Open AccessModel for predicting cost of rural road projects in Thailand
Author(s) -
Wai Phyo Lin,
Wasaporn Techapeeraparnich
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/652/1/012004
Subject(s) - cost estimate , toolbox , process (computing) , computer science , estimation , regression analysis , parametric statistics , matlab , conceptual model , predictive modelling , data mining , engineering , machine learning , systems engineering , database , statistics , mathematics , programming language , operating system
Nowadays, construction cost plays an essential role in various projects, which are buildings, roads, railways, and bridges projects. Conceptual cost estimation in feasibility study is require high accuracy and less validation error especially in construction projects at early stages. The more improvement of estimation techniques, it would lead to the lesser problems of cost overrun, and extra expenses. This paper developed a cost estimation model for new constructed rural road projects. Considering the estimation methods for predicting the cost model, parametric method based on regression learner and NN method are applied. Previously, many researchers studied the cost applicable model by using various computer applications, so that this paper differed to compare these methods based on MATLAB. Accordingly, 44 road projects were compiled from DRR database, after that identifying the effective cost parameters referred on collected data. Subsequently, the data implementation process was focusing with regression learner based on automated regression training, and lastly, with NN toolbox concentrated on nftool for simulating the cost models. Another 7 road projects are tested with holdout validation process for these models. Due to this validation, we compare the predicted cost models within these methods. Finally, the developed models are reliable for not only the conceptual phase of future rural road projects but also the related construction fields can be recovered about the cost model creation.