An Integrated Machine Learning Framework for Predictive Assessment of Soil Bearing Capacity and Settlement Using Multisource Geotechnical and Environmental Data
Abstract Reliable foundation design requires simultaneous control of ultimate bearing capacity and serviceability settlement, yet conventional prediction methods commonly treat soil parameters as static and omit rainfall, moisture, matric suction and groundwater fluctuations. This article develops a machine-learning-dr...