The deformation and damage characteristics of surrounding rock grow gradually with the increase of mining depth, and the mechanical behavior and damage mechanism of coal–rock materials vary greatly. In order to reveal the deformation and damage dynamic characteristics of coal–rock materials in deep mines, the macroscopic deformation characteristics of coal, rock, and concrete samples under uniaxial compression were studied. The macroscopic deformation amount, velocity, and acceleration of different samples were analyzed. The coal and rock samples exhibit regular dynamic characteristics before they lose stability and fail. The axial strain response of the coal samples changes significantly during the compact and elastic deformation stages. Besides, the relationship between the surface damage and the macroscopic deformation of sample was studied by means of visualization and image processing. The macroscopic deformation index of coal–rock materials changes significantly before and after the destabilization and failure. Based on the deformation and failure dynamic characteristics of coal and rock, the evolution rule of deformation critical values was taken as the deformation and destruction stages, which revealed the dynamic characteristics during the deformation and failure process of coal–rock materials in deep mines. The deformation critical values can be used to realize early warning of deformation and fracture of coal and rock materials in deep mines.
e static load carrying capacity of a noncorroded reinforced concrete (RC) simply supported beam is numerically simulated by ABAQUS software, and the reliability of the finite element model is verified by comparing with the test results. Based on the above model, the macroscopic mechanical properties of the beam under different degrees of corrosion are calculated. In the calculation, the degradation of the bond-slip performance and mechanical properties of corroded rebars and the coupling effect on the bearing capacity and ductility degradation of the beams are considered. e results show that, under conditions of slight corrosion, the degradation of bond-slip performance between the rebar and concrete has no significant influence on the bearing capacity of the beam, while the degradation of the corroded rebar had a significant effect. Under moderate and severe corrosion conditions, the bearing capacity and ductility degradation caused by bond-slip are dominant in the mechanical property degradation of the beam. Overall, the macroscopic mechanical properties of the corroded beam are influenced by the coupling effect of bond-slip degradation and the mechanical property degradation of the rebar. With the increase in the corrosion rate, the bearing capacity and ductility of the beam are decreased, and its brittleness is increased.
The progress of construction and safe production in mining, water conservancy, tunnels, and other types of deep underground engineering is seriously affected by rockburst disasters. This makes it essential to accurately predict rockburst intensity. In this paper, the ratio of maximum tangential stress of surrounding rock to rock uniaxial compressive strength (σθ/σc), the ratio of rock uniaxial compressive strength to rock uniaxial tensile strength (σc/σt), and the elastic energy index of rock (Wet) were chosen as input indices, and rockbursts were graded as level I (none rockburst), level II (light rockburst), level III (medium rockburst), and level IV (strong rockburst). A total of 104 groups of rockburst engineering samples, collected widely from around the world, were divided into a training set (84 groups of samples) and a test set (20 groups of samples). Based on the kernel principal component analysis (KPCA), the adaptive particle swarm optimization (APSO) algorithm, and the support vector machine (SVM), the KPCA-APSO-SVM model was established. The proposed model showed satisfactory classification performance: the prediction accuracies of the training set and test set were 98.81% and 95%, respectively. In addition, the trained prediction model was applied to five rockburst engineering cases and compared with the BP neural network model, SVM model, and APSO-SVM model. The comparative results show that the KPCA-APSO-SVM model has a higher prediction accuracy; as such, it provides a new reliable method for rockburst prediction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.