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Research Article

A Study on the Predictive Model for Estimating the Productivity of Vibratory Rippers Based on Rock Strength

진동 리퍼의 생산량 추정을 위한 암반강도에 따른 생산량 예측 모델 고찰

Mingyu Kim12
,
Chulho Lee34*
김 민규12
,
이 철호34*
1Member, Graduate Student, KICT School, University of Science and Technology, 283 Goyang-daero,Ilsanseo-Gu, Goyang-Si, Gyeonggi-Do 10223, Republic of Korea
2Member, Student Researcher, Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology, 283 Goyang-daero,Ilsanseo-Gu, Goyang-Si, Gyeonggi-Do 10223, Republic of Korea
3Member, Professor, KICT School, University of Science and Technology, 283 Goyang-daero,Ilsanseo-Gu, Goyang-Si, Gyeonggi-Do 10223, Republic of Korea
4Member, Senior Researcher, Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology, 283 Goyang-daero,Ilsanseo-Gu, Goyang-Si, Gyeonggi-Do 10223, Republic of Korea
*Corresponding Author
30 December 2025. pp. 41-51
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Abstract

This study presents a predictive model to estimate the productivity of excavator-mounted vibratory rippers as a function of rock strength, with the aim of improving their field applicability. Previous research on ripper productivity has predominantly focused on bulldozer-mounted rippers, while academic studies on excavator-mounted vibratory rippers remain scarce. To address this research gap, data provided by overseas manufacturers and field test results in this study were utilized to analyze the quantitative relationship between uniaxial compressive strength (UCS) of rock and the corresponding production rate. Various machine learning algorithms were compared, and the Gamma regression model was found to most effectively represent the exponential decrease characteristics associated with increasing rock strength. Furthermore, empirical equations originally developed for impact hammers were extended to derive RQD-based productivity curves, enabling the proposed model to be applied even at sites where RQD data are not available. Although the dataset used in this study is limited and may restrict generalization, the proposed approach provides a quantitative foundation for evaluating and predicting the productivity of backhoe-mounted vibratory rippers. Future work will focus on improving model reliability and prediction accuracy through the integration of additional domestic field data.

Keywords
Vibratory ripper
Excavation
Machine learning
Productivity prediction
RQD

진동 리퍼의 현장 적용성을 높이기 위해, 본 연구에서는 굴삭기용 진동 리퍼의 생산성을 암반 강도에 따라 예측할 수 있는 모델을 제안하였다. 기존의 리퍼 생산성 관련 연구는 대부분 도저용 리퍼에 한정되어 수행되었으며, 굴삭기용 진동 리퍼에 관한 학술적 연구는 매우 제한적이다. 이에 본 연구에서는 해외 제조사 제공 자료와 현장 시험 결과를 활용하여, 진동 리퍼의 생산성을 암반의 일축압축강도와 생산량 간의 상관관계로 정량적으로 분석하였다. 상관관계 분석에는 다양한 머신러닝 기법을 적용하였으며, 그중 감마 회귀(Gamma Regression) 방식이 암반 강도 증가에 따른 생산량의 지수적 감소 특성을 가장 안정적으로 반영하는 것으로 나타났다. 또한, 기존 문헌에서 제시한 임팩트 해머의 경험식을 확장 적용하여 RQD별 생산량 곡선을 제시함으로써, RQD 지표가 확보되지 않은 현장에서도 활용 가능한 예측 모델을 구축하였다. 비록 데이터의 양이 제한적이며 일반화에는 한계가 있으나, 본 연구는 굴삭기용 진동 리퍼의 생산성을 정량적으로 평가하고 예측할 수 있는 기초적 근거를 제시한다는 점에서 의의가 있다. 향후에는 국내 현장 데이터를 추가 확보함으로써 모델의 신뢰성과 예측 정밀도를 향상시킬 예정이다.

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Information
  • Publisher :Korean Geosythetics Society
  • Publisher(Ko) :한국지반신소재학회
  • Journal Title :Journal of the Korean Geosynthetics Society
  • Journal Title(Ko) :한국지반신소재학회 논문집
  • Volume : 24
  • No :4
  • Pages :41-51
  • Received Date : 2025-10-27
  • Revised Date : 2025-11-20
  • Accepted Date : 2025-11-27
  • DOI :https://doi.org/10.12814/jkgss.2025.24.4.041
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