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

Diana Car-Pusic; Silvana Petruseva; Valentina Zileska Pancovska; Zafirovski Zlatko

doi:https://doi.org/10.1155/2020/8886170

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

Journal

Advances in Civil Engineering

Date Issued

2020-09

Author(s)

Diana Car-Pusic

Silvana Petruseva

Valentina Zileska Pancovska

Zafirovski Zlatko

DOI

https://doi.org/10.1155/2020/8886170

Abstract

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 model 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.

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