Evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning.

Waste marble, an industrial byproduct generated from marble cutting and polishing processes, can be effectively utilized as a partial replacement in concrete mixtures. Incorporating waste marble in concrete not only addresses environmental concerns related to marble waste disposal but also contributes to the sustainability of construction materials. Using machine learning (ML) to predict the impact of waste marble on the compressive strength of traditional concrete offers several advantages over repeated laboratory experiments. ML offers a powerful alternative to costly and time-consuming laboratory experiments, enabling faster and more sustainable exploration of the potential of waste marble in improving concrete's compressive strength. This research has focused on evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning (ML). Advanced ML techniques such as the Group Methods Data Handling Neural Network (GMDH-NN), Support Vector Regression (SVR), K-Nearest Neighbors (kNN) and Adaptive Boosting (AdaBoost) have been applied in this research work. The GMDH-NN model was created using GMDH Shell 3.0 software, while AdaBoost, SVR and kNN models were created using "Orange Data Mining" software version 3.36. Error indices such as the sum of squared error (SSE), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and Error (%), and performance metrics such as Accuracy % and the R