Mechanical properties of magnesium phosphate cement-based ultra-high-performance concrete prepared at negative temperatures: A prediction model based on adaptive weighted stacking

Abstract

Magnesium phosphate cement-based ultra-high-performance concrete (MPC-UHPC), which is characterized by rapid hardening and high early strength, is recognized as an ideal material for the post-disaster reconstruction and wartime emergency repair of critical infrastructure in cold regions. However, laboratory conditions are frequently inadequate for accurately simulating natural severe cold environments, which can lead to deviations in the development of the mechanical properties of MPC-UHPC from anticipated performance, thereby increasing the time and cost associated with trial mix designs. In this study, an adaptive weighted stacking model was proposed, which quantifies the prediction errors of base learners. This model achieved a coefficient of determination of 0.917 and 0.935 for predicting the flexural and compressive strength of MPC-UHPC at negative temperatures, respectively, significantly outperforming any single model. Steel fiber content and ambient temperature were identified as the dominant factors influencing the flexural and compressive strength, respectively. Shapley additive explanations analysis further indicated that a low water-to-cement ratio, high V_f, and low borax-to-MgO ratio are essential for achieving optimal mechanical properties at negative temperatures; however, these parameters must be adjusted while ensuring acceptable workability. This study provides a reliable quantitative tool for predicting the mechanical properties of MPC-UHPC in severely cold regions, which serves as a powerful guide for optimizing mix proportions with respect to hardened-state performance.

Graphical abstract

Keywords

magnesium phosphate cement / ultra-high-performance concrete / machine learning / mechanical properties prediction / negative temperature

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