Artificial Neural Networks for Construction Project Cost and Duration Estimation

Abstract

Construction projects are inherently distinguished by their unique durations and associated costs, factors which are pivotal in determining project efficiency and quality, and consequently impacting broader societal development. The inherent unpredictability intrinsic to these projects presents significant challenges in completing them within established schedules and budgets. This unpredictability stems largely from the distinct nature of building operations, rendering the task of construction management analysis multifaceted and complex. In addressing these challenges, the present study explores the influence of architectural building specifics on the cost and duration estimations of construction projects through the application of artificial neural networks (ANNs). Recognized for their robust capacity to generalize from complex input-output relationships within extensive datasets, ANNs were employed to analyze a database incorporating six input variables: the number of activities, the total area of the building, the type of foundation, the number of storeys, the classification of consumers and vendors, alongside two output variables, namely cost and duration. The findings reveal that construction projects entrusted to individual or small-scale contractors are more susceptible to fluctuations in cost and duration compared to those managed by larger or multi-company contractors. The selection process for contractors was significantly influenced by the factors of bidding cost and negotiation fees, with clients possessing higher financial resources more frequently opting for larger companies. The research incorporated the development of a sophisticated intelligent model in MATLAB, utilizing a feed-forward back-propagation network for analysis. The efficacy of the ANN model was rigorously evaluated against statistical benchmarks, focusing on loss-function parameters. A strong correlation was unveiled between the ANN model predictions and the empirical data, as evidenced by an exemplary average coefficient of determination (R2) of 0.99995, markedly outperforming the multiple linear regression (MLR) model, which yielded a result of 0.6986. Additional performance metrics, including the mean absolute error (MAE) of 0.2952 and the root mean square error (RMSE) of 0.5638, attested to the model's robustness. Through the implementation of this research, a significant contribution is made towards enhancing the precision of resource and time estimations for clients and contractors undertaking construction projects, while concurrently accounting for the principal constraining factors.