Naslov
Neural Network-Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System

Autori
Car-Pušić, Diana ; Petruševa, Silvana ; Žileska Pancovska, Valentina ; Zafirovski, Zlatko

Izvornik
Advances in civil engineering (1687-8086) 2020 (2020); 1-13

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
construction costs, predicting, CPS, neural network, building management information system (BMIS)

Sažetak
A model for early construction cost prediction is useful for all construction project participants. This paper presents a combination of process based and data driven model for construction cost prediction in early project phases. Bromilow’s “time-cost” model is used as process based, and general regression neural network (GRNN), as data driven model. GRNN gave the most accurate prediction among three prediction models using neural networks which were applied, with the mean absolute percentage error (MAPE) of about 0.73% and the coefficient of determination R2 of 99.55%. The correlation coefficient between the predicted and the actual values is 0.998. The model is designed as an integral part of the Cost Predicting System (CPS), whose role is to estimate project costs in the early stages. The obtained results are used as Cost Model (CM) input being both part of the Decision Support System (DSS) and part of the wider Building Management Information System (BMIS). The model can be useful for all project participants to predict construction cost in early project stage, especially in the phases of bidding and contracting when many factors, which can determine the construction project implementation, are yet unknown.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo

POVEZANOST RADA