Prediction of Ultimate Bearing Capacity of Aggregate Pier Reinforced Clay Using Machine Learning

Aggregate piers are extensively in use for increasing bearing pressure and diminish settlement under the footing. The ultimate bearing capacity of aggregate pier reinforced clay is majorly affected by soil strength (c_u), area replacement ratio (a_r) of piles, geometry, and slenderness ratio (λ) of piles. Various prediction models have been proposed to predict the ultimate bearing capacity of aggregate piers. However, existing models have shown a broad range of bias, variation, errors, and as such they are unsuitable for practical design. In this study, machine learning algorithms (linear and non-linear regression) and Artificial neural networks (ANNs) were performed using field loading test results to predict the ultimate bearing capacity of ground reinforced by aggregate piers. Sensitivity analysis was conducted to identify the influence of input variables. To fulfil this objective, 37 test results were used for training and testing of different models and compared with each other based on statistical parameters (mean absolute error, root mean squared error, and r²-score). Random Forest Regression model came out to be the best suitable for prediction of ultimate bearing capacity with minimum mean absolute error (MAE = 38.93 kPa) and r²-score equal to 0.98.