Transportation systems, characterized by extremely heavy logging trucks running on low standard roads, are critical to Canadian woodlands operations. Because predicting logging vehicle performance is essential to planning efficient forest road transportation systems, a Heavy Vehicle Performance Model (HVPM) was developed which takes into account the characteristics of forest road transportation system components in order to predict vehicle performance parameters such as speed and fuel consumption.
Two independent field test data sets were used to verify the HVPM. Verification results showed that the average speed predicted by the HVPM was as much as 17 percent lower than the average observed speed, while predicted fuel consumption was as much as 20 percent higher than the field observations.
Implementation of the HVPM is presented by showing how it is used to solve three forest road transportation problems: a practical application is given in a case study comparing the performance of two specified trucks on two proposed road alignments, the selection of truck components using vehicle performance predictions from the HVPM is illustrated, and the HVPM is used to predict truck performance on different classes of roads.
Cable yarding is an important means for primary extraction of timber on steep slopes and/or sensitive soils throughout the world. Comprehensive planning for cable yarding requires detailed production and cost estimates which can be made using production equations. Such equations can come from the literature or independent time studies. Both options depend on previously published studies either for direct use or to improve study design. An elemental time study was designed and used in an investigation of tower yarding in coastal British Columbia. A comprehensive statistical analysis was applied to the data including stratification, fitting regression equations, and hypothesis testing. The findings include a library of production equations applicable over a wide range of operating conditions.
Unlike the traditional way of forest road planning in which the forest engineer manually tries to find alternative routes to access areas scheduled for harvest, a computerized method using data from a digital terrain model is presented. The method identifies feasible road segments, evaluates their variable and fixed cost components and then determines the optimal set of road segments to be used and the year in which the roads are to be constructed.
The history and development of log grapples used in China and their working parameters are given. An analysis is made of the main technical features. Results show that the electro-mechanical log grapple, driven by an electric drum, is the most suitable for the special working conditions found in Chinese forestry at the present time.
Over 200 commercially built chain flail delimber-debarkers are now in operation worldwide. These units, teamed with in woods chippers, are producing chips acceptable for pulping from many species of hardwoods and softwoods. The flails can remove the bark as well as drum debarkers in the case of southern pine species. The chips produced by these portable operations have been shown to be equal in quality to the chips produced at mill and satellite wood yards. It has been estimated that the flail-chipper system will produce up to 2.9% more clean chips than are obtained with conventional longwood harvesting and handling systems. The flails have been used to remove rot, foliage, and charcoal in specialized applications. The rejects from the flail represent a readily recoverable source of energy material, but this debris must be reduced in size to facilitate handling. Developments for reducing the size of the rejects are ongoing, especially using modified agricultural tub grinders. Chains are a major cost in the operation of the flails. Strategies have been developed which can prolong the life of the chains, and tests are ongoing with improved materials in the manufacturing of the chains. The cost of delimbing and debarking with the portable flails has been estimated to be between $(US)0.60and $(US)3.30 per green tonne.