Reducing Calcium Dissolution in Tunnel Shotcrete: The Impact of Admixtures on Shotcrete Performance.

Groundwater infiltration into tunnels causes water to percolate through the fissure channels in the initial support shotcrete. This results in the dissolution and outflow of calcium hydroxide, a key product of cement hydration. This process significantly incurs the formation of crystallization blockages in the tunnel drainage systems. Optimizing the shotcrete mixing ratio is a feasible way to mitigate these blockages. Therefore, this study conducts calcium dissolution tests to investigate the impact of six admixtures, namely, antialkali agent, nanosilica, nanosilica carbonate, fly ash, sodium methyl silicate waterproofing agents, and silane waterproofing agents, on calcium dissolution resistance. Also, mechanical and microscopic tests are carried out to examine their impact on the strength and pore structure of the shotcrete. The objective of this study is to determine the optimal admixture for enhancing the calcium dissolution resistance of shotcrete. Results indicate that the antialkali agent significantly reduces the calcium leaching content of shotcrete. When the dosage is 14%, the calcium leaching amount is reduced by 68.4% in 28 days. Followed by nanosilica and silane waterproofing agents, with optimal dosages of 12 and 0.4%, respectively, the dissolution amount of calcium ions in shotcrete was reduced by 32.87 and 26.5%, respectively. Fly ash curing for 28 days can also reduce the calcium ion dissolution of shotcrete, while nanocalcium carbonate and sodium methyl silicate have little effect on the calcium dissolution of shotcrete. The antialkali agent with a strong calcium ion dissolution effect can improve the tensile strength of shotcrete under long-term curing conditions, which can be increased by 52%, but it compromises the growth of compressive strength. Nanosilica, fly ash, and silane waterproofing agents can improve both the compressive strength and tensile strength of shotcrete under long-term curing conditions. Specifically, at 28 days of curing, the compressive strength increased by 16.83, 28.8, and 20% and the tensile strength increased by 50.24, 60, and 64.5%. In addition, the microscopy results show that the antialkali agent, nanosilica, and silane waterproofing agents promote the hydration process of cement to form ettringite with a low and stable calcium-silicon ratio and reduce calcium hydroxide crystals. Nanosilica and silane waterproofing agents optimize the pore distribution in shotcrete by increasing beneficial pores, decreasing harmful pores, and reducing total porosity.

Texture and semantic integrated small objects detection in foggy scenes.

In recent years, small objects detection has received extensive attention from scholars for its important value in application. Some effective methods for small objects detection have been proposed. However, the data collected in real scenes are often foggy images, so the models trained with these methods are difficult to extract discriminative object features from such images. In addition, the existing small objects detection algorithms ignore the texture information and high-level semantic information of tiny objects, which limits the improvement of detection performance. Aiming at the above problems, this paper proposes a texture and semantic integrated small objects detection in foggy scenes. The algorithm focuses on extracting discriminative features unaffected by the environment, and obtaining texture information and high-level semantic information of small objects. Specifically, considering the adverse impact of foggy images on recognition performance, a knowledge guidance module is designed, and the discriminative features extracted from clear images by the model are used to guide the network to learn foggy images. Second, the features of high-resolution images and low-resolution images are extracted, and the adversarial learning method is adopted to train the model to give the network the ability to obtain the texture information of tiny objects from low-resolution images. Finally, an attention mechanism is constructed between feature maps of the same scale and different scales to further enrich the high-level semantic information of small objects. A large number of experiments have been conducted on data sets such as "Cityscape to Foggy" and "CoCo". The mean prediction accuracy (mAP) has reached 46.2% on "Cityscape to Fogg", and 33.3% on "CoCo", which fully proves the effectiveness and superiority of the proposed method.

Fault diagnosis method of rolling bearing based on multiple classifier ensemble of the weighted and balanced distribution adaptation under limited sample imbalance.

Aiming at the minority samples cannot be effectively diagnosed when the samples are limited and imbalanced, a multiple classifier ensemble of the weighted and balanced distribution adaptation method (MC-W-BDA) is presented to solve the rolling bearing's fault diagnosis problem under the limited samples imbalance. We adopt random sampling to obtain enough different training sample sets whose base classifiers are trained in the Reproducing Kernel Hilbert Space. The appropriate base classifiers are integrated into strong classifiers by multiple classifier ensemble strategy to obtain the final result of classification. In addition, we propose A-distance method to automatically set the optimal parameter (balance factor) in MC-W-BDA. Experimental verification verifies the feasibility and effectiveness of proposed approach.