Intelligence prediction of some selected environmental issues of blasting: A review

Background: Blasting is commonly used for loosening hard rock during excavation for generating the desired rock fragmentation required for optimizing the productivity of downstream operations. The environmental impacts resulting from such blasting operations include the generation of flyrock, ground vibrations, air over pressure (AOp) and rock fragmentation. Objective: The purpose of this research is to evaluate the suitability of different computational techniques for the prediction of these environmental effects and to determine the key factors which contribute to each of these effects. This paper also identifies future research needs for the prediction of the environmental effects of blasting operations in hard rock. Methods: The various computational techniques utilized by the researchers in predicting blasting environmental issues such as artificial neural network (ANN), fuzzy interface system (FIS), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO), were reviewed. Results: The results indicated that ANN, FIS and ANN-ICA were the best models for prediction of flyrock distance. FIS model was the best technique for the prediction of AOp and ground vibration. On the other hand, ANN was found to be the best for the assessment of fragmentation. Conclusion and Recommendation: It can be concluded that FIS, ANN-PSO, ANN-ICA models perform better than ANN models for the prediction of environmental issues of blasting using the same database. This paper further discusses how some of these techniques can be implemented by mining engineers and blasting team members at operating mines for predicting blast performance

Effect of geological structure and blasting practice in fly rock accident at Johor, Malaysia

Blasting operation is common method in hard rock excavation at civil engineering and mining sites. Rock blasting results in the fragmentation along with environmental hazards such as fly rock, ground vibration, air-blast, dust and fumes. Most of the common accidents associated with blasting are due to fly rock. A fly rock accident had occurred on 15 July 2015 at a construction site at Johor, Malaysia. Due to this accident, nearby factory worker was killed while two other workers were seriously injured after being hit by rock debris from an explosion at construction site, 200 m away from the factory. The main purpose of this study is to investigate the causes of fly rock accident based on geological structures and blasting practice such as blast design, pre inspection on geological structures, identifying danger zone due to blasting and communication and evacuation of personnel before blast. It can be concluded that fly rock could have been controlled in three stages; initial drilling of holes based on blast design, ensure limiting charge for holes having less burden or having geological discontinuity, and selecting proper sequence of initiation of holes

Effect of geological structure on flyrock prediction in construction blasting

Blasting is sometimes inevitable in civil engineering work, to fragment the massive rock to enable excavation and leveling. In Minyak Beku, Batu Pahat also, blasting is implemented to fragment the rock mass, to reduce the in situ rock level to the required platform for a building construction. However, during blasting work, some rocks get an excessive amount of explosive energy and this energy may generate flyrock. An accident occurred on 15 July 2015 due to this phenomenon, in which one of the workers was killed and two other workers were seriously injured after being hit by the flyrock. The purpose of this study is to investigate the causes of the flyrock accidents through evaluation of rock mass geological structures. The discontinuities present on the rock face were analyzed, to study how they affected the projection and direction of the flyrock. Rock faces with lower mean joint spacing and larger apertures caused excessive flyrock. Based on the steoreonet analysis, it was found that slope failures also produced a significant effect on the direction, if the rock face failure lay in the critical zone area. Empirical models are often used to predict flyrock projection. In this study five empirical models are used to compare the incidents. It was found that none of the existing formulas could accurately predict flyrock distance. Analysis shows that the gap between predicted and actual flyrock can be reduced by including blast deign and geological conditions in forecasts. Analysis revealed only 69% of accuracy could be achieved if blast design is the only parameter to be considered in flyrock projection and the rest is influenced by the geological condition. Other causes of flyrock are discussed. Comparison of flyrock prediction with face bursting, cratering and rifling is carried out with recent prediction models

Prediction of rock interlocking by developing two hybrid models based on GA and fuzzy system

