Abstract
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.
Highlights
Construction time is one of the key elements at the early phases of the construction project, in bidding and contracting processes [1]. e problem arises precisely from the need to make a more accurate estimation of building time in those early phases of the project. ere is, a high degree of uncertainty and often a lack of information required for a satisfactory accurate time assessment
For evaluating the quality of the models, the 10-fold cross-validation method has been used. e mean absolute percentage error (MAPE) of the Bromilow’s time-cost (BTC)-general regression neural network (GRNN) is 3.34% and the coefficient of determination R2, which reflects the global fit of the model, is 93.17%. ese 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%. is 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
A data-driven model using only GRNN for predicting real time of construction has been developed. e real time of construction has been used as a target variable and the rest of the variables have been used as predictors
Summary
Construction time is one of the key elements at the early phases of the construction project, in bidding and contracting processes [1]. e problem arises precisely from the need to make a more accurate estimation of building time in those early phases of the project. ere is, a high degree of uncertainty and often a lack of information required for a satisfactory accurate time assessment. E problem arises precisely from the need to make a more accurate estimation of building time in those early phases of the project. Two elements are important for solving problems, collecting and systematically storing information, and developing new and improving existing models of time estimates, which will use such information. While the problem of systematic storage and use of information can be solved by an appropriate information system [2], the development and improvement of appropriate assessment models are the result of scientific research. Addressing modern challenges and developmental trends in civil engineering [3,4,5], the information system should include prediction time models that would use data system resources and supply it with new data. Watson [5] classifies the “fragmented structure” into one of the “underlying, inherent construction industry problems.” Solutions that bring integrated management information systems have a synergistic potential with the ability to Advances in Civil Engineering enhance significantly the operational, functional, economic, management, and quality dimensions of the construction
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