Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R2) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R2, MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Science

Application of artificial intelligence techniques for predicting the flyrock, Sungun mine, Iran

Flyrock is known as one of the main problems in open pit mining operations. This phenomenon can threaten the safety of mine personnel, equipment and buildings around the mine area. One way to reduce the risk of accidents due to flyrock is to accurately predict that the safe area can be identified and also with proper design of the explosion pattern, the amount of flyrock can be greatly reduced. For this purpose, 14 effective parameters on flyrock have been selected in this paper i.e. burden, blasthole diameter, sub-drilling, number of blastholes, spacing, total length, amount of explosives and a number of other effective parameters, predicting the amount of flyrock in a case study, Songun mine, using linear multivariate regression (LMR) and artificial intelligence algorithms such as Gray Wolf Optimization algorithm (GWO), Moth-Flame Optimization algorithm (MFO), Whale Optimization Algorithm (WOA), Ant Lion Optimizer (ALO) and Multi-Verse Optimizer (MVO). Results showed that intelligent algorithms have better capabilities than linear regression method and finally method MVO showed the best performance for predicting flyrock. Moreover, the results of the sensitivity analysis show that the burden, ANFO, total rock blasted, total length and blast hole diameter are the most significant factors to determine flyrock, respectively, while dynamite has the lowest impact on flyrock generation.Peer ReviewedObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPostprint (published version

Development of new comprehensive relations to assess rock fragmentation by blasting for different open pit mines using GEP algorithm and MLR procedure

The fragment size of blasted rocks considerably affects the mining costs and production efficiency. The larger amount of blasthole diameter (ϕh) indicates the larger blasting pattern parameters, such as spacing (S), burden (B), stemming (St), charge length (Le), bench height (K), and the larger the fragment size.  In this study, the influence of blasthole diameter, blastability index (BI), and powder factor (q) on the fragment size were investigated. First, the relation between each of X20, X50, and X80 with BI, ϕh, and q as the main critical parameters were analyzed by Table curve v.5.0 software to find better input variables with linear and nonlinear forms. Then, the results were analyzed by multivariable linear regression (MLR) procedure using SPSS v.25 software and gene expression programming (GEP) algorithm for prepared datasets of four open-pit mines in Iran. Relations between each of X20, X50, and X80 with the combination of adjusted BI, ϕh, and q were obtained by MLR procedure with good correlations of determination (R2) and less root mean square error (RMSE) values of (0.811, 1.4 cm), (0.874, 2.5 cm) and (0.832, 5.4 cm) respectively. Moreover, new models were developed to predict X20, X50, and X80 by the GEP algorithm with better correlations of R2 and RMSE values (0.860, 1.3 cm), (0.913, 2.49 cm), and (0.885, 5.6 cm) respectively and good agreement with actual field results. The developed GEP models can be used as new relations to estimate the fragment sizes of blasted rocks

New Developments for the Sustainable Exploitation of Ornamental Stone in Carrara Basin

5The use of natural stone has a historical and environmental value that makes it strategically valuable for landscape conservation in Europe. Marble, among others, is widely spread on Earth, and it offers high-performance features in architectural applications. However, the complexity of these formations and the rock variability in different ore bodies require detailed studies of the natural and induced stress state, the fracturing degree, and the influence of external factor (such as temperature and/or chemical agents) on the mechanical properties in order to optimize the exploitation processes by reducing extractive waste. This article shows a series of studies conducted by the authors over the last 20 years aimed at making the exploitation of marble blocks in the Carrara basin safer, more efficient, and, therefore, more sustainable. In particular, studies for increasing the knowledge on the natural and the induced stress state through on-site measurements and numerical modeling, studies to improve the quality of the exploited material through improvements of cutting technologies, studies to improve the knowledge of the mechanical behavior of the material under varying loads and temperature conditions and studies to improve the reuse of water materials and their reduction are reported.openopenFederico Vagnon, Giovanna Antonella Dino, Gessica Umili, Marilena Cardu, Anna Maria FerreroVagnon, Federico; Dino, Giovanna Antonella; Umili, Gessica; Cardu, Marilena; Maria Ferrero, Ann

The Importance of Rock Mass Damage in the Kinematics of Landslides

The stability and kinematics of rock slopes are widely considered to be functions of lithological, structural, and environmental features. Conversely, slope damage features are often overlooked and considered as byproducts of slope deformation. This paper analyzes and discusses the potential role of slope damage, its time-dependent nature, and its control on both the stability of rock slopes and their kinematics. The analysis of several major landslides and unstable slopes, combined with a literature survey, shows that slope damage can play an important role in controlling short- and long-term slope stability. Seasonal and continuously active events cause permanent deformation within the slope due to the accumulation of slope damage features, including rock mass dilation and intact rock fracturing. Rock mass quality, lithology, and scale control the characteristics and complexity of slope damage, as well as the failure mechanism. The authors propose that the role of slope damage in slope kinematics should always be considered in slope stability analysis, and that an integrated characterization–monitoring–numerical modelling approach can enhance our understanding of slope damage, its evolution, and the controlling factors. Finally, it is emphasized that there is currently a lack of guidelines or frameworks for the quantitative assessment and classification of slope damage, which requires a multidisciplinary approach combining rock mechanics, geomorphology, engineering geology, remote sensing, and geophysics

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

Remote sensing of geomorphodiversity linked to biodiversity — part III: traits, processes and remote sensing characteristics

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed

The Hidden Hazard Of Melting Ground Ice In Northern Iceland

This thesis explores the morphology, dynamics and causes of landslides and debris flows in mountainous regions of northern Iceland. The primary objectives are to define the initiation and evolution of Icelandic landslides and debris flows, and to understand the link between ground-ice thaw and rapid mass movements. Slopes are predicted to react more intensely to global warming, so improving our knowledge of rapid mass movements in cold environments, which are even more sensitive to climate change, is crucial, as they could pose at risk local population in Iceland and other mountainous periglacial areas. I first perform a detailed study of debris flows in north-western Iceland, distinguishing through quantitative geomorphological methods the different mechanisms of debris-flow initiation and the associated geomorphic features. The approach of this study is easily applicable to similar settings, and its results could help in anticipating new potentially destructive events. Secondly, I describe and quantify the morphometric characteristics of two landslides in northern Iceland, whose source materials comprised ground ice-cemented deposits. This study reveals different dynamic landslide processes and the crucial role of thawing ground ice in landslide emplacement. I then analyse meteorological and seismic data near these two landslides. I define and distinguish precipitation, seismic activity and permafrost degradation as the preparatory and triggering factors for the failures. Finally, through a geomorphic approach I analyse molards, conical mounds of debris that I found in both landslides deposits. I show conclusive evidence that molards form from thawing of blocks of ice-rich sediments that degrade into cones of debris. I demonstrate that molards are the ‘fingerprint” of permafrost degradation, and their different morphology and distribution can reveal different types of landslide processes in periglacial terrains. This thesis widens our knowledge of the conditions and processes controlling rapid mass movements in cold environments, which is crucial in the perspective of hazard assessment, and opens up new avenues for the study of potentially hazardous geomorphic responses of cold landscapes to changing climate conditions