Research on blasting cumulative dynamic damage of surrounding rock in step construction tunnel.

In the process of cyclic blasting during tunnel excavation, the reserved surrounding rock sustains irreparable damage accumulation. For safe tunnel construction, it is imperative to understand the characteristics of blasting dynamic cumulative rock damage. Sonic wave test and numerical simulation methods were applied to the research. The JH-2 model was adopted as the damage model of surrounding rock. Based on the data transfer method between solvers in ABAQUS software, the cumulative damage was calculated. The damage characteristics were obtained by combining the sonic wave test results. According to the research findings, the entire reserved surrounding rock has periodic damage characteristics. Each periodic damage area has a funnel shape along the tunnel's longitudinal direction, with a length of 160 cm, and 1.07 times the excavation footage. The latter excavation footage's blasting effect on the damaged area of the previous footage rock is 40 cm long, with three cumulative damage patterns. The three cumulative damage patterns more clearly reveal the surrounding rock's additional damage law, the degree of additional damage is greatest with the distance of 5-20 cm from the latter excavation footage. The research can provide appropriate theoretical guidance for the design of the step-blasting construction tunnel's blasting scheme and lining.

Research on structural parameter optimization of elliptical bipolar linear shaped charge based on machine learning.

Numerical simulation based on SPH method, compared with laboratory experiments, using the grey correlation theory to analyze the correlation between the parameters of the elliptical bipolar linear shaped charge and the performance of the shaped charge jet. The structure of shaped charge is optimized by machine learning to obtain the optimal structural parameters, and it is compared with the rock crack development of shaped charge blasting in practical application. The results show that the structural parameters of the shaped charge have the same influence on the jet head velocity, and there are certain differences in the impact on the jet length. The fitted curve of the support vector machine (SVM) regression model based on the genetic algorithm (GA) is high prediction accurate. By comparing the optimization results with the actual engineering application of the shaped charge structure, the rock breaking effect has been significantly improved, which has important guiding significance for the actual engineering application.

A numerical study on CO migration after blasting in high-altitude tunnel by inclined shaft.

On the western plateau of China, ventilation problems brought on by low atmospheric pressure must be overcome. And CO migration after blasting in high-altitude tunnel by inclined shaft has become a significant scientific issue. In this study, the Computational Fluid Dynamics (CFD) method was used to analyze the flow field characteristics at the junction of the inclined shaft and tunnel. In addition, the effects of different fan opening modes and different initial CO concentration distributions on the ventilation were discussed. The simulation results showed that the main difference in the ventilation wind field was reflected in the position of the vortex region due to the different fan opening modes. Meanwhile, various initial CO concentration distributions showed different migration when there was no air volume difference between the left and right tunnels. Eliminating vortex zones and fully using high velocity airflow could improve relative ventilation efficiency by at least 18%. CO would accumulate in the opposite direction of the tunnel if only one of the fans was turned on. Therefore, a two-stage ventilation scheme was proposed, and the energy consumption was reduced by at least 33%. This research can provide guidance on high-altitude tunnel construction with multiple working faces to improve ventilation efficiency and reduce energy consumption.

A multi-source information fusion approach in tunnel collapse risk analysis based on improved Dempster-Shafer evidence theory.

The tunneling collapse is the main engineering hazard in the construction of the drilling-and-blasting method. The accurate assessment of the tunneling collapse risk has become a key issue in tunnel construction. As for assessing the tunneling collapse risk and providing basic risk controlling strategies, this research proposes a novel multi-source information fusion approach that combines Bayesian network (BN), cloud model (CM), support vector machine (SVM), Dempster-Shafer (D-S) evidence theory, and Monte Carlo (MC) simulation technique. Those methods (CM, BN, SVM) are used to analyze multi-source information (i.e. statistical data, physical sensors, and expert judgment provided by humans) respectively and construct basic probability assignments (BPAs) of input factors under different risk states. Then, these BPAs will be merged at the decision level to achieve an overall risk evaluation, using an improved D-S evidence theory. The MC technology is proposed to simulate the uncertainty and randomness of data. The novel approach has been successfully applied in the case of the Jinzhupa tunnel of the Pu-Yan Highway (Fujian, China). The results indicate that the developed new multi-source information fusion method is feasible for (a) Fusing multi-source information effectively from different models with a high-risk assessment accuracy of 98.1%; (b) Performing strong robustness to bias, which can achieve acceptable risk assessment accuracy even under a 20% bias; and (c) Exhibiting a more outstanding risk assessment performance (97.9% accuracy) than the single-information model (78.8% accuracy) under a high bias (20%). Since the proposed reliable risk analysis method can efficiently integrate multi-source information with conflicts, uncertainties, and bias, it provides an in-depth analysis of the tunnel collapse and the most critical risk factors, and then appropriate remedial measures can be taken at an early stage.

Dynamic risk evaluation method for collapse disasters of drill-and-blast tunnels: a case study.

The tunnel collapse is one of the most frequent and harmful geological hazards during the construction of highway rock tunnels. As for reducing the occurrence probability of tunnel collapse, a new dynamic risk assessment methodology for the tunnel collapse was established, which combines the Cloud model (CM), the Membership function, and the Bayesian network (BN). During the preparation phase, tunnel collapse risk factors are identified and an index system is constructed. Then, the proposed novel assessment method is used to evaluate the probability of tunnel collapse risk for on-site construction. The probability of tunnel collapse risk in the dynamic process of construction can provide real-time guidance for tunnel construction. Moreover, a typical case study of the Yutangxi tunnel is performed, which belongs to the Pu-Yan Highway Project (Fujian, China). The results show that the dynamic evaluation model is well validated and applied. The risk value of tunnel collapse in a construction cycle is predicted successfully, and on-site construction is guided to reduce the occurrence of tunnel collapse. Besides, it also proves the feasibility of the dynamic evaluation method and its application potential.