Nowadays, more and more subway tunnels were planed and constructed underneath the ground of urban cities to relieve the congested traffic. Potential damage may occur in existing tunnel if the new tunnel is constructed too close. So far, previous studies mainly focused on the tunnel-tunnel interactions with circular shape. The difference between circular and horseshoe shaped tunnel in terms of deformation mechanism is not fully investigated. In this study, three-dimensional numerical parametric studies were carried out to explore the effect of different tunnel shapes on the complicated tunnel-tunnel interaction problem. Parameters considered include volume loss, tunnel stiffness and relative density. It is found that the value of volume loss play the most important role in the multi-tunnel interactions. For a typical condition in this study, the maximum invert settlement and gradient along longitudinal direction of horseshoe shaped tunnel was 50% and 96% larger than those in circular case, respectively. This is because of the larger vertical soil displacement underneath existing tunnel. Due to the discontinuous hoop axial stress in horseshoe shaped tunnel, significant shear stress was mobilized around the axillary angles. This resulted in substantial bending moment at the bottom plate and side walls of horseshoe shaped tunnel. Consequently, vertical elongation and horizontal compression in circular existing tunnel were 45% and 33% smaller than those in horseshoe case (at monitored section X/D = 0), which in latter case was mainly attributed to the bending induced deflection. The radial deformation stiffness of circular tunnel is more sensitive to the Young's modulus compared with horseshoe shaped tunnel. This is because of that circular tunnel resisted the radial deformation mainly by its hoop axial stress while horseshoe shaped tunnel do so mainly by its flexural rigidity. In addition, the reduction of soil stiffness beneath the circular tunnel was larger than that in horseshoe shaped tunnel at each level of relative density, indicating that large portion of tunneling effect were undertaken by the ground itself in circular tunnel case.

The specially-shaped Polycrystalline Diamond Compact (PDC) cutter is widely used in drill bit design due to its advantages of high rock cutting efficiency, strong impact resistance and long service life in hard and abrasive formation drilling. A detailed understanding of rock cutting behavior of worn specially-shaped PDC cutter is essential to improve the drilling efficiency and decrease the drilling costs. In this paper, the theoretical models of two new principles (loading performance (LP) and cutting performance (CP)) are derived for evaluating the cutting process of worn specially-shaped cutter, the theoretical models consider the factors, such as cutter geometry, aggressiveness, stress state, working life, and rock cutting efficiency. Besides, the numerical model of heterogeneous granite is developed using finite element method combined with Voronoi tessellation, the LP and CP of 12 kinds of worn specially-shaped PDC (SPDC) cutters are analyzed. The results found that the mechanical specific energy (MSE) of worn cutters first increase and then decrease with increasing the cutting depth, and the MSE increase with the increase of back rake angle except for Conical cutter and Wedge-shaped cutter. From the perspective of CP, the worn PDC cutters are more suitable for the smaller cutting depths, and the back rake angle has little effect on the CP of the specially-shaped worn PDC cutters. Conical cutter, Saddle-shaped cutter and Ellipse-shaped cutter have the highest CP value, while Rhombus-shaped cutter, Convex cutter and Wedge-shaped cutter have the lowest value in selecting cutters. This research leads to an enhanced understanding of rock-breaking mechanisms of worn SPDC cutters, and provides the basis to select of specially-shaped PDC cutters for the specific target formation.

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.

Bio-CaCO₃ is a blowout environment-friendly materials for soil improvement and sealing of rock fissures. To evaluate the chemical characteristics, shape, size and productivity of soybean urease induced CaCO₃ precipitates (SUICP), experimental studies were conducted via EDS, XRD, FT-IR, TGA, BET, and SEM. Also, the conversion rate of SUICP reaction at different time were determined and analyzed. The Bio-CaCO₃ product obtained by SUICP is comprehensively judged as calcite based on the results of EDS, XRD and FT-IR. The SUICP calcite precipitates are detected as spherical or ellipsoidal particles 3-6 ㎛ in diameter with nanoscale pores on their surface, and this morphology is novel. The median secondary particle size d₅₀ is 39-88 ㎛, indicating the agglomeration of the primary calcite particles. The Bio-calcite decomposes at 650-780℃, representing a medium thermal stability. The conversion rate of SUICP reaction can reach 80% in 24h, which is much more efficient than microbially induced CaCO₃ precipitation. These results reveal the knowledges of SUICP, and further direct its engineering applications. Moreover, we show an economic channel to obtain porous spherical calcite.

