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The mechanical properties of concrete are the important parameters in a design code. The amount of laboratory trial batches and experiments required to produce useful design data can be decreased by using robust prediction models for the mechanical properties of concrete, which can save time and money. Portland cement is frequently substituted with metakaolin (MK) because of its technical and environmental advantages. In this study, three mechanical properties of concrete with MK, i.e., compressive strength (f′c), splitting tensile strength (fst), and flexural strength (FS) were modelled by using four machine learning (ML) techniques: gene expression programming (GEP), artificial neural network (ANN), M5P model tree algorithm, and random forest (RF). For this purpose, a comprehensive database containing detail of concrete mixture proportions and values of f′c, fst, and FS at different ages was gathered from peer-reviewed published documents. Various statistical metrics were used to compare the predictive and generalization capability of the ML techniques. The comparative study of ML techniques revealed that RF has better predictive and generalization capability as compared with GEP, ANN, and M5P model tree algorithm. Moreover, the sensitivity and parametric analysis (PA) was carried out. The PA showed that the most suitable proportions of MK as partial cement replacement were 10% for FS and 15% for both f′c and fst.

Biodegradable materials are appropriate for the environment and are gaining immense attention worldwide. The mechanical properties (such as elongation at break, density, and failure strain) of some natural fibers (such as Coir, Hemp, Jute, Ramie, and Sisal) are comparable with those of some synthetic fibers (such as E glass, aramid, or Kevlar). However, the toughness of coconut fibers is comparatively more than other natural fibers. Numerous studies suggest coconut fibers perform better to improve the concrete mechanical properties. However, the knowledge is dispersed, making it difficult for anyone to evaluate the compatibility of coconut fibers in concrete. This study aims to perform a scientometric review of coconut fiber applications in cementitious concrete to discover the various aspects of the literature. The typical conventional review studies are somehow limited in terms of their capacity for linking different literature elements entirely and precisely. Science mapping, co-occurrence, and co-citation are among a few primary challenging points in research at advanced levels. The highly innovative authors/researchers famous for citations, the sources having the highest number of articles, domains that are actively involved, and co-occurrences of keywords in the research on coconut-fiber-reinforced cementitious concrete are explored during the analysis. The bibliometric database with 235 published research studies, which are taken from the Scopus dataset, are analyzed using the VOSviewer application. This research will assist researchers in the development of joint ventures in addition to sharing novel approaches and ideas with the help of a statistical and graphical description of researchers and countries/regions that are contributing. In addition, the applicability of coconut fiber in concrete is explored for mechanical properties considering the literature, and this will benefit new researchers for its use in concrete.