Forecasting the Final Contract Cost on the Basis of the Owner's Cost Estimation Using an Artificial Neural Network

Raising the final contract cost (FCC) is a significant risk for project owners. This study hypothesizes that the factors that cause owner's cost estimation (OCE) accuracy and FCC changes share the same causes, and a case study confirmed that the two variables (OCE and FCC) could be correlated. Accordingly, this study aims to develop a forecast model to predict FCC on the basis of the initial OCE, which has not been studied previously. This study utilized data from 34 Saudi Arabian projects. Two linear regression models developed the data, and the square root function transformed the data. Moreover, the artificial neural network (ANN) model was developed after data standardization using Zavadskas and Turskis' logarithmic method. The results showed that the ANN model had a MAPE smaller than the two linear regression models. Using Zavadskas and Turskis' logarithmic standardization method and elimination of data that had an absolute percentage error (APE) of more than 35% led to an increase in ANN model accuracy and provided a MAPE value of less than 8.5%.

Automated summary

This study investigates the correlation between owner's cost estimation (OCE) accuracy and changes in the final contract cost (FCC). Through a case study, it is confirmed that the two variables are correlated. The study aims to develop a forecast model to predict FCC based on the initial OCE, which has not been previously studied. Utilizing data from 34 Saudi Arabian projects, linear regression models and an artificial neural network (ANN) model were developed. The results show that the ANN model outperforms the linear regression models in terms of accuracy.