Assessing and Predicting Geogrid Reduction Factors after Damage Induced by Dropping Recycled Aggregates

Mateus P. Fleury (), Gustavo K. Kamakura, Cira S. Pitombo, André Luiz B. N. Cunha, Fernanda B. Ferreira and Jefferson Lins da Silva
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Mateus P. Fleury: Department of Geotechnical Engineering (SGS), São Carlos School of Engineering (EESC), University of São Paulo (USP), São Carlos 13566-590, SP, Brazil
Gustavo K. Kamakura: Department of Geotechnical Engineering (SGS), São Carlos School of Engineering (EESC), University of São Paulo (USP), São Carlos 13566-590, SP, Brazil
Cira S. Pitombo: Department of Transportation Engineering (STT), São Carlos School of Engineering (EESC), University of São Paulo (USP), São Carlos 13566-590, SP, Brazil
André Luiz B. N. Cunha: Department of Transportation Engineering (STT), São Carlos School of Engineering (EESC), University of São Paulo (USP), São Carlos 13566-590, SP, Brazil
Fernanda B. Ferreira: CONSTRUCT, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal
Jefferson Lins da Silva: Department of Geotechnical Engineering (SGS), São Carlos School of Engineering (EESC), University of São Paulo (USP), São Carlos 13566-590, SP, Brazil

Sustainability, 2023, vol. 15, issue 13, 1-23

Abstract: To fulfill the modern concept of sustainable construction, the civil engineering community has shown increased interest in alternative options to replace natural backfills for engineering purposes. Since Recycled Construction and Demolition Waste (RCDW) has proven to be attractive in environmental, economic, and technical aspects, its behavior should be assessed considering its interaction with other construction materials, such as geosynthetics. Bearing in mind that the backfill affects the durability of geosynthetic materials, this study aims to assess the damage caused to geogrids by RCDW dropped by transportation (dump) trucks. Moreover, this study aimed to obtain an equation to predict the reduction factor caused by the backfill drop process. In an experimental facility, six RCDW materials (with different grain size distributions) were dropped (using a backhoe loader) from 1.0 m and 2.0 m heights over three distinct geogrids; the geogrid samples were exhumed and then tested under tensile loading. The results provided a database subjected to machine learning (Artificial Neural Network—ANN) to predict the reduction factor caused by the induced damage. The results demonstrate that the increase in drop height or potential energy cannot be directly associated with the damage. However, the damage increases as the maximum grain size of uniform gradation backfill increases, which is different from the results obtained from the fall of continuous gradation backfill. Moreover, since ANNs do not have any of the traditional constraints that multiple linear regression has, this method is an attractive solution to predict the geosynthetic reduction factors, providing relative errors lower than 8% compared to the experimental investigation reported in the study.

Keywords: sustainable development; recycled aggregates; geosynthetics; artificial neural networks; durability; grain-size distribution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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