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.
Highlights
E authors in [19] state that cost overrun is more a rule than an exception
The proposed cost predicting system (CPS) ontology integrates different models of cost predicting, this paper focuses on neural networks (NNs) due to their specific characteristics and capabilities identified by previous research [7, 8, 10,11,12, 33, 40, 41]
The CPS uses historical data on implemented projects and a database of appropriate parameters, and on the other hand, several models of cost prediction are based on intelligent prediction techniques. ese techniques have already been tested in solving various problems of the construction industry. e paper presents CPS ontology with the indicated basic components. e NNs are singled out as especially suitable. e reasons are explained in detail
Summary
E authors in [19] state that cost overrun is more a rule than an exception. according to the reports from the World Bank in 2007, road construction in India suffers about 25% of contracted price overrun [21]. According to the research conducted in China [22], where various types of reconstructed structures were considered, the construction contracted price overrun of more than 10% was recorded at 26.39% of the structures and 5–10% at 55.56% of the structures. In Slovenia, a research was conducted on a sample of 92 traffic structures built in the period from 1993 to 1998. 93 structures were analyzed and the cost overrun was recorded in 21 or 22.58% of structures [24]. Within the scientific research project conducted in Croatia [25], 333 structures were investigated in the period from 1996 to 1998. Price overrun at 81% of structures was recorded. A similar research was conducted in Bosnia and Herzegovina on 177 structures built from 1995 till 2006. It can be concluded that construction cost overrun is present in underdeveloped countries and developing countries and in developed countries. is was confirmed by Baloi and Andrew [19], stressing that “... in most developing countries ... the problem is more acute.” Reasons are surely multifaceted and multilayer and deserve a deeper analysis of the issue
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