Forecasting sidewall displacement of underground caverns using machine learning techniques

Sidewall displacement is a vital task in underground caverns that can jeopardize the construction of these structures at risk and predict this event to provide the appropriate and timely solutions to deal with it can be very important. This article aims to predict sidewall displacement of the underground caverns using six machine learning approaches of long short-term memory, deep neural networks, K-nearest neighbors, Gaussian process regression, support vector regression, and decision tree. To this end, 310 datasets, including 12 useful parameters on the caverns' sidewall displacement, were gathered from 10 underground cavern projects in Iran and other countries. 74% of the datasets were applied to train the prediction models, and 26% for testing. Finally, through a comparison made between the prediction results and the measured ones, the most accurate predictions were produced by the LSTM model with correlation coefficient = 0.9998, root mean squared error = 0.002059 m, and variance account for = 99.98181%.

Automated summary

This article predicts sidewall displacement of underground caverns using six machine learning approaches and finds that the LSTM model provides the most accurate predictions based on a comparison of prediction results with measured data from 10 underground cavern projects.