The integration of sustainable materials such as fly ash, blast-furnace slag, recycled aggregates, and seawater into concrete mixes offers significant potential for reducing the environmental impact of construction. However, traditional experimental methods for determining the compressive strength of these concrete mixes are time-consuming and resource-intensive. This study leverages Artificial Neural Networks (ANNs) and Machine Learning (ML) to develop a predictive model for the compressive strength of sustainable concrete, using a dataset of 768 concrete mix samples. Input variables include the concrete age as well as concrete composition, including cement, water, fine and coarse aggregates, seawater, fly ash, blast-furnace slag, and superplasticizer contents, while the output is the compressive strength. The developed model captures the non-linear relationships among these variables to predict compressive strength efficiently. The best ANN model achieved a test loss of 0.051, demonstrating its ability to accurately predict compressive strength and reduce reliance on traditional testing methods. Moreover, the model’s results were compared with those of alternative algorithms to ensure its validity. These findings highlight the potential of machine learning in advancing sustainable construction practices. A relevant future research direction is to analyze feature importance in machine learning models to identify key variables and guide more effective optimization and decision-making, in addition to extending their application to other material properties and advanced concrete mixes.

Concrete creep, defined as the deformation of concrete under sustained load, can cause cracks in tensile members, redistribution of stresses over time in composite structures, loss of prestressing force in prestressed concrete elements, and excessive long-term deflection of structural members. The concrete creep coefficient is an important input in many calculations and analyses of reinforced concrete structures. Currently, the concrete creep coefficient has been predicted by many models such as the Eurocode 2 model. This study aims to improve the prediction of the Eurocode 2 creep coefficient model at long-term by implementing a correction coefficient into the model. The Northwestern University database is used and the correction coefficient is calculated using Bayesian inference. The accuracy and efficient of the proposed improvement and modification are demonstrated through statistical indicators.