Failure response of rocks under different cyclic loading histories

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

Risk Assessment in Quarries using Failure Modes and Effects Analysis Method (Case study: West-Azerbaijan Mines)

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

ISTRAŽIVANJE UTJECAJA TEKUĆINA ZA HLAĐENJE/PODMAZIVANJE NA VELIČINU STRUJE REZNIH STROJEVA S DISKOM ZA TVRDE STIJENE

One of the most crucial steps in producing dimension rocks is the rock cutting process, which incurs a high cost. The amperage draw of rock cutting machines is a major cost factor of this production process. Determining the effect of factors, such as the machine’s operating configurations, mechanical and physical characteristics of the rock, and type of cooling/lubricant fluid, on the cutting machine’s performance can significantly reduce operational costs. This study evaluates the electrical current consumption of a disc cutting machine during the cutting of hard rocks for producing dimension rocks under different operating conditions and using different fluids for cooling/lubrication. For this purpose, a number of cutting tests were performed under different operating conditions (cutting depths of 0.5, 0.7, 1, and 1.3 cm and feed rates of 45, 60, 75, and 90 cm/min) with five cooling/lubrication fluids, including tap water, soap water with a ratio of 1:40 and 1:20, and a commercial cutting power (Abtarash) with a ratio of 30:10 and 15:10. After examining the relationship between operating parameters and the amperage draw of the cutting machine in the presence of five fluids, several linear and nonlinear multivariate statistical models were developed to predict the amperage draw of the cutting machine. The developed models were evaluated using the t-test and F-test statistical methods. The results showed that using the developed models, the amperage draw of the cutting machine can be accurately predicted from the properties of the cooling/lubrication fluid, including viscosity and pH.Jedan od najvažnijih koraka u obradi arhitektonsko-građevnoga kamena jest proces rezanja, koji uzrokuje visoku cijenu proizvodnje. Veličina električne struje kod strojeva za rezanje glavni je faktor troškova ovoga proizvodnog procesa. Određivanje radnih čimbenika, kao što su radne konfiguracije stroja, mehaničke i fizičke karakteristike stijene te vrsta tekućine za hlađenje/podmazivanje, na performanse stroja za rezanje može znatno smanjiti operativne troškove. Ovo istraživanje procijenilo je potrošnju električne struje reznoga stroja s diskom tijekom rezanja tvrdih stijena pri obradi arhitektonsko-građevnoga kamena u različitim radnim uvjetima i pri korištenju različitih tekućina za hlađenje/podmazivanje. Proveden je niz ispitivanja rezanja u različitim radnim uvjetima (dubine rezanja od 0,5, 0,7, 1 i 1,3 cm te brzine rezanje od 45, 60, 75 i 90 cm/min) s pet tekućina za hlađenje/podmazivanje, uključujući vodu iz slavine, sapunicu omjera 1 : 40 i 1 : 20 te komercijalni prah za rezanje (Abtarash) u omjeru 30 : 10 i 15 : 10. Nakon ispitivanja odnosa između radnih parametara i veličine struje reznoga stroja uz upotrebu pet tekućina razvijeno je nekoliko linearnih i nelinearnih multivarijantnih statističkih modela kako bi se predvidjela veličina struje reznoga stroja. Razvijeni modeli procijenjeni su statističkim metodama t-testa i F-testa. Rezultati su pokazali kako se pomoću razvijenih modela može točno procijeniti veličina struje stroja za rezanje iz svojstava tekućine za hlađenje/podmazivanje, uključujući viskoznost i PH

Prediction and Control of Rock Burst Phenomenon in Deep Underground Mining Based on Rock Behaviour

