A Scientometric Study of LCA-Based Industrialization and Commercialization of Geosynthetics in Infrastructures

Author:

Giglio Carlo1ORCID,Vocaturo Gianluca Salvatore1,Palmieri Roberto2

Affiliation:

1. Department of Civil, Energy, Environmental and Material Engineering, Mediterranean University of Reggio Calabria, 89122 Reggio Calabria, Italy

2. Department of Mechanical, Energy and Management Enginering, University of Calabria, 87036 Rende, Italy

Abstract

This study analyzes the scientific literature on Life Cycle Assessment-based (LCA-based) industrialization and commercialization of geosynthetics for infrastructures in the field of Industry 4.0, by applying a scientometric study. A set of articles published in Scopus was analyzed through both a quantitative and a qualitative approach. The results are reported in a framework where the main keywords, themes, and topics are identified and discussed. Such results include the analysis of emerging trends and convergence among different themes and topics. In fact, results from the current literature in this area are still evolving and reveal increasingly new trends and themes, opening up new and challenging research perspectives in terms of innovative applications. Moreover, this study identifies the main affiliations and countries contributing to this area, as well as the main collaboration networks among the most prominent authors and geographical areas, thus providing scholars, namely, early career ones, with an indication of the most relevant authors to connect with for their future studies.

Funder

Italian Ministry of University and Research, Italian National Operational Programme on Research and Innovation Attraction and International Mobility

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference76 articles.

1. A comparative life cycle assessment of innovative highway slope repair techniques;Leal;Transp. Geotech.,2020

2. Shear Strength Prediction for Fiber-Reinforced Soils by Data Mining Techniques and Their Ensembles;Chou;J. Chin. Inst. Civ. Hydraul. Eng.,2016

3. An extreme learning machine model for geosynthetic-reinforced sandy soil foundations;Raja;Geotech. Eng.,2022

4. An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil;Raja;Int. J. Pavement Eng.,2022

5. Application of Machine Learning Techniques for Predicting the Dynamic Response of Geogrid Reinforced Foundation Beds;Sharma;Geotech. Geol. Eng.,2019

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