Publications

Abstract Slabbing failure and strain rock burst are the main failure patterns during the excavation and construction phases of deep tunnels in hard and brittle rock, which cause unexpected equipment damage and casualties. This study presents a numerical simulation with an unloading central hole within a hard rock specimen in the laboratory scale. A combined finite element/discrete element method (FEM/DEM) reproduces the crack initiation, propagation, and coalescence around the central hole during the entire failure process. The influence of sectional shapes, unloading rates of holes, and in situ stresses on the failure characteristics and mechanical response of typical hard and brittle rocks were investigated by analysis of the failure pattern, displacement distribution (average velocity) of monitoring elements, and released strain energy value. The numerical results indicate that the sectional shapes, unloading rates, and in situ stresses have a significant impact on the severity of destruction and failure range in hard rock under the excavation unloading conditions. Slabbing failure (stable failure) is always the dominant failure pattern around a circular hole, which shows higher bearing capacities and self‐stabilization. With the increase in unloading rates, more visible cracks are generated around the holes, and the displacement and average velocity of discrete blocks are further raised. This is particularly evident in rock specimens with holes having vertical walls, leading to intense unstable failure (strain rock burst) accompanied by a large amount of strain energy released. In situ stresses affect considerably the stability of the surrounding rock during the excavation unloading process. With constant in situ stress, the order of destruction severity around the central hole according to its sectional shape is cube > trapezoid > U‐shape > ellipse > circle. The destruction intensity is further aggravated with the increase in lateral pressure coefficient, and the failure regions are always observed in the roof and floor of the central hole. This study confirms that strain rock burst tends to be induced in hard and brittle rock tunnels with polygonal roadway section under high unloading rates and lateral pressure coefficients.

Deep rock engineering is in a high pre-stressed state before excavation. In this research, a method to calculate the release of strain energy caused by excavation in pre-stressed rock is proposed. The normal stress release after excavation leads to a reduction in strain energy in rock specimens. The influence of excavation height and width on strain energy release is inconsistent under vertical loading. When the height of the hole is 1 mm, the strain energy release is large, and the increase in height of hole leads to a slow increase in the strain energy release. When the width of the hole is 1 mm, the strain energy release is very small, and the increase in the width of the hole leads to an increasingly faster release of strain energy. This strain energy release exponentially increases with the increase in the lateral pressure coefficient, showing a trend in the second power of the lateral pressure coefficient. Moreover, the tunnel failure caused by excavation under high stress is obtained by a numerical calculation. The failure modes of the deep tunnel model are strain rockbursts caused by tangential stress concentrations and spalling caused by normal stress release, which is also observed in the failure mode of the actual tunnel.

Rock lithology recognition plays a fundamental role in geological survey research, mineral resource exploration, mining engineering, etc. However, the objectivity of researchers, rock variable natures, and tedious experimental processes make it difficult to ensure the accurate and effective identification of rock lithology. Additionally, multitype hybrid rock lithology identification is challenging, and few studies on this issue are available. In this paper, a novel multitype hybrid rock lithology detection method was proposed based on convolutional neural network (CNN), and neural network model compression technology was adopted to guarantee the model inference efficiency. Four fundamental single class rock datasets: sandstone, shale, monzogranite, and tuff were collected. At the same time, multitype hybrid rock lithologies datasets were obtained based on data augmentation method. The proposed model was then trained on multitype hybrid rock lithologies datasets. Besides, for comparison purposes, the other three algorithms, were trained and evaluated. Experimental results revealed that our method exhibited the best performance in terms of precision, recall, and efficiency compared with the other three algorithms. Furthermore, the inference time of the proposed model is twice as fast as the other three methods. It only needs 11 milliseconds for single image detection, making it possible to be applied to the industry by transforming the algorithm to an embedded hardware device or Android platform.

Abstract Hard rock failure and rockburst hazards under high in situ stresses have been the subject of deep rock mechanics and engineering. Previous investigations employed cubic rock specimens with a central hole for simulation of rock fracturing around deep tunnels at a laboratory scale, while the failure characteristics and crack evolution behavior around different shapes of holes induced by excavation unloading response have been barely considered. A commercially combined finite‐discrete element method (combined FEM/DEM) was used to investigate the failure characteristics and crack propagation process of typical hard rock specimens (marble) via the unloading of central hole with different shapes. Rock heterogeneity was also considered in the model in combination with the engineering reality. The combined FEM/DEM approach was first validated by simulating uniaxial compression and Brazilian tests. Then, the parametrical analysis was conducted in detail on the basis of five different sectional shapes of central holes, including a circle, ellipse, U‐shape, trapezoid, and cube, and two lateral pressure coefficients. Analysis of crack propagation paths, released strain energy, displacement, and average velocity distribution of the monitoring points around the central hole suggests that the failure degree and destruction intensity are strongly related to the sectional shape and lateral pressure coefficients. Hard and brittle rock failure induced by the excavation unloading effect can be classified as stable failure (slabbing failure) and unstable failure (strain rockburst). The cubic, trapezoidal, and U‐shaped holes within the specimen are the most likely to induce unstable failure, while stable failure is the dominant failure pattern around circular and elliptical holes. The lateral pressure coefficient λ was also found to affect failure position and intensity (only for the axisymmetric section) around the central hole. The influence of rock heterogeneity on failure intensity and range around the central hole within the specimen was also discussed. This study emphasizes the importance and necessity of the excavation unloading effect when evaluating surrounding rock failure around deep tunnels.