By Jurg Andreas Stuckelberger
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Extra resources for A Weighted-graph Optimization Approach for Automatic Location of Forest Road Networks
This is especially true for mountainous terrain and in areas with unstable subsoil. Here, we present a model for more accurately estimating spatial variability in road life-cycle costs, based on terrain surface properties as well as geological properties of the subsoil. This parametric model incorporates four structural components: embankment, retaining structures, pavement, and drainage and stream-crossing structures. It is linked to a geodatabase that allows users to derive location-speciﬁc parameter values as input.
Loosening and loss of ﬁll-slope material was assessed with a shrinking factor (fshr ) that depended on subsoil geotechnical properties. 3: Standard design cross-section of low-volume road. Acut : cut-slope area; Af ill : ﬁll-slope area; hcut : cut-slope height; hf ill : ﬁll-slope height, uphill side; wcut : road width, uphill side; wf ill : road width, downhill side; η: slope angle, depending on terrain surface; φcut : cut angle, depending on geotechnical properties; and φf ill : ﬁll angle, depending on geotechnical properties.
Scenario I has two connections from lake level to high level (BRH-SBU-ROW-EGS and AU-ALP-STO). Because the costs are route independent, the model tried to keep the road network at minimal length. Both eﬀects resulted in a lot of switchbacks and therefore high life-cycle cost. Scenarios II and III shows nearly identical road routes in 000-BRH-SBU-AU and EGS-ROW-TAS. However, Scenario II connects the high level via access points AU-SBU-STO in less stable subsoil where as Scenario III made a connection via AU-OBO-ALP-STO in limestone layer, which is stable and therefore favorable.
A Weighted-graph Optimization Approach for Automatic Location of Forest Road Networks by Jurg Andreas Stuckelberger