Machine Learning Approach to Predict Compressive Strength of Green Sustainable Concrete

Sustainable construction contributed to the usage of recycled and waste materials to substitute conventional concrete. This research focuses on prediction of compressive strength of cement concrete substituted by large amounts of waste materials and products with strong mechanical properties and sustainability. It also emphasizes on using analytical model for the prediction of compression strength of the green concrete, so that there is a reduction in the cost of construction, conserve energy, and it will lead to a reduction of CO2 production from cement industries within reliable limits. In this paper, machine learning approach has been used to predict the compressive strength of green and sustainable concrete. Machine learning empowers machines to learn from their experiences and data provided. The system analyses the datasets and finds different patterns formed in the given data. Then, based on its learnings the machine can make certain predictions. In civil engineering application, a special computing technique called the artificial neural network (ANN) is in huge demand. ANN is a soft computing technique that learns from previous situations and adapts without constraints to a new environment. In this work, a neural network model for prediction of compressive strength of concrete has been illustrated. Different sets of data based upon several concrete design mixes were taken and were fed to the model. The model is then trained for prediction, which are being influenced by several input attributes and were jotted down a linear regression analysis.