New Prediction Models for Mean Particle Size in Rock Blast Fragmentation

Abstract

The paper refers the reader to a blast data base developed in a previous study. The data base consists of blast design parameters, explosive parameters, modulus of elasticity and in situ block size. A hierarchical cluster analysis was used to separate the blast data into two different groups of similarity based on the intact rock stiffness. The group memberships were confirmed by the discriminant analysis. A part of this blast data was used to train a single-hidden layer back propagation neural network model to predict mean particle size resulting from blast fragmentation for each of the obtained similarity groups. The mean particle size was considered to be a function of seven independent parameters. An extensive analysis was performed to estimate the optimum value for the number of units for the hidden layer for each of the obtained similarity groups. The blast data that were not used for training were used to validate the trained neural network models. For the same two similarity groups, multivariate regression models were also developed to predict mean particle size. Capability of the developed neural network models as well as multivariate regression models was determined by comparing predictions with measured mean particle size values and predictions based on one of the most applied fragmentation prediction models appearing in the blasting literature. Prediction capability of the trained neural network models as well as multivariate regression models was found to be strong and better than the existing most applied fragmentation prediction model. Diversity of the blasts data used is one of the most important aspects of the developed models.

Investigation on Coal Fragmentation by High-Velocity Water Jet in Drilling: Size Distributions and Fractal Characteristics

“…Rosin-Rammler and Weibull distribution functions are usually used to represent the size distribution of rock fragments [23]. Some models have been proposed to predict the mean fragment size for impact fragmentation and rock blast fragmentation [24,25]. The three-parameter generalized extreme value distribution (GEV) can suitably describe the frequency distribution of fragment sizes generated from impact test using an SHPB [21,26].…”

Superpixel Image Segmentation-Based Particle Size Distribution Analysis of Fragmented Rock

“…This greatly reduces the number of samples that need to be processed [29]. As shown in Figure 8, the original image is oversegmented using the mean shift (MS) [6] and Felzenszwalb-Huttenlocher (FH) [1] algorithms to obtain several sets of superpixels of different scales and extract the features of each superpixel. Since mLab (mean value in the Lab color space) features have suitable characteristics for distinguishing different colors, LBP (local binary pattern) features can express image texture features.…”

“…These methods use the index weight distribution and calculation to obtain the final evaluation results [4]. Unascertained measurement theory uses entropy calculations to determine the weight index to quantitatively analyze the blasting effect, and the intelligent evaluation method, which is still in the research and development stage, uses intelligent computer artificial simulation technology to evaluate big complex data [5,6]. These evaluation methods mainly involve fractal theory, gray relational analysis, BP artificial neural networks, genetic algorithms, catastrophe theory, and determining the index membership function [7,8].…”

Influence of parameters on the development of landslides in the Estrada de Ferro Vitória-Minas slopes

“…Multivariate statistical techniques have been used in the literature for different purposes. Investigation of damage from seismic events (Massumi & Gholami, 2016), landslide susceptibility mapping (Ahmed & Dewan, 2017) problem solving in mining (Kulatilake et al, 2012) and prevision of the stability condition of slopes (Santos et al, 2018; are some examples of research carried out with these techniques. Thus this work, the methodology to determine the influence of previously mentioned parameters for the development of landslides was based on techniques of multivariate statistics, such as principal component analysis.…”