Prediction of Blasting Fragmentation Based on GWO-ELM

Abstract

Aiming at the complex nonlinear relationship among factors affecting blasting fragmentation, the input weight and hidden layer threshold of ELM (extreme learning machine) were optimized by gray wolf optimizer (GWO) and the prediction model of GWO-ELM blasting fragmentation was established. Taking No. 2 open-pit coal mine of Dananhu as an example, seven factors including the rock tensile strength, compressive strength, hole spacing, row spacing, minimum resistance line, super depth, and specific charge are selected as the input factors of the prediction model. The average size of blasting fragmentation X50 is selected as the output factor of the prediction model and compared with the results of PSO-ELM and ELM. The results show that MAPE of GWO-ELM, PSO-ELM, and ELM are 1.78%, 5.40%, and 10.90%, respectively; their RMSE are 0.007, 0.022, and 0.045, respectively. The ELM model optimized by the gray wolf optimizer is more accurate and has stronger data fitting ability than PSO-ELM and ELM models, and the prediction accuracy of GWO-ELM is much higher than that of PSO-ELM and ELM.

State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting

“…In the field of rock fragmentation prediction, numerous scholars have developed theoretical and empirical models that rely on blast design parameters. Notably, Jia et al (2022) are among the researchers who have contributed to this area. These models are designed to forecast the extent of rock fragmentation resulting from blasting operations.…”

“…The blasting process is inherently nonlinear and complex, making it challenging to fully comprehend. The existing theoretical and empirical approaches to predict rock fragmentation have limitations as they rely on assumptions and consider only a limited number of influencing factors (Jia et al 2022). To overcome these limitations, alternative techniques such as sieving or screening have been proposed.…”

Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM

“…Even though LSTM satisfies the accuracy requirement, it takes a longer time to find the nonlinear relationship between the sea clutter data, which results in a slower training speed. The ELM network, a single-layer feedforward neural network based on randomization, offers rapid learning, high accuracy, and straightforward implementation [ 19 ]. It has emerged as a focal point of research in recent years.…”

Research and Application of Rock Burst Hazard Assessment of the Working Face Based on the CF-TOPSIS Method

“…Numerous scholars have conducted extensive research on predicting rock bursts [5]. Teir proposed measures can be categorized into mine-wide prediction (intelligent hazard-level judgement), regional prediction (utilizing three spatialtemporal monitoring methods), and local prediction (employing a multiparameter monitoring and early warning index system for rock bursts) [6][7][8][9][10]. Given the complexity of factors contributing to rock bursts, evaluating the level of rock bursts in mines can be challenging.…”