Periodical Maintenance of Forest Roads with a Mobile Stone Crusher

Abstract

Forest road networks are exposed to damage by traffic, climate, timber harvesting and vegetation. To maintain their functionality, they must be maintained regularly. Periodical maintenance is required when the forest road surface layer is deteriorated and eroded. Well-graded material is required for replacing the forest road surface and often has to be sourced from gravel storage areas, which is costly and requires a large number of truck trips. Therefore, converting non-graded aggregate available on site into well-graded aggregate with a mobile stone crusher is considered a viable alternative.The present study was carried out during a periodical maintenance treatment at the Bavarian State Forest Enterprise and the effect of employing a mobile stone crusher was evaluated with regard to (1) forest road load bearing capacity development during a one-year period post-treatment, (2) particle size distribution of the surface layer material before and after crushing, and (3) its cost compared to other alternatives. Samples were collected pre- and post-operation for particle size distribution analysis, load bearing capacity was measured repeatedly with a light falling weight deflectometer and compared to an untreated reference section and cost of the treatment was compared to two alternatives.The mobile stone crusher was capable of reducing the non-graded to well-graded/close-to-well-graded material and particle size distributions aligned well with the recommendations for lime-water bonded surfaces. Load bearing capacity exceeded the threshold of 40 MN m-2 (Evd, elastic modulus dynamic) for primary forest roads at all times. It increased significantly after the treatment and remained on a significantly higher level throughout the following year. Absolute and relative increases were higher than on the untreated reference section. The treatment variant involving a mobile stone crusher and material available on site was substantially cheaper (5.31 € m-1) than to supply non-graded (16.29 € m-1) or well-graded (19.82 € m-1) material by truck. Material and transport costs represented 67% and 82% of the total costs in the latter two cases. It can be concluded that mobile stone crushers are capable of producing at least close-to-well-graded forest road surface aggregate and that forest road load bearing capacity can be significantly and lastingly increased at only a part of the costs of the alternatives. A maximum of cost and resource efficiency and environmental soundness can be attained when enough surface aggregate is available on site. If this is not the case, sourcing non-graded material as local as possible is the next best alternative.

Evaluating the Impact of Meteorological Data Sources on Moisture Prediction Accuracy of Eucalyptus Nitens Log Pile Natural Drying Models

“…The E. nitens log piles in the Bown and Lasserre (2015) trial had a similar high initial drying rate to that of the current study log pile; however, the summer component of their study was too short to observe any MC stabilisation. General drying patterns and total moisture loss (~20%) for log piles of a number of species in Austria (Erber et al 2012, Erber et al 2016, Erber et al 2017 were also similar to those for the log pile in the current study. Erber et al (2012) noted that drying forest biomass to an MC of <35% may attract a premium sale price.…”

“…For research purposes, the model developed from the portable weather station data may be the most appropriate as it had the best statistical fit and deployment of portable weather stations to collect data for the model would be feasible for a research trial. However, forest managers are unlikely to be able to justify the cost to purchase, deploy and maintain portable weather stations across a commercial forest estate (Erber et al 2017). Drying models developed from BOM meteorological data would be more appropriate in these cases as the data required by the model are readily available online.…”

“…Most roadside log and forest biomass drying models have been derived from weather variables recorded using onsite weather stations, whereas operational application of the drying models typically relies on data from the nearest meteorological station which may be ≥10 km from the stored material, particularly in forested areas. Given that weather variables can vary considerably temporally and spatially (Erber et al 2017, Grimmond et al 2000, Ma et al 2010, insufficient research has been conducted to evaluate the impact of different sources of meteorological data on the accuracy of MC prediction of log and forest biomass piles drying at roadside and the implications of each data source on practical implementation of the drying models.…”