By depletion of minerals at shallow depths, there is a notable growing trend towards mining operations in deeper grounds whole the world. However, as the depth of mining and underground constructions increases, the occurrence of stress-induced failure processes, such as rockburst, both inside the rock masses, away from the mined-out areas, and near excavations is inevitable. Rockburst is defined as the sudden and violent failure of a large volume of overstressed rock, which can damage structures and workers, and considerably affect the economic viability of the projects. The propensity of rocks to bursting behaviour can be aggravated by the seismic disturbances induced by different sources in deep underground openings. Therefore, the in-depth understanding of the rockburst mechanism and its prediction and treatment is of paramount significance. Due to the high-complex and non-linear nature of this hazard and the vague relationship between its influential parameters, the common conventional criteria available in the literature, cannot predict rockburst occurrence and its risk level with sufficient accuracy. However, the machine learning (ML) algorithms, which benefit from an inherent intelligence procedure, can be utilised to overcome this problem. During the last decade, significant progress has been made in implementing ML techniques to predict the propensity of rocks to bursting behaviour; however, the proposed models have complex internal structure and are difficult to use in practice. On the other hand, the experimental studies in this field are limited to measuring the bursting intensity of rocks under true-triaxial loading/unloading conditions. However, the complete stress-strain relation of rocks (i.e. the pre-peak and the post-peak regimes) subjected to different cyclic loading histories can open new insights into the rockburst/brittle failure mechanism and the long-term stability of the underground structures. The common load control techniques (i.e. the axial load-controlled and displacement-controlled techniques) cannot be employed directly to conduct the systematic cyclic loading tests and capture the failure behaviour of rocks, specifically for rocks showing class II/self-sustaining behaviour in the post-peak regime. Therefore, most current rock fatigue studies have focused on characterising the evolution of mechanical rock properties and damage parameters in the pre-peak regime. Given the above, the main focus of this thesis was on developing practical and accurate models to predict rockburst-related parameters as well as better understanding the effect of seismic disturbances on the failure mechanism of rocks using data-driven and experimental approaches. The robust ML algorithms, such as gene expression programming (GEP), GEP-based logistic regression (GEP-LR), classification and regression tree (CART) etc., were programmed and employed for the following tasks: (a) Providing a mathematical binary model to estimate the occurrence/non-occurrence of rockburst hazard; (b) developing a model to cluster the rockburst events based on their risk levels; (c) proposing a novel and practical multi-class classifier to distinguish three most common failure mechanisms of squeezing, slabbing and rockburst in underground mines based on intact rock properties; (d) quantifying the rockburst maximum stress (i.e. the stress level that bursting occurs) and bursting risk level based on the comprehensive database compiled from the true-triaxial unloading tests for different rock types and (e) predicting the peak strength variation of rocks subjected to cyclic loading histories. The obtained results from the above studies proved the high performance and capability of the used ML techniques in dealing with high-complex problems in mining projects, such as rockburst hazards. The newly proposed models in this research project outperformed the conventional rockburst criteria in terms of prediction accuracy and can be used efficiently in underground mining projects. A new testing methodology namely “Double-Criteria Damage-Controlled Test Method” was developed in this research project to measure the complete stress-strain relation of rocks under different cyclic loading histories. This methodology, unlike the common testing methods, benefits from two controlling criteria, including the maximum stress level that can be achieved and the maximum lateral strain amplitude that the specimen is allowed to experience in a cycle during loading. The conducted uniaxial multi-level systematic cyclic loading tests on Tuffeau limestone proved the capability of this testing method in capturing the post-failure behaviour of rocks. The preliminary results also showed that rocks tend to behave more brittle by experiencing more cycles. Furthermore, a quasi-elastic behaviour dominated over the pre-peak regime during cyclic loading, which finally, resulted in strength hardening. In another comprehensive experimental study, 23 uniaxial single-level systematic cyclic loading tests were undertaken on Gosford sandstone specimens at different stress levels to unveil the failure mechanism of rocks subjected to seismic events. It was found that there exists a fatigue threshold (FTS) that lies between 86-87.5% so that below this threshold, no macroscopic damage is created in the specimen; rather, strength hardening induced by rock compaction occurs. Moreover, according to the evolution of damage parameters and brittleness index, the pre-peak and post-peak behaviour of rocks below the FTS was found to be independent of the cycle number. However, for the cyclic tests beyond the FTS, the instability of rocks increased with the applied stress level, representing the propensity of rocks to brittle failures like rockburst. To better replicate the rock stress conditions in deep underground mines and understand more about the evolution of some specific rock fatigue characteristics, such as strength hardening, FTS and post-peak instability with confining pressure, a comprehensive cyclic loading study was carried out on Gosford sandstone in triaxial loading conditions under seven confinement levels (σ3/UCSavg). It was found that by an increase in σ3/UCSavg from 10% to 100%, FTS decreases from 97% to 80%. An unconventional trend was observed for the stress-strain relations of rocks by varying σ3/UCSavg. A transition brittle to the ductile point was identified at σ3/UCSavg= 65%. Therefore, it can be inferred that with an increase in depth in rock engineering projects, the propensity of rock structures to brittle failures such as rock bursting at stress levels lower than the determined average peak strength can be aggravated. Also, it was observed that below the transition point, cyclic loading has a negligible effect on rock brittleness; while for σ3/UCSavg=80% and 100%, the weakening effect of cyclic loading history was visible. According to the results of acoustic emission (AE), tangent Young’s modulus (Etan), cumulative irreversible axial strain (ωairr) and axial strain at failure point (εaf), it was found that for the hardening cyclic loading tests (with positive peak strength variation), the quasi-elastic behaviour was dominant during the pre-peak rock deformation. However, for the weakening cyclic loading tests (with negative peak strength variation), more plastic strains were accumulated within the rock specimens, which resulted in gradual damage evolution and stiffness degradation during cyclic loading before applying final monotonic loading. The peak deviator stress of Gosford sandstone under different confining pressures varied between -13.18% and 7.82%. An empirical model was developed using the CART algorithm as a function of confining pressure and the applied stress level. This model is helpful in predict peak strength variations of Gosford sandstone.Thesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 202