Maximum shear modulus (G_max or G₀) is an important soil property useful for many engineering applications, such as the analysis of soil-structure interactions, soil stability, liquefaction evaluation, ground deformation and performance of seismic design. In the current study, bender element (BE) tests are used to evaluate the effect of the void ratio, effective confining pressure, grading characteristics (D₅₀, C_u and C_c), anisotropic consolidation and initial fabric anisotropy produced during specimen preparation on the G_max of sand-gravel mixtures. Based on the tests results, an empirical equation is proposed to predict G_max in granular soils, evaluated by the experimental data. The artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models were also applied. Coefficient of determination (R²) and Root Mean Square Error (RMSE) between predicted and measured values of G_max were calculated for the empirical equation, ANN and ANFIS. The results indicate that all methods accuracy is high; however, ANFIS achieves the highest accuracy amongst the presented methods.

Permeable reactive barriers used for groundwater treatment require proper estimation of the reactive material behavior regarding the emplacement method. This study evaluates the dry emplacement of zeolite (clinoptilolite) to be used as a reactive material in the barrier by carrying out several geotechnical laboratory tests. Dry zeolite samples, exhibited higher wetting-induced compression strains at the higher vertical stresses, up to 12% at 400 kN/m². The swelling potential was observed to be limited with a 3.5 swell index and less than 1% free swelling strain. Direct shear tests revealed that inundation reduces the shear strength of a dry zeolite column by a maximum of 10%. Falling head permeability tests indicate decreasing permeability values with increasing the vertical effective stress. Regarding self-loading and inundation, the porosity along the zeolite column was calculated using a proposed 1D numerical model to predict the permeability with depth considering the laboratory tests. The calculated discharge efficiency was significantly decreased with depth and less than 2% relative to the top for barrier depths deeper than 20 m. Finally, the importance of directional dependence in the permeability of the zeolite medium for calibrating 2D finite element flow analysis was highlighted by bench-scale tests performed under 2D flow conditions.

Ensuring the integrity of a country's infrastructure is necessary to protect surrounding communities in case of disaster. Embankment dam systems across the US are an essential component of infrastructure, referred to as lifeline structures. Embankment dams are crucial to the survival of life and if these structures were to fail, it is imperative that states be prepared. Southern California is particularly concerned with the stability of embankment dams due to the frequent seismic activity that occurs in the state. The purpose of this study was to create a numerical model of an existing embankment dam simulated under seismic loads using previously recorded data. The embankment dam that was studied in Los Angeles, California was outfitted with accelerometers provided by the California Strong Motion Instrumentation Program that have recorded strong motion data for decades and was processed by the Center for Engineering Strong Motion Data to be used in future engineering applications. The accelerometer data was then used to verify the numerical model that was created using finite element modeling software RS2. The results from this study showed Puddingstone Dam's simulated response was consistent with that experienced during previous earthquakes and therefore validated the predicted behavior from the numerical model. The study also identified areas of weakness and instability on the dam that posed the greatest risk for its failure. Following this study, the numerical model can now be used to predict the dam's response to future earthquakes, develop plans for its remediation, and for emergency response in case of disaster.

In this paper, the thermal post-buckling characteristics of functionally graded (FG) pipes with initial geometric imperfection are studied. Considering the influence of initial geometric defects, temperature and geometric nonlinearity, Euler-Lagrange principle is used to derive the nonlinear governing equations of the FG pipes. Considering three different boundary conditions, the two-step perturbation method is used to solve the nonlinear governing equations, and the expressions of thermal post-buckling responses are also obtained. Finally, the correctness of this paper is verified by numerical analyses, and the effects of initial geometric defects, functional graded index, elastic foundation, porosity, thickness of pipe and boundary conditions on thermal post-buckling response are analyzed. It is found that, bifurcation buckling exists for the pipes without initial geometric imperfection. In contrast, there is no bifurcation buckling phenomenon for the pipes with initial geometric imperfection. Meanwhile, the elastic stiffness can significantly improve thermal post-buckling load and thermal post-buckling strength. The larger the porosity, the greater the thermal buckling load and the thermal buckling strength.