Gongcheng Kexue Yu Jishu/Advanced Engineering Science

In order to solve the problem of low audit efficiency due to the use of power exponent, bilinear mapping, point hash mapping and other expensive operations in the existing certificateless cloud auditing scheme, an efficient certificateless cloud auditing scheme was designed in this paper. In the key generation stage, combined with the certificateless signature technology, the user’s public and private keys were generated by the key generating center (KGC) in cooperation with the user, so that the strong dependence of auditing system security on KGC security was avoided. Consequently, the shortcomings of complex public key certificate management under the public key infrastructure (PKI) system and the inherent key escrow issues under the identity-based cloud audit scheme were solved. In the data preprocessing stage, the data was encrypted and divided into blocks. Therefore, the data content privacy was protected and the calculation and communication overhead of the scheme was reduced. In the data dynamic update stage, the virtual index data structure was used to realize the insertion, deletion and modification of data blocks. As a result, the extra computational overhead caused by label recalculation was avoided. In the data auditing stage, the task of users to verify the integrity evidence was replaced by a third party auditor (TPA), which reduces the user’s computational burden. In the section of security analysis, it was proved that the substitution attack from the cloud was resisted, the privacy protection was achieved, and the forgery attacks by two types of adversaries were resisted. In the performance analysis part, numerical analysis and comparison of the proposed scheme with the existing schemes were carried out on the JPBC library. The experiments show that the proposed scheme significantly reduces the computational cost.

When a water-rich karst cavity exists in front of the tunnel face, the thickness of the anti-inrushing rock mass between the tunnel face and the water-rich karst cavity will gradually decrease as the excavation of the tunnel progresses. Once the thickness of the anti-inrushing rock mass reaches the limit, the water inrush disaster will occur. An accurate prediction of the minimum safe thickness of the anti-inrushing structure can effectively avoid the occurrence of water inrush disaster in karst area. For the shear failure and bending failure modes of the anti-inrushing structure caused by water pressure, the corresponding mechanical models of anti-inrushing structure are established. The minimum safe thicknesses of anti-inrushing structure for the two models are calculated by using the shear strength criteria, the bending strength criteria, and the elastic thin-plate theory. The results calculated by present models are in good agreement with those calculated on the basis of the bending theory of fixed beam, the shear theory of rock column, and catastrophe theory. The results of parameter sensitivity analysis show that: 1) The water-insulating rock mass of deep-buried tunnel is prone to the bending failure, and the water-insulating rock mass of shallow tunnel is prone to the shear failure. 2) The minimum safe thickness of anti-inrushing structure is positively correlated with tunnel excavation radius, karst water pressure and Poisson’s ratio, and the order of influence degree from large to small is tunnel excavation radius, karst water pressure and Poisson’s ratio of surrounding rock. 3) The boundary conditions of the model will affect the minimum safe thickness of the anti-inrushing structure. When the excavation radius R<6 m and the karst water pressure Pw<0.35 MPa, the minimum safe thickness can be calculated based on the boundary simply supported condition. When R≥6 m and Pw≥0.35 MPa, the minimum safe thickness need to be calculated based on the fixed boundary conditions. This method provides a new way for the calculation of water gushing of circular tunnel in water-rich karst area. Finally, combined with a karst tunnel with water inrush, the corresponding parameters are substituted into the bending type Ⅰ expression, and the minimum safe thickness is generally consistent with the thickness reserved in the project, which shows that the analytical solution of the minimum safe thickness in this paper is reasonable.

