Prediction of soil-bearing capacity on forest roads by statistical approaches.

The soil-bearing capacity is one of the important criteria in dimensioning the superstructure. In Turkey, predictability of California Bearing Ratio values, which may be used in the planning and dimensioning of forest roads, of which about 26% lacks the superstructure, by using soil mechanical properties (cost and time efficient parameters that are easier to determine) is investigated. Simple linear regression, multiple linear regression, artificial neural networks and adaptive network-based fuzzy inference system methods were utilized. Two hundred sixty-four California Bearing Ratio values obtained from the project carried out on the forest roads of Bartin Forest Operation Directorate were used in both the production of training-test data and the creation of models. Statistical performance of the models was assessed by means of parameters such as root-mean-square error, mean absolute error and R2. The obtained results show that the bearing capacity values predicted by artificial neural networks and adaptive network based fuzzy inference system models display significantly better performance than the simple linear regression and multiple linear regression models. While the highest prediction capacity belongs to adaptive network based fuzzy inference system (0.969-0.991), it is followed by artificial neural networks (R2 = 0.796-0.974), multiple linear regression (R2 = 0.796) and simple linear regression (R2 = 0.554). What makes the algorithms superior than the traditional statistical models is the fact that they have many processing neurons, each with local connections, and thus have higher error tolerance. On the other hand, for the forest and rural roads, which play an important role in rural development of the forest peasants, to be able to operate all-seasons, superstructure should be immediately built in order to minimize the wear on the roads.

The assessment of environmentally sensitive forest road construction in Calabrian pine forest areas of Turkey.

Forest road construction by bulldozers in Calabrian Pine (Pinus brutia Ten.) forests on mountainous terrain of Turkey causes considerable damage to the environment and the forest standing alongside the road. This situation obliges a study of environmentally sound road construction in Turkey. This study was carried out in 4 sample sites of Antalya Forest Directorate in steep (34-50% gradient) and very steep terrain (51-70% gradient) conditions with bulldozer and excavator machine and direct damages to forest during road construction was determined, including forest area losses and damages to downhill trees in mountainous areas. It was determined that in steep terrain when excavators were used, less forest area (22.16%) was destroyed compared to bulldozers and 26.54% less area in very steep terrain. The proportion of damage on trees where bulldozer worked was nearly twofold higher than excavator was used. The results of this research show that the environmentally sensitive techniques applied for the road construction projects are considerably superior to the traditional use of bulldozers on steep slopes. The environmentally sound forest road construction by use of excavator must be considered an appropriate and reliable solution for mountainous terrain where areas of sensitive forest ecosystems are to be opened up.