Rock shear strength parameters (interlocking and internal friction angel) are considered as significant factors in the designing stage of various geotechnical structures such as tunnels and foundations. Direct determination of these parameters in laboratory is time-consuming and expensive. Additionally, preparation of good quality of core samples is sometimes difficult. The objective of this paper is introducing and evaluating two hybrid artificial neural network (ANN)-based models by considering genetic algorithm (GA) and fuzzy inference system for prediction of interlocking of shale rock samples. Therefore, hybrid GA-ANN and adoptive neuro-fuzzy inference system (ANFIS) were developed and to show the capability of the hybrid models, the predicted results were compared to those of a pre-developed ANN model. In development of these models, the results of rock index tests, i.e., point load index, dry density, p-wave velocity, Brazilian tensile strength and Schmidt hammer were taken into account as the input parameters, whereas the interlocking of the shale samples was set as the output. The results obtained in this study confirmed the high reliability of the developed hybrid models, however, ANFIS predictive model receives slightly higher performance prediction compared to GA-ANN technique. The obtained results of the developed models were (0.865, 0.852), (0.933, 0.929) and (0.957, 0.965) for ANN, GA-ANN and ANFIS models, respectively, based on coefficient of determination (R2). ANFIS can be introduced as an innovative model to the field of rock mechanics

Application of tree-based predictive models to forecast air overpressure induced by mine blasting

In surface mines and underground excavations, every blasting operation can have some destructive environmental impacts, among which air overpressure (AOp) is of major significance. Therefore, it is essential to minimize the related environmental damage by precisely evaluating the intensity of AOp before any blasting operation. The present study primarily aimed to develop two different tree-based data mining algorithms, namely M5′ decision tree and genetic programming (GP) for accurately predicting blast-induced AOp in granite quarries. In addition, a multiple linear regression technique was adopted to check the accuracy of the GP and M5′ models. To achieve the aims of this research, 125 blasts were explored and their respective AOp values were carefully recorded. In each operation, six influential parameters of AOp, i.e., stemming length, powder factor, blasting index, joint aperture, maximum charge weight per delay and distance of the blast points, were recorded and considered as inputs for modeling. After developing the predictive models of AOp, their performances were examined in terms of coefficient of determination (R2), root-mean-squared error (RMSE) and mean absolute error (MAE). Based on the computed results, the GP (with RMSE of 1.3997, R2 of 0.8621 and MAE of 0.9472) outperformed the other developed models. Then, a sensitivity analysis was employed to identify the most influential parameters in predicting the AOp values. Finally, the generality of the proposed GP model was validated by investigating its predictive results with respect to the two most effective predictor variables. The study findings demonstrated the robustness and applicability of the proposed GP model for predicting blast-induced AOp

Recent developments in machine learning and flyrock prediction

The blasting techniques are employed in mining and underground works to loosen the rock mass and ease the excavation. The blasting practices are economical and swifter in terms of their engineering application, however, they are of major environmental and safety concerns. The major issues related to blasting are flyrock, air over pressure, and ground vibrations etc. The rock fragments of rockmass are thrown outward after blasting, which can be threat to workers and machineries involved in the work, and sometimes nearby human settlements can be its victim. Therefore, an accurate prediction of the flyrock distance is the needed by mining practitioners. Earlier, experts have developed several empirical methods based on certain known parameters to assess flyrock distance. However, with time they become irrelevant and were easily replaced with advanced machine learning algorithm. The present study reviews some of these latest publications (2019–2021) examining flyrocks through artificial intelligent technique. The study incorporates types of machine learning models employed, input parameters used and number of datasets supporting the models. The input parameters were further classified according to rock-mass properties, blast design at site, and explosives responsible for blasting. Moreover, to compare the reliability of the model coefficient of correlation of the testing data of the all the documented model were evaluated. Rock density, rock mass rating and Shmidt hammer rebound number (SHRN) were found to be uncertain parameters. Artificial Neural Network (ANN) and other hybrid models for prediction of flyrock were compared

Intelligent technique for prediction of blast fragmentation due to the blasting in tropically weathered limestone

