Multiple Fractal Analysis and Prediction of the Settlement of the Upper Existing Highway Pavement Induced by Shallow-Buried Tunnel Construction

In recent years, it has become inevitable to dig underneath existing highways when excavating tunnels. The soil settlement induced by ground excavation may adversely affect existing highways. In this study, a settlement monitoring system is used to obtain the settlement sequence of multiple measurement points on the pavement. Multifractal detrended fluctuation analysis (MF-DFA) is used to focus on analyzing the multiple fractal features of the pavement settlement rate. The results show that the settlement rates of the highway caused by the tunnel excavation and construction process all show multiple fractal characteristics. The fluctuations in the measurement points above and near the entrance of the tunnel are more complex and intense. Based on the moving-average method (MA), convolutional neural network (CNN), and Extreme Learning Machine (ELM), MA-CNN and MA-ELM prediction models are constructed to predict the settlement value sequences of the fluctuating points. The results indicate that the MA-ELM prediction model demonstrates superior predictive performance (with R² values of 0.956, 0.950, and 0.979 on the test set). Further, with the help of the Dung Beetle Optimizer (DBO), a meta-heuristic algorithm for parameter optimization, the hybrid model DBO-MA-ELM greatly improves the prediction performance (R² of 0.975, 0.997, 0.998 for the testing set).