Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Alloysius Vendhi Prasmoro
Institut Teknologi Sepuluh Nopember
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Prediction of Ground Vibration due to Blasting Activities of Coal Open Pit Mine Near Village Residents Using Multivariate Logarithmic Regression Alloysius Vendhi Prasmoro; Nurhadi Siswanto; Budi Santosa; Giri Waluyo Nugraha; Mohd Shukor Salleh
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.3616

Abstract

Blasting activities are among the most important in mining and can have a negative impact on the environment and surrounding communities, causing disruption and even damaging nearby buildings and infrastructure. If the community protests and demonstrates, mining operations may be shut down, which would be very detrimental to the company. Studies are needed on effective planning to reduce the negative impacts. Ground vibrations measured by Peak Particle Velocity (PPV) are subject to thresholds set by each region's standards; in this research area, the maximum threshold is 3 mm/s to avoid damage to nearby buildings or settlements. The important variables are the explosive charge per delay and the distance, along with other blasting geometry variables such as spacing, burden, stemming, powder factor, and number of blast holes. Several previous researchers have used methods to predict PPV, with detonation parameters as the independent variables. This research uses general empirical methods and multivariate logarithmic regression (MLR). Prediction using MLR is better than general empirical methods; with R2 = 0.925, RMSE = 0.247, MAE = 0.548, MAPE = 0.218, and VAF = 96.66%, indicating near-perfect prediction. The MLR model produces a reference maximum explosive charge per delay of 59.09 kg.
Co-Authors Budi Santosa Giri Waluyo Nugraha Mohd Shukor Salleh Nurhadi Siswanto