• Neha Bhaskar
• Sonam Yadav

The sole purpose of construction planning management is to reduce the time taken in the completion of project and to complete the project under allocated budget which makes extremely important to know beforehand the expected budget. This study has been undertaken to investigate the key factors affecting the construction of projects and estimation of total construction costs of building projects in India with the help of artificial neural networks (ANN). Twenty six data sets of completed building projects or approved for funding were obtained and processed for training and estimation of total construction costs. Altogether twelve key building construction factors were identified covering areas related to the building materials, building specifications i.e. area of typical floor, number of floors, number of staircases, number of rooms etc. Based on these factors, for estimation of total construction costs of building projects, a model was established. MATLAB software was used to train the network and NEURAL DESIGNER software was used to test the reliability of ANN in estimation of total construction costs. The best network was found to having architecture of 12-7-1. The network can forecast construction costs for building projects at pre-construction stage having an accuracy of coefficient of correlation (R) of 99.71% and a mean absolute percentage error of 2.34%. The average accuracy percentage of 97.66% was achieved

Building Construction Projects, Cost Factors, Cost Estimation, Total Construction Costs, Artificial Neural Network (ANN)

"Cost Estimation of Building Projects Using Artificial Neural Network (ANN)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.769-775, June-2020, Available :http://www.jetir.org/papers/JETIR2006445.pdf

"Cost Estimation of Building Projects Using Artificial Neural Network (ANN)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp769-775, June-2020, Available at : http://www.jetir.org/papers/JETIR2006445.pdf