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

Author(s):


Medium: journal article
Language(s): English
Published in: Advances in Civil Engineering, , v. 2019
Page(s): 1-12
DOI: 10.1155/2019/7405863
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 determinationR², 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% andR² 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.

Copyright: © 2019 Silvana Petruseva et al.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10296145
  • Published on:
    27/01/2019
  • Last updated on:
    02/06/2021