Mechanisms of Differential Settlement in Widened Embankments over Soft Soil Considering Structural Degradation and Geometric Coupling: Physics-Constrained Intelligent Prediction
Hongxing Li1, Xizhong Xu2,*, Liang Wang1, Jiabo Hu2, Zhice Zhao1
1 Cangzhou Transportation Development (Group) Co., Ltd., Cangzhou, China
2 Shandong Transportation Research Institute, Jinan, China
* Corresponding Author: Xizhong Xu. Email: 
(This article belongs to the Special Issue: Sustainable and Durable Construction Materials)
Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2026.081450
Received 02 March 2026; Accepted 08 April 2026; Published online 23 April 2026
Abstract
Differential settlement control in highway widening projects on soft soil remains a major challenge. This study investigates the mechanisms of differential settlement in widened embankments and develops an intelligent prediction framework by integrating high-fidelity numerical simulations with physics-constrained deep learning. First, comprehensive numerical simulations were performed using a Hardening Soil (HS) model considering structural degradation in PLAXIS 2D. This work revealed the redistribution of additional stress under widening loads and elucidated the evolution mechanisms of plastic zone development and interface shear behavior at the junction of new and existing subgrades. A reasonable step width range of 1.5–2.0 m is identified based on deformation control, plastic zone extent, and engineering economy. To overcome the limited physical consistency of conventional deep learning, a physics-constrained WOA-Attention-BiGRU framework is developed by embedding monotonicity and logarithmic consolidation rate decay as regularization terms in the loss function, significantly improving robustness and interpretability in data-sparse regimes. Validation shows R
2 = 0.988 and RMSE ≤ 3.2 mm for full-time-series settlement prediction, substantially outperforming pure data-driven models; slight error increase occurs under extreme soft conditions but remains within engineering tolerances. The findings elucidate the nonlinear coupling mechanisms between geometric parameters and foundation stiffness governing differential settlement gradients, providing reliable support for optimized design and long-term settlement assessment in soft soil highway widening projects.
Keywords
Highway widening; differential settlement; structured soft soil; physics-constrained deep learning; Attention mechanism; geometric optimization
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