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In order to promote the engineering application of recycled concrete, the effects of PPF and nano-TiO2 dioxide on the mechanical properties and durability of recycled concrete were studied. Polypropylene fiber recycled concrete(PRAC) and nano-TiO2 recycled concrete(TRAC) were prepared by adding different volume contents of PPF and nano-TiO2. The experimental findings demonstrated that the PPF and nano-TiO2 improved the splitting tensile strength of RAC better than the compressive strength. When the volume content of nano-TiO2. and PPF is 0.8% and 1.0%, respectively, the corresponding splitting tensile strength of concrete reaches the maximum value(3.4 and 3.7 MPa). The contribution rates of nano-TiO2 and PPF with different volume contents to the mechanical properties of RAC have optimal values, which are 0.4 and 1.0%, respectively. The incorporation of nano-TiO2 and PPF can effectively inhibit the loss of RAC mass and the generation of pores under freeze–thaw conditions, and slow down the decrease of dynamic elastic modulus. When the volume content of PPF is 1.0% and the volume content of nano-TiO2 is 0.4%, the protection effect on the internal structure of RAC is better, and its carbon resistance is better. The results of RSM model analysis and prediction show that both PPF and nano-TiO2 can be used as admixture materials to improve the mechanical properties and durability of RAC, and the comprehensive improvement effect of PPF on RAC performance is better than that of nano-TiO2. [ABSTRACT FROM AUTHOR]

Accurately predicting the geometric structure of a building's roof as a vectorized representation from a raster image is a challenging task in building reconstruction. In this paper, we propose an efficient and precise parsing method called Roof-Former, based on a vision Transformer. Our method involves three steps: (1) Image encoder and edge node initialization, (2) Image feature fusion with an enhanced segmentation refinement branch, and (3) Edge filtering and structural reasoning. Our method outperforms previous works on the vectorizing world building dataset and the Enschede dataset, with vertex and edge heat map F1-scores increasing from 87.1%, 76.2% to 89.1%, 78.1%, and from 69.7%, 68.8% to 71.2%, 69.5%, respectively. Furthermore, our method demonstrates superior performance compared to the current state-of-the-art based on qualitative evaluations, indicating its effectiveness in extracting global image information while maintaining the consistency and topological validity of the roof structure. [ABSTRACT FROM AUTHOR]