Subgrade collapse threatens coastal infrastructure under harsh environments, where deterioration accelerates deformation and failure risk. Accurate prediction is essential, yet traditional monitoring suffers from low informatization and delayed response. Thus, this paper presents a Micro-Electro-Mechanical Systems (MEMS)-based intelligent perception-driven method for subgrade collapse deformation prediction to improve the level of intelligence in subgrade collapse monitoring and prediction. Firstly, a hierarchical prediction framework is established based on subgrade deformation monitoring scenarios, consisting of an intelligent perception layer, a collapse deformation prediction layer, and a functional application layer, with the functions of each layer systematically defined. Secondly, two key technologies involved in the proposed framework, including MEMS data cleaning and time-series feature extraction, as well as the deformation prediction model, are identified and corresponding solutions are developed. Finally, a linear sliding rail experiment and a subgrade collapse model test are conducted to validate the feasibility and effectiveness of the proposed method. The results indicated that effective MEMS data cleaning was achieved through Leave-One-Out Encoding (LOOE) encoding, missing value imputation, and normalization. Accurate time-series feature representation was obtained by combining seismic parameter extraction with a sliding window strategy. The improved the improved Long Short-Term Memory–Back Propagation (LSTM-BP) model model achieved accurate prediction of collapse displacement, with an accuracy of 95.56%. The proposed MEMS-based intelligent perception method accurately captured the evolution trend and spatial heterogeneity of subgrade collapse deformation, and the results can be used to support and guide early warning of subgrade collapse, providing technical support for the safety and durability management of coastal and offshore infrastructure under harsh environmental conditions.
Wu et al. (Tue,) studied this question.