Analysis of Compressive Behaviors of Concrete Mesoscale Models Based on the SISSO Algorithm

BAI Yufei; ZHANG Xinyu; QI Xiaopeng; ZHANG Yuhang; WANG Zhiyong

doi:10.21656/1000-0887.450326

Volume 47 Issue 3

Mar. 2026

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Article Navigation > Applied Mathematics and Mechanics > 2026 > 47(3): 354-366

BAI Yufei, ZHANG Xinyu, QI Xiaopeng, ZHANG Yuhang, WANG Zhiyong. Analysis of Compressive Behaviors of Concrete Mesoscale Models Based on the SISSO Algorithm[J]. Applied Mathematics and Mechanics, 2026, 47(3): 354-366. doi: 10.21656/1000-0887.450326

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Analysis of Compressive Behaviors of Concrete Mesoscale Models Based on the SISSO Algorithm

doi: 10.21656/1000-0887.450326

BAI Yufei^{1, 2
,},
ZHANG Xinyu^{1, 2},
QI Xiaopeng^{1, 2},
ZHANG Yuhang³,
WANG Zhiyong^{1, 2
,
,}

1.
College of Aeronautics and Astronautics, Taiyuan University of Technology, Taiyuan 030024, P. R. China
2.
Shanxi Key Laboratory of Material Strength and Structural Impact, Taiyuan 030024, P. R. China
3.
Institute for Nuclear Emergency and Safety, China Institute for Radiation Protection, Taiyuan 030024, P. R. China

Received Date: 2024-12-09
Rev Recd Date: 2025-04-05
Publish Date: 2026-03-01

Abstract

Abstract

The mechanical properties of concrete under external loads are influenced by its mesoscale components. Due to their heterogeneity, experimental and numerical methods struggle to reveal the impacts of mesoscale structures on the macroscopic mechanical behaviors of concrete. To effectively predict the peak stress of a 3-phase (aggregate, mortar and voids) mesoscale model of concrete under uniaxial compression, a framework for mesoscopic concrete was established with PYTHON and ABAQUS, to generate a dataset of models with varying aggregate volume fractions, porosities and peak compressive stresses. The sure independence screening and sparsifying operator (SISSO) machine learning algorithm, combined with the K-fold cross validation for hyperparameter optimization, was employed to derive a formula describing the effects of the aggregate volume fraction and the porosity on the peak stress. The formula accurately describes the peak stress variation trend, thereby achieving precise predictions and offering physical interpretability. Compared to traditional machine learning algorithms, the SISSO demonstrates advantages of maintaining precision while reducing computation costs and improving interpretability. It overcomes the "black box" limitations of conventional methods, offering new insights for multiscale mechanical analyses of composite materials.
- mesoscale concrete model,
- uniaxial compression,
- multiscale mechanical analysis,
- symbolic regression,
- peak stress

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References(46)

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