engleski

Implementation of Process-Based and Data-Driven Models for Early Prediction of Construction Time

The need of respecting the construction time as one of the construction contract elements points out that construction time early prediction 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 hybrid method for predicting construction time in early project phase, which is a combination of process based and data- driven model. Five hybrid models have been developed and the most accurate one was the BTC-GRNN model which uses the Bromilow’s time- cost model (BTC) as a process based model, and the general regression neural network (GRNN) as a data driven model. For evaluating the quality of the models, 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.

construction time, predicting, General Regression Neural Network, DTREG software, process based model, data driven model

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