Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms

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 (R2), root mean square error, and mean absolute error and mean square error. The achieved performance is then validated by using the k-fold cross-validation technique. The RF model, having an R2 value of 0.97, has more accurate prediction results than Bg, AdB, and DT models. The higher R2 values and lesser error (RMSE, MAE, and MSE) values are the indicators for better performance of RF model among all individual and ensemble models. The implementation of machine learning techniques for predicting the mechanical properties of concrete would be a practical addition to the civil engineering domain by saving effort, resources, and time.

Exploring sustainable construction through experimental analysis and AI predictive modelling of ceramic waste powder concrete

“…These tests also show how much better the random forest model performs than the decision tree, bagging, and AdaBoost models. [10] 3 Objective…”

Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures

“…This study employs the k-fold approach to validate the implemented algorithm. In the literature (Amin et al, 2022a;Khan et al, 2022a;2022b;Zou et al, 2022), the statistical analysis is reported to assess the model's performance. Usually, data splitting into ten subgroups is carried out for the random dispersion to perform the k-fold process for cross-validation; this approach is repeated ten times to achieve outcomes in a satisfactory range, as shown in Figure 17.…”

Flexural behavior of reinforced concrete beams using waste marble powder towards application of sustainable concrete

“…Therefore, studies on wastes obtained from industrial raw materials in the construction sector continue (Adesina et al, 2022;Ahmad et al, 2022;Karalar et al, 2022a;Aksoylu et al, 2022;Alani et al, 2022;Arbili et al, 2022;Karalar et al, 2022b;Martínez-García et al, 2022;Qaidi et al, 2022;Zeybek et al, 2022;Çelik et al, 2022). One of them is the marble wastes that occur significantly after manufacturing in marble processing factories (Basaran et al, 2022;El-Mandouh et al, 2022;Khan et al, 2022;Mangi et al, 2022;Nayak et al, 2022;Oza et al, 2022). During the extraction and processing of the marble blocks, two types of waste are generated, namely in the form of pieces and dust, and these wastes cause various adverse effects on the environment.…”

“…For example, in (Mangi et al, 2022), the authors of the research found that the use of marble dust reduces the setting time of the geopolymer composite and increases its strength characteristics. In (Khan et al, 2022), according to the results of experiments, it was found that the use of various types of stone flour, including marble dust, accelerates the geopolymer reaction and increases the compressive strength by more than 10%.…”