The most popular methods for soil classification from cone penetration test (CPT) data are based on examining two-dimensional charts. In the last years, several authors have dedicated efforts on replicating and discussing these methods using machine learning techniques. Nonetheless, most of them apply few techniques, include only one dataset and do not explore more than three input features. This work circumvents these issues by: (i) comparing five different machine learning techniques, which are also combined in an ensemble; (ii) using three distinct CPT datasets, one composed of 111 soundings from different countries, one composed of 38 soundings with information of soil age and the third composed of 64 soundings taken from the city of São Paulo, Brazil; and (iii) testing combinations of five input features. Results show that, in most cases, the ensemble of multiple models achieves better predictive performance than any technique isolated. Accuracies close to the maximum were obtained in some cases without the need of pore pressure information, which is costly to measure in geotechnical practice.

Geotechnical engineers frequently rely on semi-empirical methods like Décourt-Quaresma and Meyehof’s to estimate the bearing capacity of piles. This paper proposes alternatives to these methods, presenting an approach using machine learning models for predicting the bearing capacity of precast concrete piles. It uses data samples including 165 load tests, each one accompanied with a SPT sounding. This study proposes two types of analysis using two separated datasets, one based on the Décourt-Quaresma method and the other based on the Meyerhof method. Six machine learning algorithms of distinct biases are trained and tested with a leave-one-out cross validation procedure and the models’ predictive performance is assessed through two metrics: root mean squared error (RMSE) and coefficient of determination (R2). The best performing technique was random forest (RF) using Décourt-Quaresma dataset, with an RMSE of 642.38. All other machine learning techniques obtained a RMSE below 710, overcoming Meyerhof’s and Décourt-Quaresma’s semi-empirical methods, which both obtained RMSE values close to 900. This study proposes 95% and 90% confidence intervals for the best technique employing a graphical interpretation, so that geotechnical engineers can choose which level of safety they wish to work with. Finally, the study presents a case study showing that the best performing models achieve a reasonable accuracy, surpassing the semi-empirical methods in two of the three piles considered. The representativity of the new examples within the used datasets explain the accuracy of the techniques.

Helical piles are advantageous alternatives in constructions subjected to high tractions in their foundations, like transmission towers. Installation torque is a key parameter to define installation equipment and the final depth of the helical pile. This work applies machine learning (ML) techniques to predict helical pile installation torque based on information from 707 installation reports, including Standard Penetration Test (SPT) data. It uses this information to build three datasets to train and test eight machine-learning techniques. Decision tree (DT) was the worst technique for comparing performances, and cubist (CUB) was the best. Pile length was the most important variable, while soil type had little relevance for predictions. Predictions become more accurate for torque values greater than 8 kNm. Results show that CUB predictions are within $\left[0.71,1.59\right]$ times the real value with a 95% confidence. Thus, CUB successfully predicted the pile length using SPT data in a case study. One can conclude that the proposed methodology has the potential to aid in the helical pile design and the equipment specification for installation.