Late Payments for Contractors Working for Bahrain Government Building Construction Projects: Part II (Modelling Using Artificial Neural Networks and Regression)
Abstract: The problem of late payment is considered one of the major issues in the construction industry, it is an important issue faced by many countries including Kingdom of Bahrain, and it has many consequences on building construction projects. The main objectives of this research is to create a prediction model to predict the payment delay in days for Interim payments (Model I) and variation payments (Model V) for Ministry of Works (MoW) building construction projects. The main factors of payment delay were identified in Part I of the research and used for the development of models using artificial neural network (ANN) and multi linear regression analysis (MLRA). Finally, the study compared the ANN approach to MLRA and concluded that the estimation accuracy of ANN approach is better than MLRA analysis for payment delay in (days) as it showed more promising results. The best ANN for Model I and Model V were found to be Model I-48 and Model V-60, respectively.
Keywords: Interim payment delay, Variation payment delay, artificial neural network, multi linear regression analysis
How to Cite?: Sahar K. A. Falamarzi, Saad M. A. Suliman, "Late Payments for Contractors Working for Bahrain Government Building Construction Projects: Part II (Modelling Using Artificial Neural Networks and Regression)", Volume 7 Issue 3, March 2018, International Journal of Science and Research (IJSR), Pages: 700-723, https://www.ijsr.net/getabstract.php?paperid=ART2018737, DOI: https://dx.doi.org/10.21275/ART2018737