Hierarchical Prediction of Subway-Induced Ground Settlement Based on Waveform Characteristics and Machine Learning with Applications to Building Safety

Abstract

Ground settlement caused by urban subway construction can significantly impact surrounding buildings and underground infrastructure, posing risks to structural safety and long-term performance. Accurate prediction of settlement trends is therefore essential for ensuring building integrity and supporting informed decision-making during construction. This study proposes a hierarchical prediction framework that incorporates waveform-based curve classification and machine learning to forecast ground settlement patterns. Monitoring data from the Urumqi Metro construction project are analyzed, and settlement curve types are identified using Fréchet distance, categorized into five distinct forms: inverse cotangent, exponential, multi-step, one-shaped, and oscillating. Each type is then matched with the most suitable predictive model, including the Autoregressive Integrated Moving Average (ARIMA), Attention Mechanism-enhanced Long Short-Term Memory (AM-LSTM), Genetic Algorithm-optimized Support Vector Regression (GA-SVR), and Particle Swarm Optimization-based Backpropagation neural network (PSO-BP). Results show that AM-LSTM achieves the best performance for inverse cotangent and large-sample exponential curves; ARIMA excels for small-sample exponential curves; PSO-BP is most effective for multi-step curves; and GA-SVR offers superior accuracy for one-shaped and oscillating curves. Validation on a newly excavated section of Urumqi Metro Line 2 confirms the model’s potential in enhancing the safety management of buildings and infrastructure in subway construction zones.