• Ashish Sharma
• Dr. Rajeev Jain

This paper presents Soil behavior analysis is crucial in civil engineering for evaluating soil suitability in construction and road projects. Traditional soil testing methods are time-intensive and prone to errors due to manual processes. This research develops an Artificial Neural Network (ANN) model to predict soil behavior based on parameters such as Gravel Content, Fine Content, Liquid Limit, Plastic Limit, Maximum Dry Density, and Optimum Moisture Content. The ANN model, optimized for network structure and hyperparameters, predicts California Bearing Ratio (CBR) values with high accuracy, minimizing errors. Results demonstrate the model's reliability and efficiency, surpassing traditional regression methods. This ANN-based approach enables geotechnical engineers to make rapid, data-driven decisions, representing a significant advancement in soil analysis for construction and infrastructure planning.

Soil Behavior Analysis, Artificial Neural Network (ANN), California Bearing Ratio (CBR), Gravel Content, Fine Content, Liquid Limit, Plastic Limit, Maximum Dry Density (MDD), Optimum Moisture Content (OMC), Prediction Accuracy, Geotechnical Engineering.

" Artificial Neural Network-Based Prediction of California Bearing Ratio (CBR) Values for Soil Behavior Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.f471-f479, November-2024, Available :http://www.jetir.org/papers/JETIR2411553.pdf

" Artificial Neural Network-Based Prediction of California Bearing Ratio (CBR) Values for Soil Behavior Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppf471-f479, November-2024, Available at : http://www.jetir.org/papers/JETIR2411553.pdf