An Adaptive Network-based Fuzzy Inference System for Rock Share Estimation in Forest Road Construction

Abstract

This paper presents a new Rock Share Estimation (RSE) procedure that can estimate the cost of forest road construction. One of the key elements of the total cost in road construction is the cost of embankment. The proportion of the rock directly influences the price of this activity. Hence, a reliable estimation of rock proportion should be made within the entire project area, especially in rocky areas. The objective of the study is to introduce a practical expert system to estimate the share of rock as a function of terrain slope and geological formations using the Adaptive Network – based Fuzzy Inference System (ANFIS) and Analytic Hierarchy Process (AHP). This approach can be very useful first to show the variability of rock proportion and second to model the excavation costs in an area, which are essential for planning forest roads. This study treats geological composition as a decision variable that is solved by AHP method and applies the ANFIS to model and predict the share of rock in different physiographic and geological conditions. In order to investigate the impact of change in membership functions (MF), four types of MFs were adopted to generate the hybrid RSE–ANFIS models. Further­ more, to show the applicability of the proposed approach, the optimum model was applied to a mountainous forest, where additional forest road network should be constructed in the future periods.