Artificial Neural Network-Based Strength Prediction Model Using Sustainable Material In Concrete

The construction industry plays an important role in the emission of CO2 and resource depletion, due to the extensive use of concrete. Conventional concrete uses a lot of cement and natural aggregates, which negatively influence the environment through energy use, greenhouse gas emissions, and ecological degradation during production and extraction. These challenges have increased the demand for sustainable materials in concrete to reduce the environmental impact of the construction sector. Sustainable materials improve the performance and lifespan of concrete while lowering dependency on natural resources. Coconut Shell Ash (CSA) is a Supplementary Cementitious Material (SCM) in concrete instead of regular Portland cement. This study is to examine the effects of coconut shell ash in different percentages of addition in concrete. Glass Powder Waste (GPW) is used in concrete as a fine aggregate of 20 % replacement. Fineness modules of Glass waste and sand are examined. The study focuses on the workability, mechanical behaviour of concrete, durability, and microstructure analysis are investigated. Artificial Neural Network (ANN) model and Support Vector Regression(SVR) are used for predicting the compressive strength of the concrete and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value.