Soft computing techniques for analysing the mechanical properties of egg shell powder-based concrete

Abstract

The construction industry is increasingly focused on sustainability to reduce environmental impact. Researchers are actively exploring alternative materials to replace clinker-based binders. This study specifically investigates the use of eggshell powder (ESP) as a sustainable substitute in construction. Portland slag cement (PSC) is partially replaced by ESP in concrete production for this purpose. To assess the effectiveness of ESP in enhancing binder properties, the study analyses experimental data for compressive and flexural strength. Artificial Neural Network (ANN) modelling is employed for this analysis to predict material performance. The model undergoes training and testing using input data to ensure accuracy and reliability. The success of the study is demonstrated by high R2 values, with 0,9915 for compressive strength and 0,9921 for flexural strength, indicating that the ANN model closely matches actual material performance. Additionally, error analysis confirms the model's remarkable accuracy in predicting real-world results. Furthermore, the research highlights the exceptional potential of the developed ANN model, which can effectively predict the mechanical properties of construction materials containing ESP.

Keywords

• clinker-based
• esp
• psc
• ann
• compressive strength
• flexural strength