8 citations · 9 across the 3 of their papers we have counts for
3 papers
Predicting California Bearing Ratio with Ensemble and Neural Network Models: A Case Study from Turkiye
Abdullah Hulusi Kökçam, Uğur Dağdeviren, Talas Fikret Kurnaz +2
The California Bearing Ratio (CBR) is a key geotechnical indicator used to assess the load-bearing capacity of subgrade soils, especially in transportation infrastructure and found…
Soil Compaction Parameters Prediction Based on Automated Machine Learning Approach
Caner Erden, Alparslan Serhat Demir, Abdullah Hulusi Kokcam +2
Soil compaction is critical in construction engineering to ensure the stability of structures like road embankments and earth dams. Traditional methods for determining optimum mois…
Enhancing Machine Learning Model Performance with Hyper Parameter Optimization: A Comparative Study
Caner Erden, Halil Ibrahim Demir, Abdullah Hulusi Kökçam
One of the most critical issues in machine learning is the selection of appropriate hyper parameters for training models. Machine learning models may be able to reach the best trai…