Prediction of Soft Soil Settlement Based on Ensemble Smoother with Multiple Data Assimilation

The selection of soil parameters relies heavily on the engineering experience of practitioners and frequently results in settlement predictions that deviate from field monitoring, especially for soft soil foundations. This study proposes a settlement prediction method for soft soil based on the ensemble smoother with multiple data assimilation (ES-MDA), which makes use of monitoring data by updating the preliminarily selected soil parameters for improved settlement predictions. This study investigates the applicability of the ES-MDA for embankment settlement prediction. A real project on the northern coast of Zhejiang, China, is used to illustrate the implementation and practical feasibility of the ES-MDA method. After adaptive parameter updates using the ES-MDA algorithm, the uncertainty of the soil parameters is significantly reduced. Through iterative integration of observational data, the predicted mean settlement gradually approaches the measured values, while the 95% prediction interval becomes narrower, validating the effectiveness of the method. The data assimilation-based settlement prediction method is beneficial for risk management and the long-term maintenance of the embankment.