Machine learning–based uncertainty modelling of mechanical properties of soft clays relating to time-dependent behavior and its application

Keyphrases

• Mechanical Properties 100%
• Artificial Neural Network 100%
• Model Uncertainty 100%
• Time-dependent Behavior 100%
• Soil Properties 100%
• Soft Clay 100%
• Monte Carlo Dropout 100%
• Confidence Interval 33%
• Numerical Modeling 33%
• Geotechnical Engineering 33%
• Modeling Approach 33%
• Hydraulic Conductivity 33%
• Excess Pore Water Pressure 33%
• Scale Problem 33%
• Machine Learning Techniques 33%
• Excellent Performance 33%
• Long-term Performance 33%
• Risk Assessment 33%
• Geotechnical Structures 33%
• Uncertainty Evaluation 33%
• Soil Physical Properties 33%
• Engineering Scale 33%
• Statistical Learning Methods 33%
• Embankment on Soft Clay 33%
• Conduct Risk 33%
• Creep Index 33%

Engineering

• Dependent Behavior 100%
• Artificial Neural Network 100%
• Predicted Result 66%
• Hydraulics 33%
• Model Uncertainty 33%
• Term Performance 33%
• Machine Learning Method 33%
• Good Agreement 33%
• Excess Pore Pressure 33%
• Engineering 33%
• Geotechnical Engineering 33%
• Scale Problem 33%
• Numerical Modeling 33%
• Creep Behavior 33%
• Confidence Interval 33%