cable logging
Recently Published Documents


TOTAL DOCUMENTS

41
(FIVE YEARS 4)

H-INDEX

6
(FIVE YEARS 0)

2021 ◽  
Vol 51 (9) ◽  
pp. 1391-1391
Author(s):  
Francisca Belart ◽  
Ben Leshchinsky ◽  
Jeff Wimer
Keyword(s):  

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 927
Author(s):  
John Sessions ◽  
Kevin Lyons ◽  
Jeff Wimer

The standing skyline continues to be a common cable logging configuration. In payload analysis it is usually assumed that the tagline (line connecting the logs to the carriage) length is held constant while yarding a turn up the skyline corridor. We show this assumption severely limits the skyline load-carrying capacity for skylines operating with partial suspension. We suggest that smart carriage technology could markedly increase the log load capacity through the use of a variable length tagline, and thus logging productivity. A methodology for estimating the log load capacity for a standing skyline with variable tagline length is presented. We illustrate that increases of 30-40 percent in log load are possible with a variable length tagline.


PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250624
Author(s):  
Eloise G. Zimbelman ◽  
Robert F. Keefe

Analysis of high-resolution inertial sensor and global navigation satellite system (GNSS) data collected by mobile and wearable devices is a relatively new methodology in forestry and safety research that provides opportunities for modeling work activities in greater detail than traditional time study analysis. The objective of this study was to evaluate whether smartwatch-based activity recognition models could quantify the activities of rigging crew workers setting and disconnecting log chokers on cable logging operations. Four productive cycle elements (travel to log, set choker, travel away, clear) were timed for choker setters and four productive cycle elements (travel to log, unhook, travel away, clear) were timed for chasers working at five logging sites in North Idaho. Each worker wore a smartwatch that recorded accelerometer data at 25 Hz. Random forest machine learning was used to develop predictive models that classified the different cycle elements based on features extracted from the smartwatch acceleration data using 15 sliding window sizes (1 to 15 s) and five window overlap levels (0%, 25%, 50%, 75%, and 90%). Models were compared using multiclass area under the Receiver Operating Characteristic (ROC) curve, or AUC. The best choker setter model was created using a 3-s window with 90% overlap and had sensitivity values ranging from 76.95% to 83.59% and precision values ranging from 41.42% to 97.08%. The best chaser model was created using a 1-s window with 90% overlap and had sensitivity values ranging from 71.95% to 82.75% and precision values ranging from 14.74% to 99.16%. These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications associated with high-risk job functions and to advance subsequent, comparative analysis of health and safety metrics across stand, site, and work conditions.


2021 ◽  
Vol 42 (2) ◽  
Author(s):  
Omar Mologni ◽  
Luca Marchi ◽  
Kevin C. Lyons ◽  
Stefano Grigolato ◽  
Raffaele Cavalli ◽  
...  

Skyline tensile forces have been shown to frequently exceed the recommended safety limits during ordinary cable logging operations. Several models for skyline engineering analyses have been proposed. Although skyline tensile forces assume a dynamic behaviour, practical solutions are based on a static approach without consideration of the dynamic nature of the cable systems.The aim of this study was to compare field data of skyline tensile forces with the static calculations derived by dedicated available software such as SkylineXL. To overcome the limitation of static calculation, this work also aimed to simulate the actual response of the tensile fluctuations measured in the real environment by mean of a finite element model (FEM).Field observations of skyline tensile forces included 103 work cycles, recorded over four different cable lines in standing skyline configuration. Payload estimations, carriages positions, and time study of the logging operations were also collected in the field. The ground profiles and the cable line geometries were analysed using digital elevation models. The field data were then used to simulate the work cycles in SkylineXL. The dynamic response of six fully-suspended loads in a single-span cable line was also simulated by a dedicated FEM built through ANSYS®. The observed data and the software calculations were then compared.SkylineXL resulted particularly reliable in the prediction of the actual tensile forces, with RMSE ranging between 7.5 and 13.5 KN, linked to an average CV(RMSE) of 7.24%. The reliability in predicting the peak tensile forces was lower, reporting CV(RMSE) of 10.12%, but still not likely resulting in a safety or performance problem. If properly set-up and used, thus, SkylineXL could be considered appropriate for operational and practical purposes. This work, however, showed that finite element models could be successfully used for detailed analysis and simulation of the skyline tensile forces, including the dynamic oscillations due to the motion of the carriage and payload along the cable line. Further developments of this technique could also lead to the physical simulation and analysis of the log-to-ground interaction and the investigation of the breakout force during lateral skidding.


Author(s):  
Luca Marchi ◽  
Davide Trutalli ◽  
Omar Mologni ◽  
Raimondo Gallo ◽  
Dominik Roeser ◽  
...  

2020 ◽  
Author(s):  
Filippo Guerra ◽  
Luca Marchi ◽  
Stefano Grigolato ◽  
Raimondo Gallo
Keyword(s):  

2019 ◽  
Vol 53 (6) ◽  
pp. 35-44
Author(s):  
JoungWon You ◽  
◽  
Hee Han ◽  
JooSang Chung

2019 ◽  
Vol 5 (3) ◽  
pp. 114-123 ◽  
Author(s):  
Hunter Harrill ◽  
Rien Visser ◽  
Keith Raymond
Keyword(s):  

Author(s):  
V. A. Katsadze ◽  
◽  
A. R. Birman ◽  
F. V. Svoykin ◽  
N. S. Korolko ◽  
...  
Keyword(s):  

2019 ◽  
Author(s):  
Alessandro Lezier ◽  
Alberto Cadei ◽  
Omar Mologni ◽  
Luca Marchi ◽  
Stefano Grigolato
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document