Application of Intelligent Models for Flyrock Prediction Considering Design Parameters and Bench Face Characteristics

Turker Hudaverdi*, Yasar Agan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This study tries to predict blast-induced flyrock using intelligent modelling techniques. Flyrock is a site-specific phenomenon. Design variables and rock properties highly influence flyrock throw distance. Site investigations were conducted in a sandstone quarry. Fundamental operational parameters, blast dimensions, bench face condition, and rock mass structure were monitored. Stepwise regression technique was applied for variable selection. Burden–hole diameter ratio, in situ block size, and powder factor were determined as the most significant parameters. Considering output of stepwise regression, artificial neural network, ANFIS, and Gaussian process regression techniques were applied to predict flyrock throw. A comprehensive validation was performed using twelve different performance indices. Pre-determination of input parameters supported development of successful soft computing applications. Rock block size was found to be an appropriate input variable for flyrock modelling. In addition to classical performance indices, standardized and symmetric accuracy metrics were quite useful in model validation process. The intelligent models predict flyrock range with an error less than 6 m. The mean percentage errors are lower than 10%. ANFIS seems to be the best model for flyrock prediction. The calculated mean absolute error is 5.36 for ANFIS model.

Original languageEnglish
Pages (from-to)2331-2347
Number of pages17
JournalMining, Metallurgy and Exploration
Issue number6
Publication statusPublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023, Society for Mining, Metallurgy & Exploration Inc.


This study was partly supported by the Research Fund of the Istanbul Technical University (project name: The effects of the variations in blast design and initiation systems on blast induced ground vibrations. No: 38511).

FundersFunder number
Ulusal Yüksek Başarımlı Hesaplama Merkezi, Istanbul Teknik Üniversitesi
Istanbul Teknik Üniversitesi
Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik Üniversitesi38511


    • Adaptive-network-based fuzzy inference system (ANFIS)
    • Artificial neural network (ANN)
    • Blasting
    • Flyrock
    • Gaussian process regression (GPR)
    • Rock block size


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