Flexural and compressive strength of the landfast sea ice in the Prydz Bay, East Antarctic

A total of 25 flexural and 55 uniaxial compressive strength tests were conducted using landfast sea ice samples collected in the Prydz Bay. Three-point bending tests were performed at ice temperatures of −12 to −3 °C with force applied vertically to original ice surface, and compressive tests were performed at −3 °C with a strain-rate level of 10−6–10−2 s−1 in the directions vertical and horizontal to ice surface. Judging from crystal structure, the ice samples were divided into congelation ice, snow ice, and a mixture of the these two. The results of congelation ice showed that the flexural strength had a decreasing trend depending on porosity rather than brine volume, based on which a mathematical equation was established to estimate flexural strength. Both flexural strength and effective modulus increased with increasing platelet spacing. The uniaxial compressive strength increased and decreased with strain rate below and above the critical regime, respectively, which is 8.0 × 10−4–1.5 × 10−3 s−1 for vertically loaded samples and 2.0 × 10−3–3.0 × 10−3 s−1 for horizontally loaded samples. A drop off in compressive strength was shown with increasing sea ice porosity. Consequently, a model was developed to depict the combined effects of porosity and strain rate on compressive strength in both ductile and brittle regimes. The mechanical strength of mixed ice was lower than congelation ice, and that of snow ice was much weaker. To provide a safe guide for the transportation of goods on landfast sea ice in the Prydz Bay, the bearing capacity of the ice cover is estimated with the lower and upper envelopes of flexural strength and effective modulus, respectively, which turned out to be a function of sea ice porosity.

Size Effect of the Number of Parallel Joints on Uniaxial Compressive Strength and Characteristic Strength

The number of parallel joints has an impact on the size effect of the uniaxial compressive strength and characteristic strength of a rock; however, the relationships between them are yet to be derived. We studied the influence of the number of joints and rock size on the uniaxial compressive strength of the rock. This study established ten numerical simulation programs using numerical simulations and the RFPA software. Stress–strain curves of different numbers of parallel joints and sizes of rocks were analyzed. Relationships between the uniaxial compressive strength and number of parallel joints and rock size were proposed, and their special functions were obtained. Mathematical models between rock characteristic size, rock characteristic strength and the number of parallel joints were established. Simulations of the verification program confirmed that these relationships are still applicable after the angle of parallel joints changes.

Predictive Modeling of Uniaxial Compressive Strength of Rocks for Protecting Environment Using Artificial Neural Network

Sedimentary rocks provide information on previous environments on the surface of the earth. As a result, they are the principal narrators of former climate, life, and important events on the surface of the earth. Complexity and expensiveness of direct destructive laboratory tests are adversely affects the data scarcity problem, making the development of intelligent indirect methods an integral step in attempts to address the problem faced by rock engineering projects. This study established artificial neural network (ANN) approach to predict uniaxial compressive strength (UCS) in MPa of soft sedimentary rocks using different input parameters i.e. dry density (ρd) in g/cm3; Brazilian tensile strength (BTS) in MPa; point load index (Is(50)) in MPa. The developed ANN models M1, M2 and M3 were divided into the overall dataset; 70% training dataset and 30% testing dataset; and 60% training dataset and 40% testing dataset respectively. In addition, multiple linear regression (MLR) was performed to compare with the proposed ANN models to verify the accuracy of the predicted values. The performance indices were also calculated by estimating the established models. The predictive performance of the M3 ANN model with the highest coefficient of correlation (R2), the smallest root mean squared error (RMSE), the highest variance accounts for (VAF) and reliable a10-index was 0.99, 0.00060, 0.99 and 0.99 respectively at the testing dataset revealing ideal results and proposed as the best-fit prediction model for UCS of soft sedimentary rocks at the Thar Coalfield, Pakistan, among other developed models in this study. Moreover, by performing sensitivity analysis, it was determined that the BTS and Is(50) were the most influential parameters in predicting UCS.

A new method for roadheader pick arrangement based on meshing pick spatial position and rock cutting verification

This paper proposes a cutting head optimization method based on meshing the spatial position of the picks. According to the expanded shape of the spatial mesh composed of four adjacent picks on the plane, a standard mesh shape analysis method can be established with mesh skewness, mesh symmetry, and mesh area ratio as the indicators. The traversal algorithm is used to calculate the theoretical meshing rate, pick rotation coefficient, and the variation of cutting load for the longitudinal cutting head with 2, 3, and 4 helices. The results show that the 3-helix longitudinal cutting head has better performance. By using the traversal result with maximum theoretical meshing rate as the design parameter, the longitudinal cutting head CH51 with 51 picks was designed and analyzed. The prediction model of pick consumption is established based on cutting speed, direct rock cutting volume of each pick, pick rotation coefficient, uniaxial compressive strength, and CERCHAR abrasivity index. And the rock with normal distribution characteristics of Uniaxial Compressive Strength is used for the specific energy calculating. The artificial rock wall cutting test results show that the reduction in height loss suppresses the increase in pick equivalent loss caused by the increase in mass loss, and the pick consumption in this test is only 0.037–0.054 picks/m3. In addition, the correlation between the actual pick consumption and the prediction model, and the correlation between the actual cutting specific energy and the theoretical calculation value are also analyzed. The research results show that the pick arrangement design method based on meshing pick tip spatial position can effectively reduce pick consumption and improve the rock cutting performance.

Variations on Reservoir Parameters of Oil Shale Deposits under Periodic Freeze-Thaw Cycles: Laboratory Tests

As one of the most important unconventional hydrocarbon resources, the oil shale has been extracted with a frozen wall to successfully increase the shale oil production and reduce environmental pollution, which results from the harmful liquids in the in situ conversion processing of oil shale. Thereby, the strength and permeability of the frozen wall are extremely critical to reduce the harmful chemicals leaching into the groundwater. However, the permeability and strength of the frozen wall can be influenced by periodic freeze-thaw cycles. In order to investigate the damage and deterioration characteristics of oil shale samples after various periodic freeze-thaw cycles, the oil shale samples were periodically frozen and thawed as many as 48 times, after which the sample mass, stress-strain, freeze-thaw coefficient, uniaxial compressive strength, elastic modulus, and longitudinal wave velocity of the oil shale samples were separately measured. According to the measured results, the number of freeze-thaw cycles greatly influenced the physical and mechanical properties of oil shale samples. The uniaxial compressive strength and elastic modulus of the oil shale samples were changed with maximum variation rates of 64% and 65%, respectively. Meanwhile, the freeze-thaw coefficient of measured oil shale samples exponentially decreased with the increased number of freeze-thaw cycles, whereas the longitudinal wave velocity of tested samples ranged from 1602 m/s to 2464 m/s as a result of the new micropores inside the oil shale sample. Research results have enormous significance to the efficient and safe in situ exploitation of oil shale deposits.