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Base resistance of super-large and long piles in soft soil: performance of artificial neural network model and field implications

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Prediction performance of the PSO–SVR model using the testing dataset

The distribution curve of axial force along pile. (a) TP1. (b) TP2. (c)

Soil boreholes and non-working piles' locations at silos site layout.

Frontiers Application of Artificial Neural Networks for Predicting the Stability of Rectangular Tunnels in Hoek–Brown Rock Masses

Applied Sciences, Free Full-Text

Soft computing for determining base resistance of super-long piles in soft soil: A coupled SPBO-XGBoost approach - ScienceDirect

Simulations of a field test by slip and non-slip t-z models.

Statistical analysis of the dataset a Histogram of the dataset; b

Sustainability, Free Full-Text

Machine learning-based soil–structure interaction analysis of laterally loaded piles through physics-informed neural networks

Application of artificial neural networks for predicting the bearing capacity of the tip of a pile embedded in a rock mass - ScienceDirect

JMSE, Free Full-Text