Najafi Akbar, PhD. Asst. Prof.

The accurate prediction of forest road pavement performance is important for efficient management
of surface transportation infrastructure and achieves significant savings through timely
intervention and accurate planning. The aim of this paper was to introduce a methodology
for developing accurate pavement deterioration models to be used primarily for the management
of the forest road infrastructure. For this purpose, 19 explanatory and three corresponding
response variables were measured in 185 segments of 50 km forest roads. Logistic regression
(LR) and artificial neural networks (ANNs) were used to predict forest road pavement
deterioration, Pothole, rutting and protrusion, as a function of pavement condition, environmental
factors, traffic and road qualify. The results showed ANNs and LR models could classify
from 82% to 89% of the current pavement condition correctly. According to the results,
LR model and ANNs predicted rutting, pothole and protrusion with 83.5%, 83.00% and
81.75%, 88.65% and 85.20%, 80.00% accuracy. Equivalent single axle load (ESAL), date of
repair, thickness of pavement and slope were identified as most significant explanatory variables.
Receiver Operating Characteristic Curve (ROC) showed that the results obtained by
ANNs and logistic regression are close to each other.

This study was done to determine the appropriate maintenance strategies for the deteriorating gravel forest roads in the Mediterranean, sub-humid and semi-arid climates. Unmanned Aerial Vehicle (UAV) was used to monitor Unpaved Road Condition Index (UPCI), immediately after maintenance activities and seasonally in one year. The deterioration time of the wearing course was predicted using Markov chain analysis. Results showed that roads in sub-humid climates presented lower UPCI (7.19) compared to the Mediterranean (7.81) and semi-arid (8.82) climates. When roads were maintained by a high-budget strategy, deterioration time was longer than when other strategies were used. The cost-effectiveness (CE) value of the low-budget strategy was more favorable than different strategies in all traffic levels of the Mediterranean climate and high-traffic roads in a semi-arid environment. Low-budget maintenance activities include one culvert improvement per 6 km, light blading, and 30 mm layer graveling. In a semi-arid climate, a medium-budget maintenance strategy was more efficient in medium and low-traffic roads. Medium, high, and low-budget maintenance strategies were efficient in high, medium, and low-traffic roads in sub-humid climates. High-budget maintenance activities include one culvert improvement per 4 km, heavy blading and local compaction, and 60 mm layer graveling. Overall, it was concluded that monitoring UPCI over time and probability analysis using time series is helpful for a sustainable and long-term management of forest roads.