Applications of AI and ML Techniques to Predict Backbreak and Flyrock Distance Resulting from Blasting

Bhatawdekar, Ramesh Murlidhar; Armaghani, Danial Jahed; Azizi, Aydin

doi:10.1007/978-981-16-8237-7_3

Cited by 6 publications

(2 citation statements)

References 76 publications

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“…Researchers and subject matter experts have used these techniques to identify rock types within mining blocks, optimize the truck dispatching approach to increase mine production, and provide better and faster mineral reserve and ore grade estimates [13,[15][16][17][18][19]. Additionally, several researchers have used data analytics and ML algorithms to optimize drill and blast in terms of fragmentation, back breaks, fly rocks, and blast-induced ground vibrations [20][21][22][23][24][25]. Srivastava et al [26][27][28][29], have employed these techniques to improve mill throughput, reduce high costs, and enhance mill efficiency in operating mines.…”

Section: Work Done By Researchers and Subject-matter Expertsmentioning

confidence: 99%

Untitled

2022

AMMS

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Advances in data analytics and Machine Learning (ML) techniques have seen a tremendous uplift in recent years and impacted most complex mining operations regarding safety improvement, operations optimization, and cost reduction. However, due to a lack of coding skills, most mining personnel face the challenge of fully adopting these techniques. In this project, a desktop application, Ute Analytics, was developed to allow users with little or no coding skills to perform data analytics and apply machine learning to any structured data. The app has an easy-to-use Graphical User Interface (GUI) and in-built data cleaning algorithms for users to import structured data, perform data cleaning, and store the cleaned data on their computers. Several Exploratory Data Analysis (EDA) tools were built into the app to enable users to explore trends and get insights from data. Additionally, the app has in-built machine learning tools-regression and classification algorithms-to give users the ability to train, test, and build machine learning models for predictive purposes. Finally, the app is made executable so it can be installed on any computer with Windows Operating System. With its user-friendly GUI, the desktop application was tested on real industrial data, and its ease of use proved that mining personnel and data enthusiasts with no coding experience could use it to benefit from data analytics without the need to understand or write complex computer codes.

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Section: Work Done By Researchers and Subject-matter Expertsmentioning

confidence: 99%

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2022

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“…Blast design necessitates a careful match of drill design parameters, explosive properties, rock properties, and environmental conditions. Raina et al and Bhatawdekar et al observed that a mismatch between these parameters during blast design causes fly-rock during the blasting operation [19,20]. The three primary mechanisms for the generation of fly-rock, according to Mohamad et al, are cratering, rifling and rock face bursting [21].…”

Section: Introductionmentioning

confidence: 99%

Safe small scale mine blasting operation: An application of soft computing techniques to predict blast-induced flyrock distance

Taiwo,

Hosseini,

Fissha

et al. 2023

Preprint

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Flyrock is a major environmental and safety issue during mine production blasting. This can affect workers’ safety and efficiency. Blasting in mines can cause rock fragments to travel far to endanger workers, machinery, and nearby structures. Also, many rock excavation projects anticipated fly-rocks and prevent its impact. This study uses Long Short-term memory networks and seven other machine learning methods to predict the Akoko Edo dolomite Quarries' fly-rock distance. Two hundred and fifty-eight (258) blasting data samples were obtained for model-building. Fly-rock prediction was created using LSTM machine learning. The proposed LSTM fly-rock prediction model was tested using bidirectional recurrent neural networks (BRNN), support vector regressions (SVR-S, SVR-RBF, SVR-L, and SVR-P), and random forest (RF) models. Each modeling method is detailed enough for other scientists to replicate. The top three influencing parameters, which have different correlations with fly-rock, were used to create a fly-rock possibility assessment chart. After testing and training, each machine-learning method was compared. The model error analysis indices and coefficient of determination showed that the LSTM model was the best at predicting fly-rock distance. By accurately predicting fly-rock distance, the LSTM prediction model will go a long way to improve blast area reliability and safety.

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Six Novel Hybrid Extreme Learning Machine–Swarm Intelligence Optimization (ELM–SIO) Models for Predicting Backbreak in Open-Pit Blasting

Zhou

Khandelwal

et al. 2022

Nat Resour Res

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Backbreak (BB) is one of the serious adverse blasting consequences in open-pit mines, because it frequently reduces economic benefits and seriously affects the safety of mines. Therefore, rapid and accurate prediction of BB is of great significance to mine blasting design and other production activities. For this purpose, six different swarm intelligence optimization (SIO) algorithms were proposed to optimize the extreme learning machine (ELM) model for BB prediction, i.e., ELM-based particle swarm optimization (ELM–PSO), ELM-based fruit fly optimization (ELM–FOA), ELM-based whale optimization algorithm (ELM–WOA), ELM-based lion swarm optimization (ELM–LOA), ELM-based seagull optimization algorithm (ELM–SOA) and ELM-based sparrow search algorithm (ELM–SSA). In total, 234 data records from blasting operations in the Sungun mine in Iran were used in this study, including six input parameters (special drilling, spacing, burden, hole length, stemming, powder factor) and one output parameter (i.e., BB). To evaluate the predictive performance of the different optimization models and initial models, six performance indicators including the root mean square error (RMSE), Pearson correlation coefficient (R), determination coefficient (R2), variance accounted for (VAF), mean absolute error (MAE) and sum of square error (SSE) were used to evaluate the models in the training and testing phases. The results show that the ELM–LSO was the best model to predict BB with RMSE of 0.1129 (R: 0.9991, R2: 0.9981, VAF: 99.8135%, MAE: 0.0706 and SSE: 2.0917) in the training phase and 0.2441 in the testing phase (R: 0.9949, R2: 0.9891, VAF: 98.9806%, MAE: 0.1669 and SSE: 4.1710). Hence, ELM techniques combined with SIO algorithms are an effective method to predict BB.

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Applications of AI and ML Techniques to Predict Backbreak and Flyrock Distance Resulting from Blasting

Cited by 6 publications

References 76 publications

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Safe small scale mine blasting operation: An application of soft computing techniques to predict blast-induced flyrock distance

Six Novel Hybrid Extreme Learning Machine–Swarm Intelligence Optimization (ELM–SIO) Models for Predicting Backbreak in Open-Pit Blasting

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