Search

The stability classification of loess deposits around tunnels is a vital prerequisite for safe construction in underground environment. Due to the fuzziness and randomness of loess physical and mechanical parameters, the stability prediction of loess deposits shows uncertainty. Existing loess deposit stability classification models rarely consider the uncertainty of influencing factors. A novel classification probability model of loess deposits is proposed for the above problems based on Monte Carlo simulation and multi-dimensional normal cloud (MCS-Cloud). Specifically, five loess parameters, including water content, cohesion, internal friction angle, elastic modulus, and Poisson ratio, were selected as predictors for the stability level of loess deposits. The weights of the predictors were obtained through 50 test samples. After acquiring the numerical characteristics of the normal cloud, the stability level can be comprehensively evaluated with the weighted multi-dimensional normal cloud model. The classification model was applied to the loess tunnel in Yan'an, China. The prediction results are in good agreement with practical engineering, denoting the rationality of the weighted multi-dimensional normal cloud. Finally, the stability classification of loess deposits was discussed from the perspective of uncertainty analysis with the application of MCS. Results proved that the MCS-Cloud model is feasible for classifying the stability of loess deposits surrounding tunnels. The obtained classification probability can be used for quantitative risk assessment of loess tunnels. [ABSTRACT FROM AUTHOR]

This paper presents a case study of a large cross-section high-speed railway tunnel overlaid by a Q2 soft-plastic loess layer. Due to poor engineering properties of the soft-plastic loess, two unexpected collapses occurred. This indicates that there is great difficulty and risk during tunneling. In this paper, the deformation control methods are studied by carrying out a series of numerical simulations and then validating via field tests. The stress and seepage coupling equations are deducted to embed into a 3D fluid-mechanical coupled numerical analysis. The results show that the curtain grouting scheme can effectively control the deformation of surrounding rock, and the real field data of settlement and horizontal convergence are 152 mm and 37 mm, respectively. However, the tunneling advance rate is slow (0.6 m/day) in trial sections because of the mutual interference between construction procedures. It can be used as an alternative scheme for lager deformation. The advanced drainage scheme can lower the ground water table below the excavation outline. The average water content of soft-plastic loess decreases from 26 to 22%, and the state of surrounding rock changes from soft plastic to hard plastic. The in situ settlement and horizontal convergence are 165 mm and 51 mm, respectively. Finally, it is proposed to use advanced drainage in combination with advanced small pipe grouting as a follow-up construction scheme. The successful completion of the whole tunnel proved the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The total deformation of loess tunnels is an essential basis for determining reasonable support timing, optimizing support parameters, and predicting collapses. Accurately predicting the total deformation is of considerable significance to the safe design and construction of loess tunnels. In this paper, a representative data sample set of the total deformation in loess tunnels was obtained using numerical simulations, and a back-propagation neural network (BPNN) was adopted. The input parameters were the bulk density, water content, elastic modulus, Poisson's ratio, cohesion, internal friction angle of the loess, tunnel depth, and primary support strength. The output parameters were the total vertical and horizontal convergence deformation of the loess tunnel. The prediction results showed that the BPNN model could effectively predict the total deformation of a typical loess tunnel. Finally, the sensitivity analysis of the impact factors showed that the primary support strength and internal friction angle were parameters affecting the total deformation the most and least, respectively. This study can provide targeted guidance for the safe construction of loess tunnels. [ABSTRACT FROM AUTHOR]