Prefabrication of composites beam reduces the construction time and makes them easily to be assembled, deconstructed, and partially repaired. The use of high‐strength frictional bolt shear connectors can greatly enhance the sustainability of infrastructure. However, researches about the concrete‐steel friction behavior are very limited. To provide a contribution to this area, 21 tests were conducted to measure the friction coefficient and slip stiffness with different concrete strength, steel strength, and surface treatment of steel. An effective finite element model was developed to investigate the ultimate bearing capacity and load‐slip characteristics of bolt shear connection. The accuracy of the proposed finite element model is validated by the tests in this paper. The results demonstrate a positive correlation between concrete strength and friction coefficient and better performance of shot‐blasted steel. It is also proved that high‐strength frictional bolt has a 30% lower bearing capacity but better strength reserve and antiuplifting than the headed stud.

A static collapse experiment was carried out to study the progressive collapse resistance and failure mechanisms and modes of a 1/3-scale,2×3-bay, and 2-story reinforced concrete frame with specially shaped columns subjected to middle column removal. A vertical concentrated load was applied to the top of the middle column to simulate the gravity load of the upper floors and the applied load was statically transmitted to the adjacent columns through the frame beams and slabs during the collapse process. The frame collapsed when the vertical displacement of the joint on the top of the failed column reached 170 mm due to the failure of beam-column joints. Based on the experimental phenomena and results, the progressive collapse-resistant behavior of the model frame is analyzed and the redistribution and transition of the load resisting mechanisms are discussed. It is concluded that the redistribution of internal force was mainly realized via the beam resisting mechanism and the compressive arch action in beams played an important role to improve the collapse-resistant capacity.

Forests and grasslands play an important role in carbon cycling. They not only absorb CO₂ from the air through vegetation biomass and soil carbon sinks, but also reduce and control the horizontal transport of soil carbon (i.e., reinforcing soil carbon storage via soil conservation), thus avoiding erosion-induced CO₂ emissions. In this study, vegetation biomass and soil carbon sinks, soil carbon reinforcement and reduced carbon emissions via soil conservation by forests and grasslands were quantified on the scale of the whole of China. The analysis was based on the distribution of biomass and the soil carbon pool and soil erosion rates derived from national surveys, as well as carbon density values from field surveys and literature. In 2021, forests and grasslands in China generated 394.18 Mt C/year (y) of steady-state carbon sinks through vertical biomass and soil absorption. The biomass carbon sinks of grasslands, and those of leaves, twigs, flowers and fruits of the forests, were not taken into account when quantifying the stable biomass sink, because they can become net producers of CO₂ due to seasonal withering and carryover, or they can form soil organic carbon as potential soil carbon sinks. The amount of horizontal soil carbon reinforcement in China’s forests and grasslands in 2021 was 20.31 Mt C/y, which was positively correlated with the reduction in the water erosion area; consequently, vertical emissions of approximately 14.89–29.78 Mt of CO₂ into the atmosphere were avoided. Overall, in 2021, China’s forests and grasslands absorbed atmospheric CO₂ and reduced emissions by 1.46–1.47 Gt CO₂/y, equivalent to approximately 13% of China’s annual fossil CO₂ emissions. This study demonstrates the fact that the adoption of forest and grassland measures sequesters carbon in soil and biota and reduces the risks of CO₂ emissions by both vertical and horizontal paths, which is important for achieving carbon neutrality and mitigating climate change.

The bearing capacity of soft clay foundations is low, which easily causes excessive cumulative and uneven settlement of the substation. This will seriously threaten the safe operation of equipment in the substation. Based on a 220‐kV substation project in a coastal soft clay ground, the settlement of the substation building is monitored on the spot, and the settlement law of soft clay ground is analyzed. Furthermore, a three‐dimensional finite element model is developed using finite element software named ZSOIL.PC. A numerical model for predicting the settlement of soft clay ground in a substation is proposed using the appropriate element type, material constitutive model, and calculation parameters. The rationality of the numerical method is validated by comparison with the measured data. After improving the soft clay ground with the cement‐soil deep mixing method, there are still certain degrees of cumulative and differential settlements in the substation distribution device building, but both are within the specification requirements. The settlement of the soft soil ground in the substation increases with time, but the growth rate gradually decreases and ultimately tends to stabilize after a certain period of time, ensuring the uniformity of foundation treatment is the key to controlling the differential settlement. The research results can provide a reference for the subsequent installation and construction of electrical equipment and the design of substation engineering in soft soil ground.

Water leakage defects often occur in underground structures, leading to accelerated structural aging and threatening structural safety. Leakage identification can detect early diseases of underground structures and provide important guidance for reinforcement and maintenance. Deep learning-based computer vision methods have been rapidly developed and widely used in many fields. However, establishing a deep learning model for underground structure leakage identification usually requires a lot of training data on leakage defects, which is very expensive. To overcome the data shortage, a deep neural network method for leakage identification is developed based on transfer learning in this paper. For comparison, four famous classification models, including VGG16, AlexNet, SqueezeNet, and ResNet18, are constructed. To train the classification models, a transfer learning strategy is developed, and a dataset of underground structure leakage is created. Finally, the classification performance on the leakage dataset of different deep learning models is comparatively studied under different sizes of training data. The results showed that the VGG16, AlexNet, and SqueezeNet models with transfer learning can overall provide higher and more stable classification performance on the leakage dataset than those without transfer learning. The ResNet18 model with transfer learning can overall provide a similar value of classification performance on the leakage dataset than that without transfer learning, but its classification performance is more stable than that without transfer learning. In addition, the SqueezeNet model obtains an overall higher and more stable performance than the comparative models on the leakage dataset for all classification metrics.