Search

The post-failure instability of rocks was investigated through an extensive experimental study under four different loading histories, including the monotonic quasi-static loading, single-level systematic cyclic loading (SLSCL), multi-level systematic cyclic loading (MLSCL), and post-peak cyclic loading (PPCL). The lateral strain-controlled and double-criteria damage-controlled testing methodologies were implemented for the experiment. A combined postpeak Class I-II behavior to different extents was detected for soft to strong rocks, while the unstable fracture propagation was more dominant for stronger rocks under monotonic loading. Additionally, rocks exhibited more self-sustaining behavior under MLSCL history with increasing the number of cycles before the failure point. On the other hand, the results of the SLSCL tests revealed that rock brittleness reaches its maximum value by applying systematic cyclic loadings at stress levels close to the monotonic strength. However, the effect of post-peak cyclic loading (PPCL) history on the post-failure response of rocks was negligible.

The seismic events are contaminated with the blast/noise signals in microseismic monitoring of the underground excavations, negatively affecting the interpretation and detection of high-stress zones. This study proposes explicit and comprehensible classifiers by hybridizing the principal component analysis (PCA) with genetic programming (GP) and classification and regression tree (CART) algorithms. Six discriminant parameters representing the spectrum and source characteristics of the signals were used as input variables. PCA reduced the problem's dimensionality to two components, which were then fed into GP and CART algorithms as the new input variables. A systematic hyperparameter tuning procedure was employed to find the optimum values of the controlling parameters of the algorithms. The hybrid PCA-GP and PCA-CART classifiers provided practical mathematical equations and tree structures, respectively, capable of distinguishing between the signal types with high accuracy. However, the PCA-GP model outperformed the PCA-CART model based on the performance indices.

Understanding the dynamic behavior of soil is crucial for developing effective mitigation strategies for natural hazards such as earthquakes, landslides, and soil liquefaction, which can cause significant damage and loss of life. The ultrasonic wave testing method provides a non-invasive and reliable way of measuring the shear modulus, damping ratio and density of soils, which are fundamental parameters for understanding soil’s dynamic characteristics. The aim of this study was to investigate the effects of environmental factors, such as water salinity, soil liquid limit, plasticity index, dry density, and water content, on ultrasonic wave velocities (specifically shear and primary waves) in kaolin–sand mixtures subjected to near-zero vertical stress, as well as to predict these effects utilizing two unique artificial intelligence methods, including Classification and Regression Random Forests (CRRF) and Artificial Neural Networks (ANN), which, to our knowledge, have not been utilized in previous literature. The CRRF and ANN models were developed using two well-known algorithms and five different architectures using a database of 128 datasets. Water salinity, dry density, water content, liquid limit and plasticity index were predictor variables. The results showed that both CRRF and ANN were highly accurate. The coefficient of determination (R2) and mean absolute error (MAE) of the best CRRF were 0.963 and 9.191, respectively to predict Vs, and 0.974 and 7.809 to predict Vp, respectively. Furthermore, in ANN, R2 and MAE were respectively 0.994 and 0.016 to predict both Vs and Vp. According to importance analysis, liquid limit, molality, and dry density are the most critical parameters, while water content is the least critical.

Iran is one of the countries with the largest number of quarry mines in the world. Diamond cutting wire is usually used in quarries to cut dimension stone cubes, which is accompanied by hazardous events. Therefore, detecting and investigating the possible quarry risks is crucial to have a safe and sustainable mining operation. In mine exploitation, maintaining the safety of vehicles and increasing the knowledge of personnel regarding safety issues can considerably mitigate the number or radius of effect of hazards. Hence, the incidents and risks in the West-Azerbaijan quarries in Iran are investigated in this work. To do so, a list of the hazards and their descriptions are first prepared. Then the hazard risk rating is conducted using the Failure Modes and Effects Analysis (FMEA) method. The number of priorities is calculated for each incident based on probability, intensity, and risk detection probability. Finally, the main causes of risks in the studies quarries are identified. The results obtained show that the most likely dangers in dimensional stone mines in West Azerbaijan are diamond cutting wire breaking, rock-fall, and car accidents, with the priority numbers of 216, 180, and 135, respectively. These hazards can be mitigated by applying some preservative activities such as timely cutting wire replacement, utilizing an intelligent system for cutting tool control, necessary personal training, and considering some preservative points.