• Title/Summary/Keyword: chainsaw

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Comparison of Three Ergonomic Risk Assessment Methods (OWAS, RULA, and REB A) in Felling and Delimbing Operations (벌도 및 가지제거작업에서 세 가지 인간공학적 위험 평가기법의 비교분석)

  • Cho, Min-Jae;Jeong, Eung-Jin;Oh, Jae-Heun;Han, Sang-Kyun
    • Journal of Korean Society of Forest Science
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    • v.110 no.2
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    • pp.210-216
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    • 2021
  • Musculoskeletal disorders affect workers' safety in most industries, and forest operations are classified as a musculoskeletal burden according to the Occupational Safety and Health Act in South Korea. In particular, felling and delimbing operations are mainly conducted by manpower, and then, it is necessary to evaluate ergonomic risk assessment for safety of felling and delimbing workers. Three ergonomic risk assessment methods, such as Ovako Working posture Analysis System (OWAS), Rapid Upper Limb Assessment (RULA), and Rapid Entire Body Assessment (REBA), are available for assessing exposure to risk factors associated with timber harvesting operations. Here, three ergonomic risk assessment methods were applied to examine ergonomic risk assessments in chainsaw felling and delimbing operations. Additionally, exposure to risk factors in each method was analyzed to propose an optimal working posture in felling and delimbing operations. The risk levels of these operations were evaluated to be highest in the RULA method, followed by the OWAS and REBA methods, and most of the exposed working postures were examined with a low-risk level of two and three without requiring any immediate working posture changes. However, two significant working postures, including the bending posture of the waist and leg in felling operation and standing posture on the fallen trees in delimbing operation, were assessed as the high-risk level and needed immediate working posture changes. Low-risk work levels were examined in the squatting posture for felling operation and the straightened posture of the waist and leg for delimbing operation. Moreover, the slope in felling operation and the tree height in delimbing operation significantly affected risk level assessment of working posture. Therefore, our study supports that felling and delimbing workers must operate with low-risk working postures for safety.

Productivity and Cost of Mechanized Felling and Processing Operations Performed with an Excavator-based Stroke Harvester by Tree Species (수종에 따른 스트로크 하베스터의 벌도⋅조재작업 생산성 및 비용)

  • Yun-Sung, Choi;Min-Jae, Cho;Ho-Seong, Mun;Jae-Heun, Oh
    • Journal of Korean Society of Forest Science
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    • v.111 no.4
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    • pp.567-582
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    • 2022
  • Chainsaw use for motor-manual timber harvesting in South Korea is associated with worker safety issues. However, forestry operations such as timber harvesting have already been mechanized to reduce hazards to workers and increase productivity. This study analyzed the productivities and costs of felling and processing, felling and processing using an excavator-based stroke harvester for Pinus rigida and Quercus mongolica stands. To efficiently operate the stroke harvester, we developed a regression equation to estimate the productivities of felling and processing, felling, and processing operations,and we conducted sensitivity analysis of the operation costs using DBH and machine utilization. The felling and processing productivity was 6.53 and 4.02 m3/SMH for P. rigida a nd Q. mongolica, respectively, and the cost was 17,983 and 29,210 won/m3, respectively. The felling productivity for P. rigida a nd Q. mongolica wa s 40.9 and 23.0 m3/SMH, respectively, and the cost was 2,667 and 4,743 won/m3, respectively. The processing productivity for P. rigida and Q. mongolica was 8.25 and 7.75 m3/SMH, respectively, and the cost was 15,296 and 16,283 won/m3, respectively. In the developed regression equation, the DBH, traveling distance, and number of cuttings were found to be important factors (p<0.05). Therefore, it is necessary to construct a DB considering the various conditions and species associated with harvester operations, and further research is needed to increase the accuracy of predicting operation productivity and costs.