Analysis of the Relationship between Mechanical Properties and Pore Structure of MSW Incineration Bottom Ash Fine Aggregate Concrete after Freeze-Thaw Cycles Based on the Gray Theory

The destruction of concrete building materials in severely cold regions of the north is more severely affected by freeze-thaw cycles,and the relationship between the mechanical properties and pore structure of concrete with fine aggregate from municipal solid waste(MSW)incineration bottom ash after freeze-thaw cycles is analyzed under the degree of freeze-thaw hazard variation.In this paper,the gray correlation method is used to calculate the correlation between the relative dynamic elastic modulus,compressive strength,and microscopic porosity parameters to speculate on the most important factors affecting their changes.The GM(1,1)model was established based on the compressive strength of the waste incineration ash aggregate concrete,the relative error between the simulated and actual values in the model was less than 5%,and the accuracy of the model was level 1,indicating that the GM(1,1)model can well reflect the change in the compressive strength of the MSW incineration bottom ash aggregate concrete during freeze-thaw cycles.Using the gray correlation method,the correlation between the relative dynamic elastic modulus,compressive strength,air content,specific surface area,pore spacing coefficient,and pore average chord length was calculated,and the pore spacing coefficient and pore average chord length were determined to be highly correlated with each other.This determination can help analyze and infer the deterioration mechanism of concrete subject to freeze-thaw cycles.These results can provide a theoretical basis for guiding the engineering practice of concrete with fine aggregates of household bottom ash in the northern cold region.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.