Nanotechnology in Concrete: a Bibliometric Review

Costa, Vítor Corrêa; Souza, Fernando Gomes de; Thomas, Sabu; Filho, Romildo Dias Tolêdo; Sousa, Luana De Castro; Filho, Sérgio Thode; Carvalho, Fernanda Veloso De; Maranhão, Fabíola da Silveira; Aboelkheir, Mostafa Galal; Lima, Nathali Ricardo Barbosa de; Pereira, Emiliane Daher; Hasparyk, Nicole Pagan

doi:10.55747/bjedis.v1i1.48410

Cited by 2 publications

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“…This is key to discovering novel properties of nanomaterials, predicting material behaviors, and guiding the creation of new nanocomposites with desired characteristics. Data mining also helps map the research landscape in nanomaterials, pinpointing knowledge gaps and suggesting potential areas for further research [ 216 , 217 , 300 , 301 , 302 ].…”

Section: Introductionmentioning

confidence: 99%

“…The research landscape is refined using network graph methodologies to identify key research nodes and their interconnections [ 31 , 120 , 140 , 215 , 217 , 218 , 301 , 302 , 441 , 442 , 443 , 444 , 445 , 446 , 447 , 448 , 449 , 450 , 451 ], spotlighting pivotal works and active research clusters, especially in emerging research hubs like the United States, China, and India.…”

Section: Introductionmentioning

confidence: 99%

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A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

Gomes Souza,

Bhansali,

Pal

et al. 2024

Materials

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From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the “State-of-the-Art and Gaps Extracted from Results and Discussions” section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the “Literature Review Guided by Artificial Intelligence” section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

Gomes Souza,

Bhansali,

Pal

et al. 2024

Materials

Self Cite

View full text Add to dashboard Cite

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Using synthetic data to develop machine learning models to predict the performance of fiber- reinforced concrete

Silva,

Pessoa,

Sousa

et al. 2024

Preprint

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Concrete is a widely used construction material due to its high compressive strength. However, its durability is often compromised by the development of cracks caused by tensile stress within structures. These cracks can occur during the drying process, leading to water infiltration and corrosion of the concrete reinforcement, which subsequently require repair. As a result, innovative technologies, such as self-repairing concrete and crack control, have become crucial in reducing the costs associated with structural repairs. Given this context, this study investigated novel crack control technologies in concrete structures using a machine learning model that can accurately predict the performance of a specific fiber in fiber-reinforced concrete using a comprehensive dataset. The dataset was compiled from 18 studies and further augmented using synthetic data generation techniques. It encompassed 13 different fiber types and a total of 1953 fiber-reinforced concrete formulations. The computational model was then implemented in Python, and multiple linear regression (MLR), support vector regression (SVR), Random Forest and GradientBootsting techniques were employed to develop the prediction model. The results showed that Random Forest (R² = 0.887 and RMSE = 0.110), GradientBoosting (R² = 0.868 and RMSE = 0.368) and SVR models (R² = 0.856 and RMSE = 0.376) outperformed its MLR counterpart (R² = 0.587 and RMSE = 0.637). Moreover, Random Forest shows a lower RMSE, making it more suitable to accurately predict the performance of the performance of fiber-reinforced concrete.

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Nanotechnology in Concrete: a Bibliometric Review

Cited by 2 publications

References 0 publications

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

Using synthetic data to develop machine learning models to predict the performance of fiber- reinforced concrete

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