Cost Forecasting of Public Construction Projects Using Multilayer Perceptron Artificial Neural Networks: A Case Study

• Main Authors: Alcineide Pessoa, Gean Sousa, Luiz Maués, Felipe Alvarenga, Débora Santos
• Format: Article
• Language: English
• Published: Universidad Nacional de Colombia 2021-06-01
• Series: Ingeniería e Investigación
• Subjects: costs; artificial neural network; public undertakings
• Online Access: https://revistas.unal.edu.co/index.php/ingeinv/article/view/87737

The execution of public sector construction projects often requires the use of financial resources not foreseen during the tendering phase, which causes management problems. This study aims to present a computational model based on artificial intelligence, specifically on artificial neural networks, capable of forecasting the execution cost of construction projects for Brazilian educational public buildings. The database used in the training and testing of the neural model was obtained from the online system of the Ministry of Education. The neural network used was a multilayer perceptron as a backpropagation algorithm optimized through the gradient descent method. To evaluate the obtained results, the mean absolute percentage errors and the Pearson correlation coefficients were calculated. Some hypothesis tests were also carried out in order to verify the existence of significant differences between real values and those obtained by the neural network. The average percentage errors between predicted and actual values varied between 5% and 9%, and the correlation values reached 0,99. The results demonstrated that it is possible to use artificial intelligence as an auxiliary mechanism to plan construction projects, especially in the public sector.