Mapping horizontal displacement of soil nail walls using machine learning approaches

Machine learning (ML) approaches have stormed nearly all engineering fields since recent years. However, the situation is somehow subtle in civil engineering practice, especially in the sub-field of geotechnical engineering where data from real-life projects are usually scarce, which in turn prevents development of meaningful mapping tools based on ML techniques. This study first shares a database containing a total of 376 measured horizontal displacements from instrumented soil nail walls reported in the literature. Then, these data are utilized to develop three types of ML models for mapping the wall horizontal displacement along depth, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM). The uncertainties of the ANN, RF, and SVM models are then quantitatively evaluated using bias statistics where bias is defined as the ratio of measured to predicted horizontal displacement. The three ML models are proved to be accurate on average with medium dispersions in prediction, which outperform the existing simple empirical regression models. Probability distribution functions for those biases are also characterized. This study demonstrates that introduction of machine learning approaches into the reliability-based design framework for soil nail walls and other geotechnical structures is promising.