The landslide—blocking river—dammed lake disaster chain is a typical disaster in mountain and gorge areas. The interaction between landslides and rivers, as the linking process in this disaster chain, often determines the scale of continuation of subsequent disasters in time and space. It is critical to reproduce the complex fluid-solid coupling process in the assessment of the landslide-blocking river disasters. By using the Drucker–Prager model to simulate the sliding of the landslide body, along with an artificial equation of state to model the flow of water, the two-phase double-point material point method (TPDP–MPM) program was developed. The simulation of the landslide-water body interaction process on an experimental scale was conducted and verified the reliability of TPDP–MPM in multiphase coupling problems. On this basis, a landslide—dammed lake disaster chain, namely the Pingdu River being blocked by the Erhuang Village landslide, Shiyan City, Hubei in 2011, was reproduced by the TPDP–MPM program. According to the TPDP–MPM simulation results, this disaster could be divided into four stages: unstable initiation, high-speed sliding, river braking, and accumulation. Based on the velocity field of the landslide mass and the evolution of the kinetic energy, the sliding pattern of the Erhuang Village landslide and the mechanism of river braking were revealed. The research results provided a theory and method for the whole process analysis and risk assessment of the landslide—dammed lake disaster chain

A circular carbon steel tube was added to the core concrete inside square stainless-steel tubes, the constraint effect coefficient to the core concrete and the ductility of the composite CFST sections could be improved remarkably, as a result, the anti-corrosion protection and fire protection of traditional steel structures could be reduced, furthermore, the disadvantageous influence of the demolished concrete lumps (DCLs) in new concrete could also be improved. Sixteen composite-sectioned concrete-filled stainless steel tubular (CS–CFSST) stub columns filled with high-strength demolished concrete lumps (DCLs) were tested. The influence of factors such as the replacement ratio of the demolished concrete lumps, confinement factor, and different concrete strength of new or demolished concrete on the mechanical performance was investigated. According to the testing results, the axial bearing capacity of the CS–CFSST stub columns decreased with the increase of the mix ratio of DCLs, the maximum decreasing amplitude of the bearing capacity was 6.8% with the replacement ratio of 20%. The failure modes of the CS–CFSST stub columns were quite familiar with those of normal CS–CFSST stub columns filled with normal fresh concrete. Two types of failure modes, drum-shaped and shear failure modes were found in the inner circular CFST stub columns, and excellent ductility and residual bearing capacity were also found as the load-deformation curves kept horizontally or ascended slowly, the residual bearing capacity of all the specimens were higher than 63% of its bearing capacity. The simplified method of constraint effect coefficient in the CS–CFSST stub column was proposed, and four calculation methods of the bearing capacity of the CS–CFSST stub columns infilling with DCLs were compared, coincident results were obtained using the calculation method considering the ultimate bearing capacity reduction of the circular CFST section

In order to solve three problems of traditional recommendation models, i.e., data sparsity, low robustness and the lack of deep-level semantics among heterogeneous features, a novel correlation visual adversarial Bayesian personalized ranking (CVABPR) recommendation model was proposed. First, based on the movie titles in the original MovieLens datasets, the corresponding movie posters were downloaded from Internet movie database (IMDB) to construct two multimodal datasets named MovieLens-100k-WMI and MovieLens-1M-WMI, respectively. Second, a group of heterogeneous but complementary image features were extracted using the SENet model to describe movie posters accurately. Then, the cluster canonical correlation analysis model was improved to mine the implicit cluster canonical correlation between the heterogeneous features. Afterwards, the correlation was used to optimize the visual Bayesian personalized ranking (VBPR) model to better depict the movies to be recommended. Finally, a perturbation factor was absorbed into the recommendation model to enhance the robustness of the CVABPR model through adversarial learning, making the recommendation model more stable and generating high-quality recommendation results. To verify the proposed CVABPR model, a set of experiments were carried out on two multimodal datasets. Evident performance improvements of the CVABPR model were observed on the two datasets. Specifically, a 3.802% performance improvement of the mean average precision (MAP) metric was obtained on the MovieLens-100k-WMI dataset, and a 4.609% performance improvement of the MAP metric was observed on the MovieLens-1M-WMI dataset. The mainstream baseline was defeated by the CVABPR model. Based on ablative analysis experiments, a more important role of the adversarial learning strategy was found compared with the cluster canonical correlation. Additionally, larger performance improvements were observed on the MovieLens-1M-WMI dataset with higher data sparsity. The key challenges of data sparsity and the lack of deep semantic among heterogeneous features were solved to a certain degree. Meanwhile, the CVABPR model has strong robustness