Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques

Special Issue: "Recent Developments in Geopolymers and Alkali-Activated Materials"

As efforts toward global sustainability converge with the imperative to reduce the environmental impact of construction materials, extensive research and development is underway in the field of geopolymers and alkali-activated materials (AAMs) [...].

Knowledge Mapping of the Literature on Fiber-Reinforced Geopolymers: A Scientometric Review

This study examined the bibliographic data on fiber-reinforced geopolymers (FRGPs) using scientometrics to determine their important features. Manual review articles are inadequate in their capability to connect various segments of literature in an ordered and systematic manner. Scientific mapping, co-citation, and co-occurrence are the difficult aspects of current research. The Scopus database was utilized to find and obtain the data needed to achieve the study's aims. The VOSviewer application was employed to assess the literature records from 751 publications, including citation, bibliographic, keyword, and abstract details. Significant publishing outlets, keywords, prolific researchers in terms of citations and articles published, top-cited documents, and locations actively participating in FRGP investigations were identified during the data review. The possible uses of FRGP were also highlighted. The scientometric analysis revealed that the most frequently used keywords in FRGP research are inorganic polymers, geopolymers, reinforcement, geopolymer, and compressive strength. Additionally, 27 authors have published more than 10 articles on FRGP, and 29 articles have received more than 100 citations up to June 2022. Due to the graphical illustration and quantitative contribution of scholars and countries, this study can support scholars in building joint ventures and communicating innovative ideas and practices.

A State-of-the-Art Review on the Incorporation of Recycled Concrete Aggregates in Geopolymer Concrete

Geopolymer concrete (GC) has the potential to incorporate recycled concrete aggregates (RCA) obtained from construction and demolition waste. This research aims to review the current state-of-the-art knowledge of the RCA in GC and identify the existing knowledge gaps for future research direction. This paper highlights the essential factors that impact the GC’s mechanical and durability properties. Moreover, the influence of various percentages of coarse and fine RCA and the pattern of their replacement will be assessed. The effect of aluminosilicate material, alkaline activators, and curing regime also will be evaluated. Besides, the durability-related characteristics of this concrete will be analysed. The impact of exposure to a higher temperature, freeze–thaw cycles, marine environment, and acid and alkali attack will be comprehensively reviewed. A literature review revealed that increasing alumina silicate content, such as slag and metakaolin, and increasing the Na2SiO3/NaOH ratio and alkali-activator-to-binder ratio improve the hardened GC. However, increasing slag and metakaolin content and the Na2SiO3/NaOH ratio has an adverse impact on its workability. Therefore, finding the optimum mix design for using RCA in GC is vital. Moreover, there is a scope for developing a self-compacting GC cured at ambient temperature using RCA.

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

Research on the applications of new techniques such as machine learning is advancing rapidly. Machine learning methods are being employed to predict the characteristics of various kinds of concrete such as conventional concrete, recycled aggregate concrete, geopolymer concrete, fiber-reinforced concrete, etc. In this study, a scientometric-based review on machine learning applications for concrete was performed in order to evaluate the crucial characteristics of the literature. Typical review studies are limited in their capacity to link divergent portions of the literature systematically and precisely. Knowledge mapping, co-citation, and co-occurrence are among the most challenging aspects of innovative studies. The Scopus database was chosen for searching for and retrieving the data required to achieve the study’s aims. During the data analysis, the relevant sources of publications, relevant keywords, productive writers based on publications and citations, top articles based on citations received, and regions actively engaged in research into machine learning applications for concrete were identified. The citation, bibliographic, abstract, keyword, funding, and other data from 1367 relevant documents were retrieved and analyzed using the VOSviewer software tool. The application of machine learning in the construction sector will be advantageous in terms of economy, time-saving, and reduced requirement for effort. This study can aid researchers in building joint endeavors and exchanging innovative ideas and methods, due to the statistical and graphical portrayal of participating authors and countries.