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

doi:10.54361/ajmas.247462

Predicting the Dry Density of Clay Soil Improved by Adding Glass Powder Using a Back Propagation Neural Network Model

Authors

Galal Senussi Department of Mechanical Engineering, Faculty Engineering, Omar Al Mukhtar University, Al-Baida, Libya
Fathia Alnaas Department of Civil Engineering, Faculty Engineering, Omar Al Mukhtar University, Al-Baida, Libya https://orcid.org/0009-0003-4072-3385
Samiha Abdelrahman Department of Civil Engineering, Faculty Engineering, Omar Al Mukhtar University, Al-Baida, Libya
Naima Mohammed Department of Civil Engineering, Faculty Engineering, Omar Al Mukhtar University, Al-Baida, Libya
Heba Mansour Department of Civil Engineering, Faculty Engineering, Omar Al Mukhtar University, Al-Baida, Libya
A'laa Khalid Department of Civil Engineering, Faculty Engineering, Omar Al Mukhtar University, Al-Baida, Libya https://orcid.org/0009-0009-6543-9122

DOI:

https://doi.org/10.54361/ajmas.247462

Abstract

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.

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Published

2024-11-26

How to Cite

Galal Senussi, Fathia Alnaas, Samiha Abdelrahman, Naima Mohammed, Heba Mansour, A’laa Khalid. Predicting the Dry Density of Clay Soil Improved by Adding Glass Powder Using a Back Propagation Neural Network Model . Alq J Med App Sci [Internet]. 2024 Nov. 26 [cited 2024 Nov. 27];:1344-9. Available from: https://journal.utripoli.edu.ly/index.php/Alqalam/article/view/698