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Abstract. Better understanding of the properties of cement-based materials, one of the most widely used building materials, at the nano-scale is crucial to improve its functionality in the built environment. This paper presents areas of using nano-materials in improving the characteristics of cement-based materials as well as introducing a new role of nanotechnology together with waste management in enhancing the concept of sustainable construction.A case study on the use of nano-granite waste particles as a replacement of cement and fine aggregate in mortar production is presented.The research concluded that replacing 5% cement and 10% sand with nano-granite waste in the mortar mix increased the compressive strength of the green mortar by 41% compared to that of the control mix (CM). SEM images reinforced this result as the green mortar mix showed maximum density and minimum micro cracks and number of pores.A comparative study between the green mortar and traditional mortar was carried out using sustainability indicators to examine the environmental, social and economic implications. The environmental and social attributes showed a saving of 10% in the field of resource consumption, whereas savings in energy consumption and CO2 emissions reached 5%. The economic field showed saving of 6.5% indicating promising results in enhancing the sustainable construction industry.

Purpose Utilization of sustainable materials is a global demand in the construction industry. Hence, this study aims to integrate waste management and artificial intelligence by developing an artificial neural network (ANN) model to predict the compressive strength of green concrete. The proposed model allows the use of recycled coarse aggregate (RCA), recycled fine aggregate (RFA) and fly ash (FA) as partial replacements of concrete constituents. Design/methodology/approach The model is constructed, trained and validated using python through a set of experimental data collected from the literature. The model’s architecture comprises an input layer containing seven neurons representing concrete constituents and two neurons as the output layer to represent the 7- and 28-days compressive strength. The model showed high performance through multiple metrics, including mean squared error (MSE) of 2.41 and 2.00 for training and testing data sets, respectively. Findings Results showed that cement replacement with 10% FA causes a slight reduction up to 9% in the compressive strength, especially at early ages. Moreover, a decrease of nearly 40% in the 28-days compressive strength was noticed when replacing fine aggregate with 25% RFA. Research limitations/implications The research is limited to normal compressive strength of green concrete with a range of 25 to 40 MPa. Practical implications The developed model is designed in a flexible and user-friendly manner to be able to contribute to the sustainable development of the construction industry by saving time, effort and cost consumed in the experimental testing of materials. Social implications Green concrete containing wastes can solve several environmental problems, such as waste disposal problems, depletion of natural resources and energy consumption. Originality/value This research proposes a machine learning prediction model using the Python programming language to estimate the compressive strength of a green concrete mix that includes construction and demolition waste and FA. The ANN model is used to create three guidance charts through a parametric study to obtain the compressive strength of green concrete using RCA, RFA and FA replacements.

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.