ArticleScientific reports2022
Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Identification of Selected Physical and Mechanical Properties of Cement Composites Modified with Granite Powder Using Neural Networks.Materials (Basel, Switzerland) · 2025Article
- Ensemble learning based compressive strength prediction of concrete structures through real-time non-destructive testing.Scientific reports · 2024Article
- Novel cementitious materials with mechanoluminescence for the application of visible stress monitoring and recording.Scientific reports · 2023Article
- Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In this study, a machine learning model for the precise manufacturing of green cementitious composites modified with granite powder sourced from quarry waste was designed. For this purpose, decision tree, random forest and AdaBoost ensemble models were used and compared. A database was created containing 216 sets of data based on an experimental study. The database consists of parameters such as the percentage of cement substituted with granite powder, time of testing and curing conditions. It was shown that this method for designing green cementitious composite mixes, in terms of predicting compressive strength using ensemble models and only three input parameters, can be more accurate and much more precise than the conventional approach. Moreover, to the best of the authors' knowledge, artificial intelligence has been one of the most effective and precise methods used in the design and manufacturing industry in recent decades. The simplicity of this method makes it more suitable for construction practice due to the ease of evaluating the input variables. As the push towards decreasing carbon emissions increases, a method for designing green cementitious composites without producing waste that is more precise than traditional tests performed in a laboratory is essential.
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Registered trials
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