The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R², RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R² values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings.

This investigation aims to examine the behavior of natural and manufactured pozzolans, as well as the properties of blended mortars. Eco-friendly building materials are gaining the attention of researchers worldwide, as they are made from supplementary cementitious materials that alter the fresh and mechanical properties of the blended mortar. Granulated blast furnace slag, silica fume, zeolite powder, metakaolin, clay powder, fly ash, rice husk ash, copper slag, and glass powder have all been used to improve the microstructure, workability, setting time, and strength. The control mixes contain a range of replacement rates (5–50 %) as supplementary cementitious materials compared to the OPC reference mortar. Brick clay powder (5–15 %), Granulated Blast Furnace Slag (5–20 %), Silica Fume (5–25 %), Copper Slag (5–35 %), Metakaolin (5–25 %), Fly Ash (5–30 %), Rice Husk Ash (5–20 %), Zeolite Powder (5–20 %), Ceramic Waste (5–35 %) and Glass Powder (5–10 %) were identified as the beneficial proportion. Moreover, strength increases simultaneously with the volume/index of the big content of all supplementary cementitious materials blends, but beyond the critical level of supplementary cementitious materials mixes, it tends to reduce strength. The changes made by integrating supplementary cementitious materials are significant in terms of both the fresh and hardened properties of blended mortar.