In the rock blasting scenario, the success of fragmentation plays a major role. Prediction of blasted rock mass fragmentation has a significant role in the overall economics of opencast mines. Blast fragmentation has a direct impact on efficiency and cost of operation consisting of loading, transport and crushing. Tropical weathering has a direct impact on rock mass properties and strength of rock. Thus, challenging issues are created for blastability of tropically weathered limestone. It is necessary to analyze the blast fragmentation and optimize the blasting parameters. Selected limestone mine for this study is in Thailand. Various rock mass properties such as GSI, RMR, and stemming length were studied to find out the correlation with rock fragmentation. Stiffness ratio, hole diameter to burden ratio, powder factor, maximum charge per delay, RQD, blastability index (BI), weathering index (WI) and mean block size were input parameters to analyse mean rock fragment size. Total 105 data sets were collected. In this paper, a hybrid model with Artificial neural network (ANN), Particle swarm optimization (PSO) known as PSO-ANN was implemented to analyse the blast fragmentation. Multivariate regression Analysis (MVRA) was also performed. 83 datasets were trained and balance data sets were utilised for testing data. R2 values for training with PSO-ANN and MVRA showed 0.818 and 0.657 respectively. R2 values for testing with PSO-ANN and MVRA showed 0.70 and 0.694 respectively. Thus the hybrid model PSO-ANN was found useful to predict fragmentation

The use of new intelligent techniques in designing retaining walls

The stability of retaining walls against overturning is analyzed in this study using artificial intelligence methods. Five input parameters including wall height, wall thickness, soil friction angle, soil density, and stone cement mixture density were varied and 2000 cases were considered in developing the predictive models. Using the artificial neural network (ANN) method, eight prediction models were developed and evaluated based on the coefficient of determination (R2) and the root mean square error. R2 values of 0.9740 and 0.9824 for training and testing datasets, respectively (for the best model), indicate the level of ANN capability in predicting safety factor (SF) of retaining walls. After developing the ANN model, the ant colony optimization (ACO) algorithm was used to maximize the safety factor of the wall by varying the input parameters. In fact, the best ANN model was selected to be used as a modeling function in ACO algorithm. The SF result from optimization section was obtained as 3.057 which show a significant difference from the mean SF values used in the modeling. It can be concluded that ACO may be used as a powerful optimization algorithm in optimizing SF results of retaining walls

A novel intelligent ELM-BBO technique for predicting distance of mine blasting-induced flyrock

Blasting is an economical technique for rock breaking in hard rock excavation. One of its complex undesired environmental effects is flyrock, which may result in human injuries, fatalities and property damage. Because previously developed techniques for predicting flyrock are having less accuracy, this paper develops a new hybrid intelligent system of extreme learning machine (ELM) optimized by biogeography-based optimization (BBO) for prediction of flyrock distance resulting from blasting in a mine. In the BBO-ELM system, the role of BBO is to optimize the weights and biases of ELM. For comparison purposes, another hybrid model, i.e., particle swarm optimization (PSO)-ELM and a pre-developed ELM model were also applied and proposed. To do so, 262 datasets including burden to spacing ratio, hole diameter, powder factor, stemming, maximum charge per delay and hole depth as input variables and flyrock distance as system output were considered and used. Many models with different combinations of training and testing datasets have been constructed to identify the best predictive model in estimating flyrock. The results indicate capability of the newly developed BBO-ELM model for predicting flyrock distance. The coefficient of determination, coefficient of persistence and root mean square error values of (0.93, 0.93 and 21.51), (0.94, 0.95 and 18.84) and (0.79, 0.85 and 32.29) were obtained for testing datasets of PSO-ELM, BBO-ELM and ELM model, respectively, which reveal that the BBO-ELM is a powerful model for predicting flyrock induced by blasting. The developed BBO-ELM model can be introduced as a new, capable and applicable model for solving engineering problems