TY - JOUR
T1 - A machine-learning-based model for accurately predicting the bending behavior of sandwich beams
AU - Yan, Yu
AU - Duan, Yu
AU - Peng, Zhilong
AU - Zhang, Bo
AU - Yao, Yin
AU - Chen, Shaohua
N1 - Publisher Copyright:
© 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2026/7
Y1 - 2026/7
N2 - Sandwich beams always exhibit a shear-bending-coupling mechanical behavior, which is difficult to be accurately predicted by existing bending theories due to the prior assumptions, such as an overlook of the interlayer shear stress and a zigzag displacement distribution on the beam's cross-section. In this work, based on the large dataset provided by finite element simulation, an alternative model of sandwich beams is generated using machine learning (ML) methods without any assumptions, and applied to analyze the influence of modulus ratio and thickness ratio between the surface and core layers on the stress and deformation fields of sandwich beams under four point bending. It is found that an increase in modulus ratio not only causes significant shear stress in the core layer, but also results in a mixed tensile and compressive stress state in the surface layer, causing the neutral plane to shift from the core layer to the surface layers. In contrast, the thickness ratio only affects the magnitude of stress and deformation in the beam, and has little effect on the position of the neutral plane. All the predicted results are consistent with existing experimental and numerical ones, indicating that the modulus difference between the surface and core layers dominates the shear-bending-coupling behavior of sandwich beams and determines the applicability of existing bending theories. The present ML-based model should be of guiding value for the design of sandwich structures with desirable mechanical properties.
AB - Sandwich beams always exhibit a shear-bending-coupling mechanical behavior, which is difficult to be accurately predicted by existing bending theories due to the prior assumptions, such as an overlook of the interlayer shear stress and a zigzag displacement distribution on the beam's cross-section. In this work, based on the large dataset provided by finite element simulation, an alternative model of sandwich beams is generated using machine learning (ML) methods without any assumptions, and applied to analyze the influence of modulus ratio and thickness ratio between the surface and core layers on the stress and deformation fields of sandwich beams under four point bending. It is found that an increase in modulus ratio not only causes significant shear stress in the core layer, but also results in a mixed tensile and compressive stress state in the surface layer, causing the neutral plane to shift from the core layer to the surface layers. In contrast, the thickness ratio only affects the magnitude of stress and deformation in the beam, and has little effect on the position of the neutral plane. All the predicted results are consistent with existing experimental and numerical ones, indicating that the modulus difference between the surface and core layers dominates the shear-bending-coupling behavior of sandwich beams and determines the applicability of existing bending theories. The present ML-based model should be of guiding value for the design of sandwich structures with desirable mechanical properties.
KW - Bending behavior
KW - Machine learning (ML) method
KW - Modulus and thickness ratios
KW - Sandwich beam
KW - Stress and deformation fields
UR - https://www.scopus.com/pages/publications/105036253738
U2 - 10.1016/j.mechmat.2026.105701
DO - 10.1016/j.mechmat.2026.105701
M3 - Article
AN - SCOPUS:105036253738
SN - 0167-6636
VL - 218
JO - Mechanics of Materials
JF - Mechanics of Materials
M1 - 105701
ER -