Using Machine Learning to Predict Axial Pile Capacity

Abstract

Accurate estimation of the ultimate axial load bearing capacity of piles is necessary to ensure the safety of the supported structures and to prevent cost overruns. Traditional mechanics-based design methods do not always predict pile capacity accurately, or precisely, leaving room for improvement. This study focuses on the potential of machine learning (ML) in estimating pile capacity. A dataset of 546 load tests was compiled from three databases. The baseline performance of traditional design methods was first established by comparing the capacities computed using four traditional approaches, against the capacities interpreted from load tests using Davisson’s criterion. Sixteen different ML techniques were explored. First, the optimal feature selection technique for model training was investigated. Second, hyperparameters of each technique were optimized. The process involved the training of 32,000 different models and tuning their hyperparameters. Next the dataset was randomly split into training (70%) and testing (30%) for comparing the 16 different ML regression models. Each of the optimized models was then trained using six feature sets. The performance of each of the 16 ML models with the best performing feature subset was compared with the baseline performance from traditional methods. Evaluation criteria included measured versus predicted capacities, influence of soil type on accuracy, as well as the absence of pile diameter, or length effects on accuracy and precision. In general, the ML methods performed significantly better than the best traditional method. The current research demonstrated that ML may offer advantages in geotechnical design when large datasets are available.