ArticleMaterials (Basel, Switzerland)2022
Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Prediction and parametric modeling of compressive strength of waste marble dust concrete through machine learning and experimental analysis.Scientific reports · 2025Article
- Evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning.Scientific reports · 2025Article
- An AI-driven approach for modeling the compressive strength of sustainable concrete incorporating waste marble as an industrial by-product.Scientific reports · 2024Article
- Revolutionizing applications: the impact of controlled surface chemistry on marble powder.RSC advances · 2024Review
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- Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar.PloS one · 2023Article
- In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches.Materials (Basel, Switzerland) · 2022Article
- Data-Driven Techniques for Evaluating the Mechanical Strength and Raw Material Effects of Steel Fiber-Reinforced Concrete.Materials (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors.
Funding
Abstract
Recently, the high demand for marble stones has progressed in the construction industry, ultimately resulting in waste marble production. Thus, environmental degradation is unavoidable because of waste generated from quarry drilling, cutting, and blasting methods. Marble waste is produced in an enormous amount in the form of odd blocks and unwanted rock fragments. Absence of a systematic way to dispose of these marble waste massive mounds results in environmental pollution and landfills. To reduce this risk, an effort has been made for the incorporation of waste marble powder into concrete for sustainable construction. Different proportions of marble powder are considered as a partial substitute in concrete. A total of 40 mixes are prepared. The effectiveness of marble in concrete is assessed by comparing the compressive strength with the plain mix. Supervised machine learning algorithms, bagging (Bg), random forest (RF), AdaBoost (AdB), and decision tree (DT) are used in this study to forecast the compressive strength of waste marble powder concrete. The models' performance is evaluated using correlation coefficient (R
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