Terrorist attacks or contact explosions could trigger severe damage to structural components or even cause the local or global collapse of the structure in civil engineering and infrastructures. Single‐steel‐concrete (SSC) slabs consist of a concrete core and one steel faceplate combined with shear studs or tie bars that can be applied in resisting explosion or blast load. This paper experimentally and numerically studies the blast performance of the scale SSC specimens under a contact explosion. The damage modes, deflections, and acceleration response of the SSC specimens are obtained and compared by experimental and numerical methods. And then the influences of the explosive, the shear stud length, and the steel plate thickness on the damage modes, acceleration, and displacement response are studied parametrically. It is observed that both of the concrete layers in SSC slabs have crater failure at the impacted surface and steel plates occur local deformation or buckling. No penetrability failure generates in the slabs because of the existence of the steel plate, which can improve the blast resistance and decrease the maximum deflections of the specimens. Finally, an empirical equation is developed to estimate the midspan displacement of the slab using the multivariable regression method (MNRM).
In the process of microbial curing of desert aeolian sandy soil, we thought of the water-holding properties of straw flour in view of the high proportion of fine particles and poor water retention of desert aeolian sandy soil, and therefore designed an experiment to add straw flour to enhance the effect of microbial curing of desert aeolian sandy soil. The sand columns prepared under different curing stages were analysed by low-field NMR techniques. The test results show that: (1) the curing product calcium carbonate can effectively fill the pores of the sand and reduce the total porosity, and the addition of straw powder increases the total porosity of the sand column, especially the number of large pores (5–60µm) increases significantly; (2) The addition of straw powder increased the original sand column pore volume and pore size, significantly increasing the water holding capacity of the cured sand column, and the free water content of the sand column was significantly greater than that of the original sand column;(3) The pore filling rate of the sand column decreased after mixing with straw powder, which improved the uniformity of calcium carbonate precipitation within the sand column, but the shear strength was lower than that of the original sand column. The results of the study provide a theoretical basis and data support for optimising the curing effect of desert aeolian sandy soil and its water-holding capacity.
In concrete structures, surface cracks are an important indicator for assessing the durability and serviceability of the structure. Existing convolutional neural networks for concrete crack identification are inefficient and computationally costly. Therefore, a new CSWin transformer-skip (CSW-S) is proposed to classify concrete cracks. The method is optimized by adding residual links to the existing CSWin transformer network and then trained and tested using a dataset with 17,000 images. The experimental results show that the improved CSW-S network has an extended range of extracted image features, which improves the accuracy of crack recognition. A detection accuracy of 96.92% is obtained using the trained CSW-S without pretraining. The improved transformer model has higher recognition efficiency and accuracy than the traditional transformer model and the classical CNN model.
The proportion of natural sand replaced by steel slag sand affects the volumetric stability of steel slag mortar and steel slag concrete. However, the steel slag substitution rate detection method is inefficient and lacks representative sampling. Therefore, a deep learning-based steel slag sand substitution rate detection method is proposed. The technique adds a squeeze and excitation (SE) attention mechanism to the ConvNeXt model to improve the model's efficiency in extracting the color features of steel slag sand mix. Meanwhile, the model's accuracy is further enhanced by using the migration learning method. The experimental results show that SE can effectively help ConvNeXt acquire images' color features. The model's accuracy in predicting the replacement rate of steel slag sand is 87.99%, which is better than the original ConvNeXt network and other standard convolutional neural networks. After using the migration learning training method, the model predicts the steel slag sand substitution rate with 92.64% accuracy, improving accuracy by 4.65%. The SE attention mechanism and the migration learning training method can help the model acquire the critical features of the image better and effectively improve the model's accuracy. The method proposed in this paper can identify the steel slag sand substitution rate quickly and accurately and can be used for the detection of the steel slag sand substitution rate.
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