Version 1
: Received: 13 July 2022 / Approved: 15 July 2022 / Online: 15 July 2022 (14:55:58 CEST)
How to cite:
Heidari, M.J.; Najafi, A.; Borges, J.G. Introducing New Index in Forest Roads Pavement Management System. Preprints2022, 2022070238. https://doi.org/10.20944/preprints202207.0238.v1
Heidari, M.J.; Najafi, A.; Borges, J.G. Introducing New Index in Forest Roads Pavement Management System. Preprints 2022, 2022070238. https://doi.org/10.20944/preprints202207.0238.v1
Heidari, M.J.; Najafi, A.; Borges, J.G. Introducing New Index in Forest Roads Pavement Management System. Preprints2022, 2022070238. https://doi.org/10.20944/preprints202207.0238.v1
APA Style
Heidari, M.J., Najafi, A., & Borges, J.G. (2022). Introducing New Index in Forest Roads Pavement Management System. Preprints. https://doi.org/10.20944/preprints202207.0238.v1
Chicago/Turabian Style
Heidari, M.J., Akbar Najafi and Jose G. Borges. 2022 "Introducing New Index in Forest Roads Pavement Management System" Preprints. https://doi.org/10.20944/preprints202207.0238.v1
Abstract
Forest road pavement needs an evaluation methodology based on a comprehensive assessment of road conditions. This research was conducted to evaluate the performance of a method for rating the surface condition of forest roads and eventually to adapt the method to the situation prevailing in a forest road network. The rating method selected as the basis for this experiment was the pavement condition index (PCI) developed by the U.S. Army Corps of Engineers. A 53 km of forest roads were selected contained the most influential factors and conditions variability. Eventually, 201 road segments were delineated between 150-300 m in length. Within the given segments, sample plots were set 20 m in length consecutively. It was concluded that the panel scores for distress and surface condition of sample unit and section differed from forest road pavement condition index (FRPCI) and PCI. Linear regression was used to derive equations between distress and PCI score to determine effective FRPCI parameters that provide a numerical rating for the condition of road segments within the road network, where 0 worlds are the worst possible condition, and 100 is the best possible condition best. Also, regression analysis showed the FRPCI model with a 0.87 correlation for the total of the road is a performance index used for the first time in forest roads. This study showed a range of FRPCI from 7.8 to 96.3, different from PCI and URCI ratings (0.85-45 and 1.2-53). The FRPCI index helps forest managers in road maintenance, harvesting, and planning to use road information.
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
