Interpretation of the Pile Static Load Test Using Artificial Neural Networks

This study presents a novel approach for interpreting static load tests (SLT) of piles using Artificial Neural Networks (ANNs) integrated with the Meyer and Kowalow load-settlement mathematical model. Reliable estimation of pile bearing capacity and settlement behavior is critical for safe and economical geotechnical design, particularly given the nonlinear and heterogeneous nature of soils. Traditional SLT interpretation methods, such as Chin-Kondner, Decourt, and hyperbolic fitting approaches, provide useful extrapolation of the ultimate capacity but are sensitive to test termination levels and parameter estimation uncertainties. The Meyer and Kowalow function offers a robust mathematical representation of the load-settlement curve, allowing decomposition of the total pile resistance into the shaft and base components. In this work, ANN models were trained to solve both the direct and inverse forms of the Meyer and Kowalow problem, enabling rapid identification of constitutive parameters (initial stiffness, nonlinearity coefficient, and ultimate capacity) from measured SLT data. Numerical experiments demonstrated that networks with a single hidden layer achieved accurate predictions with low RMSE for both training and test sets. The proposed ANN-based framework facilitates improved parameter identification, supports partial-load SLT interpretation, and provides a practical tool for engineers seeking the reliable prediction of pile performance under service loads.