Implementation of Artificial Neural Networks to Forecast the cost of Road Projects

  Amit Sanjay Waikar,  Prof. Trupti V. Kulkarni

Estimation of the cost of road construction projects is an important task in the management of these projects. Accurate cost estimation is key element for the quality of construction management. For proper functioning of any construction company, cost Estimating is one of the most significant aspects. Construction costs prediction becomes very difficult as well as sophisticated task especially when using manual calculation methods. This paper contains Artificial Neural Network (ANN) approach to develop a parametric cost-estimating model for site cost estimation. An ANN (artificial neural network) is an analogy-based process, which is suitable for the cost forecasting domain. The primary advantages of ANNs include their ability to learn by examples (past projects), and to generalize solutions for forthcoming applications (future projects). Data used in the study are for road projects from different sites in Pune. Data is collected from predominantly primary sources using real-life data contained in project files, with some data obtained from the road project Cost Information Service, supplemented with further information, and some from a questionnaire. These are used in training the model and evaluating its performance. MATLAB software is used for artificial neural network preparation. The neural network architecture is presented for the estimation of the costs as a percentage from the total project price.

Keywords- ANN, MATLAB, Hidden Layers, Output Layer, Input Layer

Unique Identification Number - IJEDR1904116
Page Number(s) - 705-710
Pubished in - Volume 7 | Issue 4 | December 2019
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Amit Sanjay Waikar,  Prof. Trupti V. Kulkarni,   "Implementation of Artificial Neural Networks to Forecast the cost of Road Projects", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.7, Issue 4, pp.705-710, December 2019, Available at :http://www.ijedr.org/papers/IJEDR1904116.pdf