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 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 o...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/7405863 |
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| _version_ | 1832555488315703296 |
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| author | Silvana Petruseva Valentina Zileska-Pancovska Diana Car-Pušić |
| author_facet | Silvana Petruseva Valentina Zileska-Pancovska Diana Car-Pušić |
| author_sort | Silvana Petruseva |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b36f1f36dceb42339047e02b494289a2 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-b36f1f36dceb42339047e02b494289a22025-02-03T05:48:06ZengWileyAdvances in Civil Engineering1687-80861687-80942019-01-01201910.1155/2019/74058637405863Implementation of Process-Based and Data-Driven Models for Early Prediction of Construction TimeSilvana Petruseva0Valentina Zileska-Pancovska1Diana Car-Pušić2“Ss Cyril and Methodius” University, Faculty of Civil Engineering, Partizanski Odredi 24, 1000 Skopje, Macedonia“Ss Cyril and Methodius” University, Faculty of Civil Engineering, Partizanski Odredi 24, 1000 Skopje, MacedoniaUniversity of Rijeka, Faculty of Civil Engineering, Radmile Matejcic 3, Rijeka 51 000, CroatiaThe 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.http://dx.doi.org/10.1155/2019/7405863 |
| spellingShingle | Silvana Petruseva Valentina Zileska-Pancovska Diana Car-Pušić Implementation of Process-Based and Data-Driven Models for Early Prediction of Construction Time Advances in Civil Engineering |
| title | Implementation of Process-Based and Data-Driven Models for Early Prediction of Construction Time |
| title_full | Implementation of Process-Based and Data-Driven Models for Early Prediction of Construction Time |
| title_fullStr | Implementation of Process-Based and Data-Driven Models for Early Prediction of Construction Time |
| title_full_unstemmed | Implementation of Process-Based and Data-Driven Models for Early Prediction of Construction Time |
| title_short | Implementation of Process-Based and Data-Driven Models for Early Prediction of Construction Time |
| title_sort | implementation of process based and data driven models for early prediction of construction time |
| url | http://dx.doi.org/10.1155/2019/7405863 |
| work_keys_str_mv | AT silvanapetruseva implementationofprocessbasedanddatadrivenmodelsforearlypredictionofconstructiontime AT valentinazileskapancovska implementationofprocessbasedanddatadrivenmodelsforearlypredictionofconstructiontime AT dianacarpusic implementationofprocessbasedanddatadrivenmodelsforearlypredictionofconstructiontime |