The physical and mechanical properties of the loess differ from other kinds of soil due to its collapsibility, which has resulted in the complex displacement development law of the loess slope. Therefore, the accurate estimation of the displacement of high slopes in a loess gully region is critical for the safety of people and in construction activities. In the present study, to improve the accuracy of traditional methods, the original cumulative displacement curve was decomposed into trend and fluctuation terms using Empirical Mode Decomposition (EMD) and Wavelet Decomposition (WD). Subsequently, the results were estimated using the Support Vector Machine (SVR) and Long Short-Term Memory Network (LSTM) optimized by Biogeography-based Optimization (BBO), respectively. To select the most appropriate model, SVR, LSTM, EMD-SVR-LSTM, EMD-BBO-SVR-LSTM, and WD-BBO-SVR-LSTM were employed to predict the deformation of a loess slope in the Loess Plateau of China. According to the results, the displacement increases rapidly at the starting stage, and then gradually stabilizes, which is the same as the trend in reality. On comparing the predicted results with field data, it was found that the models with decomposition algorithms achieved higher accuracy. Particularly, the determination coefficient of the EMD-BBO-SVR-LSTM model reaches 0.928, which has better algorithm stability and prediction accuracy than other models. In this study, the decomposition algorithm was applied to the loess slope displacement innovatively, and the appropriate machine learning algorithm adopted for the displacement components. The method improves the accuracy of prediction and provides a new idea for instability warning of loess excavation slopes. The research has implications for urban construction and sustainable development in loess mountainous areas.

Argillization is a process in which clay‐bearing rocks disintegrate into the clay under the action of high temperature, pressure, and water. When tunnel boring machines (TBMs) excavate in the mudstone, argillization takes place, causing the clogging of the TBM cutterhead. As a result, the penetration rate drops gradually. Abnormal wear might occur. To investigate the evolution of argillization of mudstone and cutter wear during the TBM tunnelling, a series of rotary indentation tests were carried out on the self‐designed experimental bench for different loading times. During the test, the temperature and penetration depth of disc cutters were measured in real time. After loading, microstructures of cutting grooves, slacking mudstone, and worn cutter ring were observed by stereomicroscope. Consequently, the evolution of argillization in mudstone and cutter wear were investigated. Experimental results indicate that the argillization process of mudstone by disc cutter can be divided into three stages: mechanical cutting stage, deterioration of mudstone and the formation of slacking mudstone stage, and adherence of slacking mudstone stage. Specifically, at mechanical cutting stage, the rock was cut by cutter directly, causing high frictional heat. Then the microstructure of mudstone was deteriorated due to the water‐weakening mechanisms, temperature effect, and mechanical activation effect. Finally, the slacking mudstone was adhered to the disc cutter. Correspondingly, due to the argillization of mudstone, the disc cutter wear goes through the mechanical wear stage, argillization wear stage, and secondary wear stage in sequence. This investigation reveals the rock cutting mechanism of TBM considering the argillization of mudstone. Furthermore, it provides some references for design and operation of the TBM.