Machine Learning Models to Evaluate the Load-Settlement Behavior of Piles from Cone Penetration Test Data

The evaluation of the load-settlement behavior of piles is crucial in meeting the strength and serviceability criteria for pile analysis and design. The most reliable approach for estimating this behavior is by conducting pile load tests. However, due to the considerable expense and time requirements of these tests, the load-transfer methods were used routinely in practice. The objective of this study is to explore the potential application of several machine learning (ML) algorithms to evaluate the load-settlement behavior of axially loaded single square precast prestressed concrete from cone penetration test (CPT) data. Several ML models such as artificial neural network (ANN), random forest (RF), and gradient boosted tree (GBT), were developed to estimate the load-settlement behavior from CPT data (corrected cone tip resistance, qt, and sleeve friction, fs). A database of load-settlement curves of 64 static pile load tests and corresponding CPT data were compiled and used for the development of these ML models. The developed ANN, RF, and GBT models are evaluated based on several statistical criteria. The load-settlement curves predicted using the developed ML models were compared with the measured curves from pile load tests and the load-settlement curves predicted using the conventional load-transfer methods. The results of this study demonstrated the great potential of using ML models to predict the load-settlement behavior of axially loaded piles from CPT data. The comparison clearly shows that ML models outperformed the load-transfer methods. The results showed that both the GBT and ANN algorithms demonstrated to be the best-performing ML models.