Open AccessCost Forecasting of Public Construction Projects Using Multilayer Perceptron Artificial Neural Networks: A Case Study
Author(s) -
Alcineide Dutra Pessoa De Sousa,
Gean Carlos Lopes de Sousa,
Luiz Maurício Furtado Maués,
Felipe Campos Alvarenga,
Débora de Gois Santos
Publication year - 2021
Publication title -
ingeniería e investigación/ingeniería e investigación
Language(s) - English
Resource type - Journals
eISSN - 2248-8723
pISSN - 0120-5609
DOI - 10.15446/ing.investig.v41n3.87737
Subject(s) - artificial neural network , backpropagation , procurement , artificial intelligence , multilayer perceptron , computer science , public sector , machine learning , gradient descent , plan (archaeology) , christian ministry , correlation coefficient , engineering , business , geography , economics , philosophy , economy , theology , archaeology , marketing
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.