Predicting the Dry Density of Clay Soil Improved by Adding Glass Powder Using a Back Propagation Neural Network Model - Galal Senussi; Fathia Alnaas; Samiha Abdelrahman; Naima Mohammed; Heba Mansour; A’laa Khalid (AlQalam Journal of Medical and Applied Sciences, 2024)
Clay soil has undesirable engineering properties, which can compromise structural stability. This study aims to enhance the compaction properties of high-plasticity clay soil by adding glass powder using artificial intelligence (AI), specifically through the Back propagation Neural Network (BPNET), to accurately predict dry density. The model used influential factors, such as wet soil weight (Wnet), glass powder ratio (Wglass), and water content (ω %) as inputs, with dry density (γ) as the output. The model demonstrated high accuracy, achieving a Mean Squared Error (MSE) of 0.0000117 and a Mean Absolute Error (MAE) of 0.002849, reflecting its effectiveness in improving clay soil properties and supporting its stability.