Pregled bibliografske jedinice broj: 839282
Model for Predicting Construction Time by Using General Regression Neural Network
Model for Predicting Construction Time by Using General Regression Neural Network // Book of Abstracts-People, Buildings and Environment 2016. / Korytarova, Jana ; Serrat Carles ; Hanak, Tomaš ; Vankova, Lucie (ur.).
Brno: Brno: Brno University of Technology ; Faculty of Civil Engineering, 2016. str. 31-31 (predavanje, međunarodna recenzija, sažetak, znanstveni)
CROSBI ID: 839282 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Model for Predicting Construction Time by Using General Regression Neural Network
Autori
Petruševa, Silvana ; Car-Pušić, Diana ; Žileska Pancovska, Valentina
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Izvornik
Book of Abstracts-People, Buildings and Environment 2016.
/ Korytarova, Jana ; Serrat Carles ; Hanak, Tomaš ; Vankova, Lucie - Brno : Brno: Brno University of Technology ; Faculty of Civil Engineering, 2016, 31-31
ISBN
978-80-214-5408-8
Skup
International Scientific Conference "People, Buildings and Environment 2016"
Mjesto i datum
Luhačovice, Češka Republika, 29.09.2016. - 01.10.2016
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Construction time; DTREG software; General Regression Neural Network; predicting
Sažetak
Construction time is an element of every construction contract. Thus, its prediction is of particular interest. This paper presents a construction time prediction model by using General Regression Neural Network. Key data on a total of 70 constructed buildings have been collected through field studies. Chief engineers of construction companies have been interviewed on contractual and actually incurred construction times, contractual and actual construction costs, type of facilities and construction year. General Regression Neural Network (GRNN) from predictive modelling software named DTREG, as new approach in forecasting, was used for building the predictive model to predict the real construction time. Prediction was very accurate with mean absolute percentage error, MAPE, around 2.19 which means that the error of the model is around 2.19%, the coefficient of correlation between the actual and the predicted values is very high, r = 0.99 (rounded) and the coefficient of determination which measures the global fit of the model R2 is 0.97875 (or 97.88%). This paper contributes to and can be useful for the decision process on planning the construction time in construction companies and in the construction industry in general.
Izvorni jezik
Engleski
Znanstvena područja
Građevinarstvo
