Qigui Yang

Three-dimensional laser scanning technology has increasingly gained favor among professionals in tunnel monitoring. A fundamental challenge in tunnel point cloud processing is to efficiently manage massive datasets using appropriate data structures and accurately extract features such as tunnel axes and cross-sections. However, existing studies often disconnect tunnel point cloud indexing from post-processing tasks. Conventional structures (e.g., voxels, octrees) struggle with long strip-like uneven spatial distribution, resulting in imbalanced trees with numerous empty nodes, which are incompatible with axis-aligned operations. Therefore, this study proposes a dual-layer indexing structure tailored to tunnel geometries. The upper layer reorganizes the tunnel point cloud along its axis, while the lower layer leverages local octrees for fast data querying and updates. In implementation, we introduce a merge-based octree generation strategy for ultra-large-scale datasets, and a rapid Hough transform-based algorithm for tunnel boundaries and axes extraction. Experimental results demonstrate that the proposed method successfully supports the management and visualization of a tunnel point cloud exceeding 6 billion points, significantly enhancing efficiency in narrow tunnel scenarios and streamlining various axis-aligned post-processing tasks.

The risk classification of barrier lakes is the key to conducting emergency treatment in a scientific manner. However, risk classification faces difficulties such as a short time for risk evaluation, complex evaluation indicators, difficulty in obtaining information quickly, and quantifying index weights. Based on this, this paper constructs a quantitative risk classification model for barrier lakes based on D-AHP. On the basis of studies on nearly 100 cases of barrier lakes, an eight-factor evaluation index system and quantitative classification are proposed. The methods of rapid calculation of reservoir capacity curve of barrier lakes and intelligent identification of particles on the surface of barrier bodies were developed, which realized the rapid acquisition of eight-factor evaluation index information in an emergency environment. The D-AHP method dealt with inconsistent weight assignment to evaluation factors by experts, which helped achieve weight quantification of eight factors. The risk assessment on 15 barrier lakes such as Tangjiashan barrier lake shows that the conclusions drawn for the risk classification method proposed in this paper are basically consistent with those of the traditional table-lookup method. However, the table-lookup method ignores cumulative loss impacts on the risk level of barrier lakes and considers the extremely severe loss of barrier lakes as a sufficient condition for the evaluation level to be grade I, and thus a deviation in the evaluation. The risk classification method proposed in this paper is more reasonable and reliable.

The risk classification of barrier lake is the key to conducting emergency treatment in a scientific manner. Such difficulties are faced as short time for risk evaluation, complex evaluation indicators, difficulty in obtaining information quickly and quantifying index weights. Based on this, this paper constructs a quantitative risk classification model for barrier lakes based on D-AHP. On the basis of studies on nearly 100 cases of barrier lakes, an 8-factor evaluation index system and quantitative classification are proposed. The methods of rapid calculation of reservoir capacity curve of barrier lake and intelligent identification of particles on the surface of barrier body were developed, which realized the rapid acquisition of 8-factor evaluation index information in emergency environment. The D-AHP method dealt with inconsistent weight assignment to evaluation factors by experts, which helped achieve weight quantification of 8 factors. The risk assessment on15 barrier lakes such as Tangjiashan barrier lake show that the conclusion drawn with risk classification method proposed in this paper is basically consistent with that of the traditional table-lookup method. However, the table-lookup method ignores cumulative loss’s impacts on the risk level of barrier lakes, and considers the extremely severe loss of barrier lakes as a sufficient condition for the evaluation level to be grade I, and thus a deviation in the evaluation. The risk classification method proposed in this paper is more reasonable and reliable.

The density of rockfill material is an important index to evaluate the quality of rockfill dams. It is of great significance to accurately obtain the densities and permeability coefficients of rockfill material dams quickly and accurately by scientific means. However, it takes a long time to measure the permeability coefficient of rockfill material in practice, which means that such measurements cannot fully reflect all the relevant properties. In this paper, using a convolutional neural network (CNN), a machine learning model was established to predict the permeability coefficient of rockfill material with the full scale (d₁₀~d₁₀₀), pore ratio, Cu, and Cc as the inputs and the permeability coefficient as the output. Through collecting the permeability coefficient and related data in the literature, the set samples were sorted for model training. The prediction results of the trained CNN model are compared with those of the back propagation (BP) model to verify the accuracy of the CNN model. Laboratory constant head penetration experiments were designed to verify the generalization performance of the model. The results show that compared with the BP model, the CNN model has better applicability to the prediction of the permeability coefficient of rockfill material and that the CNN can obtain better accuracy and meet the requirements of the rough estimation of rockfill materials’ permeability in engineering.

Barrier lakes are secondary disasters with associated landslides and debris flow that can cause serious damage to the downstream populations and areas. Existing studies are lacking in comprehensive descriptions of the rescue process, where the main channel streamflow varies and topographic erosion develops, as well as engineering disposal performs. This paper aimed to theoretically investigate the formation and emergency responses to barrier lakes using on-the-spot investigation and calculus theory. The results showed that the formation of a barrier lake led to a sudden variation in the flow-change rate (normal to infinite). However, after implementing emergency measures, this rate returned to normal. The whole rescue process could be regarded as the accumulation of disposal effects. Volume changes in the main streams were expressed by a differential equation of the lake surface area and water level variations. In addition, a corresponding theoretical description of flow discharges was also given when engineering measures such as the excavation of diversion channels and engineering blasting were adopted. Specifically, the theoretical expressions of flow discharge were given respectively in the developing stage and breach stable stage after the excavation of diversion channels. The flow discharge through certain sections was also described theoretically when engineering blasting was chosen to widen and deepen the cross-section of the diversion channels. Overall, this paper mathematicizes and theorizes the existing emergency measures, which helps to better understand their implementation principles and application requirements.