Practical Models To Distinguish Between Seismic Events And Blast Signals

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

AI grey box model for alum sludge as a soil stabilizer : an accurate predictive tool

By using a grey box AI model, a comprehensive study is presented on the behaviour prediction of alum sludge as a soil stabilizer. To creat models for predicting the California bearing rtio (CBR) of alum sludge as a soil stabilizer, the study employs statistical models, including multiple linear regression (MLR) and Partial least squares (PLS), and advanced artificial intelligence, including classificatoin and regression random forests (CRRF) and classification and regression trees (CART). Results show that CRRF and CART models accurately predict CBR values better than MLR and PLS models. For predicting the behaviour of alum sludge in soil stablization, the compaction number of hammer and sludge content were the most significant parameters. Gs and optimum moisture content of soil were the least important parameters. Study results provide valuable insights into alum sludge’s behaviour as a soil stablizer, which could reduce waste and promote sustainable practice. © 2023 Informa UK Limited, trading as Taylor & Francis Group

Modeling the Effects of Particle Shape on Damping Ratio of Dry Sand by Simple Shear Testing and Artificial Intelligence

This study investigates the effects of sand particle shape, in terms of roundness, sphericity and regularity, on the damping ratio of a dry sand material. Twelve different cyclic simple shear testing scenarios were considered and applied using vertical stresses of 50, 150 and 250 kPa and cyclic stress ratios (CSR) of 0.2, 0.3, 0.4 and 0.5 in both constant- and controlled-stress modes. Each testing scenario involved five tests, using the same sand that was reconstructed from its previous cyclic test. On completion of the cyclic tests, corresponding hysteresis loops were established to determine the damping ratio. The results indicated that the minimum and maximum damping ratios for this sand material were 6.9 and 25.5, respectively. It was observed that the shape of the sand particles changed during cyclic loading, becoming progressively more rounded and spherical with an increasing number of loading cycles, thereby resulting in an increase in the damping ratio. The second part of this investigation involved the development of artificial intelligence models, namely an artificial neural network (ANN) and a support vector machine (SVM), to predict the effects of sand particle shape on the damping ratio. The proposed ANN and SVM models were found to be effective in predicting the damping ratio as a function of the particle shape descriptors (i.e., roundness, sphericity and regularity), vertical stress, CSR and number of loading cycles. Finally, a sensitivity analysis was conducted to identify the importance of the input variables; the vertical stress and regularity were, respectively, ranked as first and second in terms of importance, while the CSR was found to be the least important parameter

Ultrasonic Characterization of Compacted Salty Kaolin–Sand Mixtures Under Nearly Zero Vertical Stress Using Experimental Study and Machine Learning

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

Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm

The main objective of blasting operations is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as flyrock. Flyrock is the source of most of the injuries and property damage in a majority of blasting accidents in surface mines. Therefore, proper prediction and subsequently optimization of flyrock distance may reduce the possible damages. The first objective of this study is to develop a new predictive model based on gene expression programming (GEP) for predicting flyrock distance. To achieve this aim, three granite quarry sites in Malaysia were investigated and a database composed of blasting data of 76 operations was prepared for modelling. Considering changeable GEP parameters, several GEP models were constructed and the best one among them was selected. Coefficient of determination values of 0.920 and 0.924 for training and testing datasets, respectively, demonstrate that GEP predictive equation is capable enough of predicting flyrock. The second objective of this study is to optimize blasting data for minimization purpose of flyrock. To do this, a new non-traditional optimization algorithm namely firefly algorithm (FA) was selected and used. For optimization purposes, a series of analyses were performed on the FA parameters. As a result, implementing FA algorithm, a reduction of about 34 % in results of flyrock distance (from 60 to 39.793 m) was observed. The obtained results of this study are useful to minimize possible damages caused by flyrock