Integrating artificial intelligence and green concrete in the construction industry is a challenge that can help to move towards sustainable construction. Therefore, this research aims to predict the compressive strength of green concrete that includes a ratio of cement kiln dust (CKD) and fly ash (FA), then recommend the optimum sustainable mixture design. The artificial neural network (ANN) and multiple linear regression techniques are used to build prediction models and statistics using MATLAB and IBM SPSS software. The input parameters are based on 156 data points of concrete components and compressive strengths that are collected from the literature. The developed models have been trained, validated, and tested for each technique. TOPSIS method is used to assign the optimum mixture design according to three sustainable criteria: compressive strength, carbon dioxide (CO2) emission, and cost. The results of ANN models showed a better prediction of the compressive strength with regression (R) equal to 0.928 and 0.986. The optimum mixture includes CKD 10–20% and FA 0–30%. Predicting the compressive strength of green concrete is a non-destructive approach that has sustainable returns including preservation of natural resources, reduction of greenhouse gas emissions, cost, time, and waste to landfill as well as saving energy.

Integrating artificial intelligence in construction industry is a challenge that can help to move towards sustainable construction. Therefore, Artificial Neural Network (ANN), which is a computing system that simulates the human brain processes, can be helpful tool for prediction of the compressive strength of green concrete. Green concrete can be made using waste materials as a replacement portion of cement (supplementary cementitious materials) or aggregate that can benefit in the reduction of negative impacts on the environment and improve its compressive strength. This research aims to predict the compressive strength of green concrete that includes a ratio of cement kiln dust (CKD) and fly ash (FA), as industrial by-products, using artificial neural network technique and MATLAB software. The developed ANN model is based on the collected necessary information about the concrete components and compressive strengths from literature. Two models have been trained and tested. The first includes CKD in the concrete mix using 35 training samples with 3 hidden layers. While the second includes CKD and FA in the concrete mix using 42 training samples with 7 hidden layers. The results of both models showed a good prediction of the compressive strength of green concrete with error less than 10%. The benefits of this nondestructive approach may include preservation of natural resources, reduction of greenhouse gasses emissions, cost, time, and waste to landfill as well as saving energy.