy Proceedings in Engineering Sciences, 2026, v. 51, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Suman, Ankit Kumar; Biswas, Sanjit; Rajeswari, J S 3 of 3
Abstract
This study focuses on enhancing the lateral load-bearing capacity of single piles under static and dynamic loading scenarios by incorporating Fiber-Reinforced Soil (FRS) in the topsoil layer surrounding the pile. Utilising 3D finite element analyses, the load-deflection behaviour of the pile was investigated under varying conditions. Two types of fibers (nylon and papyrus) are examined alongside three different aspect ratios (10, 15, and 20) of the pile. Our findings underscore the profound influence of topsoil layer properties on the capacity of piles for lateral loading. Introducing FRS at an optimal depth, approximately 2.5–3 times the pile diameter, and with an area spanning 5–7 times the pile diameter, notably improves the pile's stiffness and load-bearing capability. Further, the study unveils that the system's resonant frequency shifts to a higher value when FRS is introduced, indicating improved resistance to dynamic loads. This research provides valuable insights for optimising pile design, offering a practical approach to enhance the stability and performance of deep foundations in the face of growing demands for innovative and resilient structural solutions. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Sādhanā: Academy Proceedings in Engineering Sciences. 2026/03, Vol. 51, Issue 1, p1
- Document Type:Article
- Subject Area:Physics
- Publication Date:2026
- ISSN:0256-2499
- DOI:10.1007/s12046-025-03031-2
- Accession Number:191498111
- Copyright Statement:Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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