Proto Andrea Rosario, PhD.

Tensile Force Monitoring on Large Winch-Assist Forwarders Operating in British Columbia

volume: 39, issue: 2

The forest industry around the world is facing common challenges in accessing wood fiber on
steep terrain. Fully mechanized harvesting systems based on specialized machines, such as
winch-assist forwarders, have been specifically developed for improving the harvesting performances
in steep grounds. While the mechanization process is recognized as a safety benefit,
the use of cables for supporting the machine traction needs a proper investigation. Only a few
studies have analyzed the cable tensile forces of winch-assist forwarders during real operations,
and none of them focused on large machines normally used in North America. Consequently,
a preliminary study focused on tensile force analysis of large winch-assist forwarders was
conducted in three sites in the interior of British Columbia during the fall of 2017.
The results report that in 86% of the cycles, the maximum working load of the cable was less
than one-third of the minimum breaking load. The tensile force analysis showed an expected
pattern of minimum tensile forces while the forwarders were traveling or unloading on the
road site and high tensile forces when operating on steep trails, loading or traveling. Further
analysis found that the maximum cycle tensile forces occurred most frequently when the
machines were moving uphill, independently of whether they were empty or loaded. While the
forwarders were operating on the trails, slope, travel direction, and distance of the machines
from the anchor resulted statistically significant and able to account for 49% of tensile force
variability. However, in the same conditions, the operator settings accounted for 77% of the
tensile force variability, suggesting the human factor as the main variable in cable tensile force
behavior during winch-assist operations.

A Three-Step Neural Network Artificial Intelligence Modeling Approach for Time, Productivity and Costs Prediction: A Case Study in Italian Forestry

volume: 41, issue: 1

The improvement of harvesting methodologies plays an important role in the optimization of wood production in a context of sustainable forest management. Different harvesting methods can be applied according to forest site-specific condition and the appropriate mechanization level depends on a number of factors. Therefore, efficiency and functionality of wood harvesting operations depend on several factors. The aim of this study is to analyze how the different harvesting processes affect operational costs and labor productivity in typical small-scale Italian harvesting companies. A multiple linear regression model (MLR) and artificial neural network (ANN) have been carried out to predict gross time, productivity and costs estimation in a series of qualitative and quantitative variables. The results have created a correct statistical model able to accurately estimate the technical parameters (work time and productivity) and economic parameters (costs per unit of product and per hectare) useful to the forestry entrepreneur to predict the results of the work in advance, considering only the values detectable of some characteristic elements of the worksite.


Web of Science Impact factor (2019): 2.500
Five-years impact factor: 2.077

Quartile: Q1 - Forestry

Subject area

Agricultural and Biological Sciences