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Interaction Between Biaxial Geogrid and Solid Waste Materials: Laboratory Study and Artificial Neural Network Model Development

Sarkar, S and Prakash, S and Hegde, A (2023) Interaction Between Biaxial Geogrid and Solid Waste Materials: Laboratory Study and Artificial Neural Network Model Development. In: International Journal of Geosynthetics and Ground Engineering, 9 (6).

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Official URL: https://doi.org/10.1007/s40891-023-00498-z


The growing industrialism has led to the generation of unmanageable waste, which is usually dumped in landfills. Utilizing these materials in reinforced earth structures will not only help in large-scale recycling of the materials but also restrict the overutilization of geo-materials. The aim of the present study is to assess the interaction of biaxial geogrid with two different solid waste materials namely, steel slag (SS) and construction and demolition waste (CDW) and compare its performance with the conventional backfill material, sand. To achieve this, direct shear tests and pullout tests were conducted at various normal stress levels ranging from 25 to 200 kPa. The direct shear results revealed that both steel slag and CDW exhibits higher shear strength compared to sand. Similarly, the pullout test results indicated that the pullout resistance of the geogrid is higher in steel slag and CDW than in sand. The pullout resistance factor (F�) was evaluated to quantify the interaction of the geogrid with backfills. F� values were found to be greater than 1 at lower normal stress (25 kPa), and less than 1 at higher normal stress (200 kPa), indicating a stronger interaction at lower normal stresses. According to interaction coefficient ratio (ICR) values, the interaction of the geogrid with steel slag and CDW at 25 kPa normal stress was 38 and 33 higher, respectively, than that with sand. Furthermore, an artificial neural network (ANN) model was developed using data from the current study as well as data gathered from previous studies to predict the pullout resistance of geogrids in a wide range of geomaterials. An excellent prediction capability was observed with coefficient of determination (R 2) value more than 0.9. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Item Type: Journal Article
Publication: International Journal of Geosynthetics and Ground Engineering
Publisher: Springer Science and Business Media Deutschland GmbH
Additional Information: The copyright for this article belongs to author.
Keywords: Demolition; Geosynthetic materials; Neural networks; Reinforcement; Slags, Backfill-geogrid interaction; Construction and demolition waste; Earth structures; Geogrids; Normal stress; Pull-out test; Pullout resistance; Reinforced earth structure; Reinforced earths; Steel slag, Sand
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Date Deposited: 28 Feb 2024 11:58
Last Modified: 28 Feb 2024 11:58
URI: https://eprints.iisc.ac.in/id/eprint/83647

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