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This study aims to quantitatively analyze the three-dimensional microstructure of ultra-thin HDF paving boards and molding boards, in order to explore the modeling methods of HDF and attempt to predict its mechanical properties more accurately through theoretical and simulation analyses. In this study, non-destructive analysis of the internal microstructure was conducted using computed tomography (CT) imaging technology. The characteristics of microstructural evolution during the hot compression molding process were explored, and all analyzed data were utilized for 3D reconstruction and simulation model construction. Ultra-thin fiberboards with different fiber ratios were modeled through volume compression and experimentally simulated in tension. The results were compared with experimentally determined results for verification purposes. Additionally, differences in surface roughness, water absorption, and expansion of ultra-thin fiberboards with varying fiber ratios were analyzed. The findings indicate that different fiber ratios have a significant impact on the mechanical properties of ultra-thin fiberboards; tensile strength increases with an increase in medium-length fibers content when fibers are pulled or split, highlighting the key role played by the strength of the fibers themselves. Furthermore, it was observed that surface roughness and water-absorbing expansion rate are lower when a higher proportion of fine fibers is present. The results from this study hold great significance for gaining a deeper understanding of the composition, structure, and properties of ultra-thin fiberboards as well as exploring modeling methods for high-density fiberboards. They also provide guidance for optimizing material formulations and preparation processes while improving performance and quality standards for poplar-ultra-thin fiberboards. • We quantitatively analysed the three-dimensional microstructure of ultra-thin fibreboard paving slabs and moulded panels. • We explored the modelling method of high-density fibreboard and obtained a model of high-density fibreboard with a volume fraction of 90%. • We explored the effects of different fibre ratios on the mechanical properties of ultra-thin fibreboards. [ABSTRACT FROM AUTHOR]

• Based on image feature extraction technology to build aggregate feature data set. • A fusion feature importance analysis method is proposed. • Establish a random forest and XGBoost aggregate shape classification model. • The ability to recognize the 3D features of aggregates in 2D images are improved. Aggregate plays the role of skeleton filling in asphalt pavements. The shape of the aggregate affects the embedded structure between the aggregates, thus affecting the performance of asphalt concrete. In this study, extreme gradient boosting (XGBoost) classification is used to study the automatic shape classification of aggregates. The expression of main and microscopic features of aggregate was improved by transforming aggregate images into data, and a feature importance analysis method based on method fusion is proposed to select the feature parameters of aggregate morphology. Based on cross-validation, the XGBoost classification model was trained by optimizing the super parameter combination to complete the classification of aggregate shapes. Compared with the random forest model, the results show that the proposed method can effectively classify aggregate shapes. It is also proved that the two-dimensional images can reflect the three-dimensional features of the aggregate to some extent. This method provides a certain theoretical basis for the automatic classification of aggregate, and simultaneously it has important practical significance to promote the intelligent production of asphalt mixtures. [ABSTRACT FROM AUTHOR]