With the increasing depth of coal mining each year, rock burst has emerged as one of the most severe dynamic disasters in deep mining. The research status of rock burst prevention and control theory is summarized. Focused on deep coal mining, the major issues encountered in researching the prevention theory of rock bursts are summarized. Subsequently, the scientific connotation theory of stress relief-support reinforcement cooperative prevention and control of rock bursts in deep coal mines is proposed. Then, the mechanisms underlying the major research directions of the theory of stress relief-support reinforcement coordinated prevention and control and present a preliminarily theoretical framework for stress relief-support reinforcement coordinated prevention and control are outlined. To tackle the key scientific problems in the coordinated prevention and control of rock bursts on relief and support in deep mine, the in-depth research based on the synergetic theory is conducted. This involved exploring the principles of near-field coal mass stress relief, near-field roof and floor stress relief, and anchor support. Additionally, the stress-energy evolution processes of the roadway near-field surrounding rock structure under various stress relief and anchor support modes be analyzed. Subsequently, a mechanical model for the optimized matching of stress relief surrounding rock and anchor support is established, with the control of the rock burst energy source at its core. Finally, the principle of collaborative prevention and control of deep mining rock burst stress relief and support from the perspectives of structural synergy, strength synergy, and stiffness synergy is elucidated. This insight is expected to provide theoretical support for the research and development of designs and techniques for deep mining rock burst prevention and control.

As a widely used rock excavation method in civil and mining construction works, the blasting operations and the induced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one of the most important issues induced by blasting operations, since the accurate prediction of which is crucial for delineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets of blasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stacked MK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve the model performance by addressing the importance level of different features. For comparison purpose, 6 other machine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), Artificial Neural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validation process for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVM model achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95 and 99.25 in training and testing phase, respectively.

Water leakage inspection in the tunnels is a critical engineering job that has attracted increasing concerns. Leakage area detection via manual inspection techniques is time-consuming and might produce unreliable findings, so that automated techniques should be created to increase reliability and efficiency. Pre-trained foundational segmentation models for large datasets have attracted great interests recently. This paper pro-poses a novel SAM-based network for accurate automated water leakage inspection. The contributions of this paper include the efficient adaptation of the SAM (Segment Anything Model) for shield tunnel water leakage segmentation and the demonstration of the application effect by data experiments. Tunnel SAM Adapter has satisfactory performance, achieving 76.2 % mIoU and 77.5 % Dice. Experimental results demonstrate that our approach has advantages over peer studies and guarantees the integrity and safety of these vital assets while streamlining tunnel maintenance.

Aiming at the deformation control problem of the tunnel entrance crossing the spoil heap at the Xialao junction, this paper adopts the micropile combined with the coupling beams method to treat the spoiled layers. The results show that the excavation of the tunnel after the construction of the micropile and coupling beam will cause vertical deformation of the tunnel and the slope surface. The main reason is that the soil layer structure is loose, and the tunnel excavation causes the whole displacement of the loose body. In addition, the buried depth of the tunnel is shallow, so it cannot form an effective soil arch. The stability process after the construction of the micropile method is the process of stress redistribution, and the rock and soil are gradually compressed and compacted. That is, the construction by the micropile method changes the surrounding rock level of the tunnel and reduces the height of the soil arch. Therefore, it is suggested that the tunnel excavation should be carried out when the micropile is constructed after the soil layers are consolidated completely. The micropile method treats the loose spoiled soil at the tunnel entrance, which saves 73% of the total cost compared with the scheme of directly digging out the accumulation, and the economic benefit is very obvious.

China is a mountainous country with highly developed road geologic hazards, which pose a great threat to the construction and operation of highways, bridges, and tunnels and to the safety of people and property. This paper discussed the types, basic features, formation, and prevention conditions of road geologic hazards in China based on field research and study data collected thus far. The study considered an urban area of a city in southwest China as the center and a geological field investigation was performed over a total of 282 km on three important lifeline projects. The results show: Types of geologic hazards along the highways are mainly avalanches, debris flows, and landslides, respectively. Among them, the landslips are mainly distributed along the roads, with slip, dumping, and wrong break types as the main ones; the debris flows are widely distributed, mainly concentrated in the river valleys; and the unstable slopes are relatively few in number. Geological disasters are characterized by large-scale and concentrated triggering in time and space, and a single disaster can easily trigger other disasters, thus forming a chain of disasters. Neotectonic movement, seismic activity, topography, climatic conditions, stratigraphic lithology, and human activities are important factors leading to geologic hazards in the study area. This study is of great practical significance for reducing the occurrence of roadbed diseases and prolonging the service life of highways.

Application of Artificial Intelligence (AI) in tunnel construction has the potential to transform the industry by improving efficiency, safety, and cost-effectiveness. This paper presents a comprehensive literature review and analysis of hotspots and frontier topics in artificial intelligence-related research in tunnel construction. A total of 554 articles published between 2011 and 2023 were collected from the Web of Science (WOS) core collection database and analyzed using CiteSpace software. The analysis identified three main study areas: Tunnel Boring Machine (TBM) performance, construction optimization, and rock and soil mechanics. The review highlights the advancements made in each area, focusing on design and operation, performance prediction models, and fault detection in TBM performance; computer vision and image processing, neural network algorithms, and optimi-zation and decision-making in construction optimization; and geo-properties and behaviours, tunnel stability and excavation, and risk assessment and safety management in rock and soil mechanics. The paper concludes by discussing future research directions, emphasizing the integration of AI with other advanced technologies, real-time decision-making systems, and the management of environmental impacts in tunnel construction. This comprehensive review provides valuable insights into the current state of AI research in tunnel engineering and serves as a reference for future studies in this rapidly evolving field.