AI-Powered Simulation Models for Estimating the Consolidation Settlement of Shallow Foundations

The shallow foundations are one of the commonly used, cost-effective and versatile substructure in the infrastructure and geotechnical society. The consolidation settlement is one of the influential parameters for the design purpose of the shallow foundation. This study utilized the AI based models like Deep Neural Network (DNN), Random Forest (RF) and Gradient Boosting Machine (GBM) for the prediction of S _c . In order to forecast the S _c , different soil conditions such as void ratio, compression index, density and the load were considered as the input criteria and their respective settlement is the output. These adopted AI driven models, provide better results with higher precisions. The output produced by the adopted models were considered for different statistical assessments, specifically, DNN model outperforms the other models in terms of precision (R ²  = 0.9992) and less errors (RMSE = 0.6404). Moreover, the rank analysis, Taylor diagram and the reliability index were also computed for justifying the capability of the developed AI models.