Work

Embankment Settlement Prediction Considering Dynamic Changes in Settlement Process Under Scarce Physical Information

Year: 2026

Type: article

Abstract: Accurate prediction of embankment settlement and evaluation of its serviceability in permafrost regions are significantly challenged by scarce monitoring data and dynamic, non-stationary settlement processes. To address this, an integrated framework combining change-point detection with a novel dynamic prediction model is proposed. Analysis of long-term monitoring data from the Qinghai–Tibet Railway using the Pettitt test revealed a key change point around 2015, indicating a transition towards stabilization. Subsequently, an SAA-GRU-LSTM hybrid model, employing a dynamic compensation prediction strategy, was developed. The model successfully utilized only early-stage data to forecast future settlement trends, demonstrating robust performance in adapting to the identified abrupt change. Furthermore, by applying established engineering serviceability criteria to both historical and predicted data, the framework enables a dynamic and prospective serviceability assessment. This methodology provides a practical tool for the maintenance and risk management of infrastructure in permafrost environments under conditions of data scarcity and process uncertainty.

Source: Applied Sciences

DOI: 10.3390/app16073124

Authors: Meng Yuan, Xiaoyue Lin, Zhaojia Fang, Yuhe Ruan, Saize Zhang

Institutions: China Communications Construction Company (China), Changsha University of Science and Technology

Language: English

Topic: Geotechnical Engineering and Soil Stabilization

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896