journal article·2026-04-28

A hybrid physics-informed and data-driven framework for unsaturated sandy dolomite: Advancing constitutive modeling with subloading surface theory and artificial neural networks

Meiqian Wang, Wenlian Liu, Wei Xu, Xiaoyu Zhang, Ting Xie

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Abstract

The mechanical characterization of unsaturated problematic geomaterials remains a significant challenge in geotechnical engineering. This study introduces a comprehensive hybrid framework for unsaturated severely sandy dolomite (SSD) by unifying an advanced physics-based constitutive model (CM) with a data-driven artificial neural network (ANN) predictive tool. The constitutive component innovatively integrates subloading surface (SLS) theory with the Barcelona Basic Model (BBM), establishing a modified Cam-clay framework that explicitly accounts for the coupled effects of matric suction, strain-softening, and overconsolidation. The model is rigorously calibrated and validated against an extensive suite of laboratory experiments, including consolidated undrained triaxial tests on SSD specimens from the Central Yunnan Water Diversion Project under varying confining pressures (100–400 kPa) and saturation states. The results demonstrate that the SLS formulation effectively overcomes the limitations of traditional single-yield-surface models, achieving exceptional predictive accuracy ( R 2 > 0.92) for the complex stress–strain responses observed. Parameter sensitivity analysis identifies the critical state slope ( M ) and saturation compression index (λ(0)) as dominant factors. The model's practical utility is confirmed via a C++ implementation in FLAC3D, accurately replicating experimental stress paths with a mean error below 8%. Complementing this, a shallow feedforward ANN model (2-5-4 architecture) is developed, which predicts key shear strength parameters ( C cu , C ′, φ cu , φ′) from basic index properties (moisture content, saturation) with near-perfect fidelity ( R 2 > 0.998). This dual-pronged approach provides a robust and versatile toolkit for the analysis and design of critical infrastructure, such as tunnels in arid regions, founded on unsaturated geomaterials.
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Research context

OpenAlex topics: Geotechnical Engineering and Soil Mechanics · Rock Mechanics and Modeling · Soil and Unsaturated Flow

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Kunming University of Science and Technology

Intelligent Infrastructure Operation and Maintenance Technology Innovation Team of Yunnan Provincial Department of Education, Kunming University of Science and Technology, Kunming, China

Kunming Prospecting Design Institute of China Nonferrous Metals Industry Co., Ltd

Yunnan Key Laboratory of Geotechnical Engineering and Geohazards, Kunming, China

Funding

Analysis and Testing Foundation of Kunming University of Science and Technology · Major Science and Technology Special Plan of Yunnan Province Science and Technology Department

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