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.

The need of respecting the construction time as one of the construction contract elements points out that early prediction of construction time is of crucial importance for the construction project participants’ business. Thus, having a model for early prediction of construction time is useful not only for the participants involved in the construction contracting process, but also for other participants in the construction project realization. Regarding that, this paper aims to present a hybrid method for predicting construction time in the early project phase, which is a combination of process‐based and data‐driven models. Five hybrid models have been developed, and the most accurate one was the BTC‐GRNN model, which uses Bromilow’s time‐cost (BTC) model as a process‐based model and the general regression neural network (GRNN) as a data‐driven model. For evaluating the quality of the models, the 10‐fold cross‐validation method has been used. The mean absolute percentage error (MAPE) of the BTC‐GRNN is 3.34% and the coefficient of determination R², which reflects the global fit of the model, is 93.17%. These results show a drastic improvement of the accuracy in comparison to the model when only data‐driven model (GRNN) has been used, where MAPE was 31.8% and R² was 75.64%. This model can be useful to the investors, the contractors, the project managers, and other project participants for construction time prediction in the early project phases, especially in the phases of bidding and contracting, when many factors, that can determine the construction project realization, are unknown.

Time and price of contracted construction as well as their overrun are among highly pronounced issues in the construction practice. Numerous studies indicate that there is a dependency between these parameters at various construction markets. The paper shows the results of a research conducted on a group of 40 projects related to water supply systems carried out in the Federation of Bosnia and Herzegovina from 2001 to 2012. Collected data, obtained through interviewing chief engineers of construction companies, have been used as input parameters for implementation of single linear regression by applying the BTC (Bromilow time-cost) algorithm. The final result is a model in the form of an exponential equation. Whereas it was obtained based on effectuated values, it can be considered appropriate for assessing and testing the construction time in the early planning stage, as well as at work contracting. Since the parameter values in the model depend on circumstances of the specific area under consideration, the application of the model is recommended primarily in the area where the research was conducted. Results of the price overrun and construction overrun interdependence have not resulted in determining the exponential model. The paper also proposes application recommendations and guidelines for further research.

Cost prediction in early stages of construction projects is one of the crucial problems of project sustainability. Previous research has been aimed at process based and data driven model development by using various techniques, e.g. regression analysis, support vector machine (SVM), neural networks etc. According to the research results, neither of the techniques can be considered the best for all circumstances. Therefore, the research has been redirected towards hybrid modelling, i.e. combination of different techniques. In this research, for prediction of the target variable-real construction cost of road structures, available variables: contracted construction cost, contracted construction time and real construction time and cost, hybrid model-combination of SVM technique (data-driven model) and Bromilow time-cost model (TCM) (process-based model) have been used. Five hybrid models have been built for comparison purposes: SVM-Bromilow TCM, LR-Bromilow TCM, RBFNN-Bromilow TCM, MLP-Bromilow TCM and GRNN-Bromilow TCM, combining Bromilow TCM with SVM, LR (linear regression), RBFNN (radial basis function neural network), MLP (Multilayer perceptron) and GRNN (general regression neural network), respectively. The highest accuracy has been obtained with SVM-Bromilow TCM with mean absolute percentage error (MAPE) 1.01% and coefficient of determination (R 2) 97.61%.