Prediction and Evaluation of Rockburst Based on Depth Neural Network

The formation mechanism of rockburst is complex, and its prediction has always been a difficult problem in engineering. According to the tunnel engineering data, a three-dimensional discrete element numerical model is established to analyze the initial stress characteristics of the tunnel. A neural network model for rockburst prediction is established. Uniaxial compressive strength, uniaxial tensile strength, maximum principal stress, and rock elastic energy are selected as input parameters for rockburst prediction. Training through existing data. The neural network model shows that the rockburst risk is closely related to the maximum principal stress. Based on the division of rockburst risk areas, according to different rockburst levels, the corresponding treatment methods are put forward to avoid the occurrence of rockburst disaster. Based on the field measured data and test data, combined with the existing rockburst situation, numerical simulation and neural network method are used to predict the rock burst classification, which is of great significance for the early and late construction safety of the tunnel.

Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study

Rockburst is a complex phenomenon of dynamic instability in the underground excavation of rock. Owing to the complex and unclear rockburst mechanism, it is difficult to accurately predict and reasonably assess the rockburst potential. With the increasing availability of case histories from rock engineering and the advancement of data science, the data mining algorithms provide a good way to predict complex phenomena, like rockburst potential. This paper investigates the potential of J48 and random tree algorithms to predict the rockburst classification ranks using 165 cases, with four parameters, namely maximum tangential stress of surrounding rock, uniaxial compressive strength, uniaxial tensile strength, and strain energy storage index. A comparison of developed models’ performances reveals that the random tree gives more reliable predictions than J48 and other empirical models (Russenes criterion, rock brittleness coefficient criterion, and artificial neural networks). Similar comparisons with convolutional neural network resulted at par performance in modeling the rockburst hazard data.

Evaluating the Effects of Textural Properties on the Strength Parameters of Marbles from North-Western Khyber Pakhtunkhwa, Pakistan

Marble is globally used as a natural stone for decorative and architectural purposes. Primary utilization of marble is as building and dimension stones. Mechanical properties and aesthetic aspects are major characteristics of marble and decisive factors for its selection and utilization. It is therefore imperative to evaluate the key strength properties i.e. Uniaxial Compressive Strength (UCS) and Uniaxial Tensile Strength (UTS) of marble before its utilization. These key strength parameters are dependent on textural features of marble. Present study investigates the effect of two key textural features i.e. grain size and grain shape on two key strength parameters i.e. UCS and UTS of marble samples taken from three different regions i.e. Buner, Chitral and Swat in the north western part of Khyber Pakhtunkhwa. Correlation and regression analysis between these textural properties and strength parameters revealed that prominent textural features of grain size and shape can be used as a quick indicator for assessment of strength parameters and as guideline for appropriate utilization of marble.

Experimental Investigation into the Influences of Weathering on the Mechanical Properties of Sedimentary Rocks

The time-dependent behaviors of the sedimentary rocks which refer to the altering of the mechanical and deformable properties of rock elements in the long-term period are of increasing importance in the investigation of the failure mechanism of the rock strata in underground coal mines. In order to obtain the accurate and reliable mechanical parameters of the sedimentary rocks at different weathering grades, the extensive experimental programs including the Brazilian splitting test, uniaxial compression tests, and direct shear tests have been carried out on the specimens that exposed to the nature environments at different durations. The correlation between the weathering grades and mechanical parameters including uniaxial tensile strength, uniaxial compression strength, elastic modulus, Poisson’s ratio, cohesion, and friction coefficient was proposed. The obtained results suggested that uniaxial tensile strength, uniaxial compressive strength, elastic modulus, and cohesion dramatically decreased with increasing weathering time, characterized as the negative exponential relationship in general. The influences of various weathering grades on fracture behavior of the rock specimens were discussed. The cumulative damage of the rock by the weathering time decreased the friction coefficient of the specimens which led to the initiation and propagation of microcrack within the rock at lower stress conditions. The obtained results improved the understanding of the roles of weathering on the mechanical properties of sedimentary rocks, which is helpful in the design of the underground geotechnical structures.