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Hoang-Quan Nguyen

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Open access Jul 2026

Elastic and failure properties of 2D primitive TPMS lattices with varying shape factor

Architected materials based on Triply Periodic Minimal Surfaces (TPMS) have demonstrated outstanding mechanical performance; however, their two-dimensional (2D) counterparts remain insufficiently explored. This study presents a comprehensive numerical investigation of the elastic response and failure behavior of 2D Primitive TPMS lattice structures, with particular emphasis on the role of geometric parameters C1 and C2. A phase-field damage framework is employed to capture the full mechanical evolution, from initial elastic deformation to crack initiation and subsequent propagation leading to ultimate failure. The results reveal that mechanical performance is governed not only by relative density but also critically by geometric configuration. In particular, structures with nearly identical relative densities can exhibit strength variations of up to five times, underscoring the dominant influence of shape parameters. In addition, certain configurations display pronounced auxetic behavior, with a minimum Poisson’s ratio of −0.12 observed for C1 = 1.2 and C2 = 0.8. Failure consistently initiates at the specimen center, where stress concentration is most severe. Overall, this study provides both a high-quality numerical dataset and fundamental insights into the geometry–mechanical performance relationship of 2D TPMS lattices. The findings establish a foundation for the rational design and optimization of TPMS-based architected materials in advanced engineering applications.

Dinh-Thao-Anh Truong, Minh-Cuong Le, Hoang-Quan Nguyen et al. · 0 citations
Open access Jul 2026

Experimental and numerical validations of predictive models for compressive strength of pervious concrete

Pervious concrete (PC) is a key material in sustainable urban development, but its design is complicated by the inverse relationship between permeability and compressive strength. To optimize its use, various predictive models—analytical, numerical, and data-driven—have been developed. However, a comparative validation of these diverse approaches on a consistent experimental dataset is lacking. This study aims to validate and compare the performance of three fundamental predictive frameworks: an analytical model based on micromechanics, a numerical simulation using the phase-field method, and an Artificial Intelligence (AI) approach, specifically a "white-box" symbolic regression model. An experimental case study involving the fabrication and testing of PC specimens was conducted to provide a new, independent dataset for validation. The results show that the symbolic regression, finite element method, and micromechanical models demonstrate strong agreement with the experimental data, achieving a high coefficient of determination. While black-box AI models often offer high accuracy, this study highlights that simpler, interpretable models provide a compelling balance of precision and practical applicability for engineering design.

Van-Hung Nguyen, Hoang-Quan Nguyen, B. Tran et al. · 0 citations

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