Modeling the proportion of cut slopes rock on forest roads using artificial neural network and ordinal linear regression

Abstract

Rock proportion of subsoil directly influences the cost of embankment in forest road construction. Therefore, developing a reliable framework for rock ratio estimation prior to the road planning could lead to more light excavation and less cost operations. Prediction of rock proportion was subjected to statistical analyses using the application of Artificial Neural Network (ANN) in MATLAB and five link functions of ordinal logistic regression (OLR) according to the rock type and terrain slope properties. In addition to bed rock and slope maps, more than 100 sample data of rock proportion were collected, observed by geologists, from any available bed rock of every slope class. Four predictive models were developed for rock proportion, employing independent variables and applying both the selected probit link function of OLR and Layer Recurrent and Feed forward back propagation networks of Neural Networks. In ANN, different numbers of neurons are considered for the hidden layer(s). Goodness of the fit measures distinguished that ANN models produced better results than OLR with R (2) = 0.72 and Root Mean Square Error = 0.42. Furthermore, in order to show the applicability of the proposed approach, and to illustrate the variability of rock proportion resulted from the model application, the optimum models were applied to a mountainous forest in where forest road network had been constructed in the past.

Metric measurements for optimization of forest road network alternatives in GIS-based programs

“…Six road alternatives were designed using a road-pegging tool (PEGGER) on integrated map from soil, slope, aspect, stock volume, and etc factors (Najafi et al 2008). AHP was then used to evaluate the construction cost of the alternatives (Babapour et al 2015). Babapour et al (2006) applied GIS and PEG-GER to produce a stability map and plan a new road network, respectively.…”

Computation and Analysis of High Rocky Slope Safety in a Water Conservancy Project

“…The study of a high slope has become very popular in the geotechnical engineering field [2][3][4]. Mathematical methods, such as neural network and time series, became a popular way to analyze the monitoring data (see Babapour et al [5], Abdalla et al [6], Zevallos and Hotta [7], Vinoth et al [8], and the references therein). The finite element method (FEM) of numerical simulation technology was also widely used for geotechnical engineering (see Chang et al [9], Alemdag et al [10], and Vasilev et al [11]).…”