Machine Learning Modeling of Forest Road Construction Costs

Forests

Iman Pazhouhan¹  Abolfazl Jaafari²  Pete Bettinger³ 

[1] Department of Range and Watershed Management, Natural Resource and Environment Faculty, Malayer University, Malayer 6571995863, Iran;Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran 1496813111, Iran;Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA;

关键词: forest roads;    road construction industry;    cost estimation;    machine learning;    Hyrcanian forests;   

DOI  :  10.3390/f12091169

来源: DOAJ

【 摘 要 】

The economics of the forestry enterprise are largely measured by their performance in road construction and management. The construction of forest roads requires tremendous capital outlays and usually constitutes a major component of the construction industry. The availability of cost estimation models assisting in the early stages of a project would therefore be of great help for timely costing of alternatives and more economical solutions. This study describes the development and application of such cost estimation models. First, the main cost elements and variables affecting total construction costs were determined for which the real-world data were derived from the project bids and an analysis of 300 segments of a three kilometer road constructed in the Hyrcanian Forests of Iran. Then, five state-of-the-art machine learning methods, i.e., linear regression (LR), K-Star, multilayer perceptron neural network (MLP), support vector machine (SVM), and Instance-based learning (IBL) were applied to develop models that would estimate construction costs from the real-world data. The performance of the models was measured using the correlation coefficient (R), root mean square error (RMSE), and percent of relative error index (PREI). The results showed that the IBL model had the highest training performance (R = 0.998, RMSE = 1.4%), whereas the SVM model had the highest estimation capability (R = 0.993, RMSE = 2.44%). PREI indicated that all models but IBL (mean PREI = 0.0021%) slightly underestimated the construction costs. Despite these few differences, the results demonstrated that the cost estimations developed here were consistent with the project bids, and our models thus can serve as a guideline for better allocating financial resources in the early stages of the bidding process.

【 授权许可】

Unknown