Diaphragm-Wall Settlement Prediction and Relative Anomaly Screening for Deep Excavations Using Multi-Model Comparison and Intelligent Optimization
Abstract
Deep excavations are high-risk geotechnical activities, and accurate prediction of diaphragm-wall settlement is important for construction monitoring and deformation control. This study investigates cumulative vertical settlement at 23 diaphragm-wall monitoring points from the deep excavation of Tianjin Goldin Finance 117 in Tianjin, China. Seven prediction models—a naïve persistence model, autoregressive integrated moving average (ARIMA), K-nearest neighbors (KNN), multilayer perceptron (MLP), gated recurrent unit (GRU), Transformer, and XGBoost—were evaluated using a unified five-fold rolling-origin expanding-window validation scheme. GRU achieved the best overall baseline performance, with a mean R2 of 0.9172, a mean absolute error (MAE) of 0.1009 mm, a root mean square error (RMSE) of 0.1341 mm, and a mean absolute percentage error (MAPE) of 0.5875%. GRU was subsequently optimized using the crow search algorithm (CSA), the genetic algorithm (GA), and the whale optimization algorithm (WOA). GRU-WOA achieved the best numerical performance, with a mean R2 of 0.9289 and an RMSE of 0.1215 mm. Relative anomaly levels were further identified from predicted settlement-change rates to characterize temporal concentration and spatial clustering of settlement-change activity. The proposed framework can support priority inspection and targeted monitoring, although the resulting anomaly levels represent project-relative statistical deviations rather than code-based engineering risk classes.