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A Comparative Study of Models for the Construction Duration Prediction in Highway Road Projects of India

Author

Listed:
  • P. Velumani

    (Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil 626126, Tamil Nadu, India)

  • N. V. N. Nampoothiri

    (Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil 626126, Tamil Nadu, India)

  • M. Urbański

    (Faculty of Civil Engineering, Czestochowa University of Technology, 42201 Czestochowa, Poland)

Abstract

Predicting the duration of construction projects with acceptable accuracy is a problem for contractors and researchers. Numerous researchers and tools are involved in sorting out this problem. The aim of the study is to predict the construction duration using four analytical tools as an approach. The success of construction projects in regard to time depends on various factors such as selection of contractors, consultants, cost of the projects, quality of the projects, the quantity of the projects, environmental factors, etc. Presently available commercial tools in the market are not designed as universally common and concerned. Every tool performs well in a particular situation. The prediction of India’s highway road projects duration is the biggest construction issue in the country due to various reasons. To overcome this problem, the methodology of the paper adopts various strategies to find suitable tools to predict the highway road projects’ duration, in which it classifies and analyzes the collected data. As a part of this work, the details of 363 government infrastructure projects (traditional procurement) were collected from 2000 to 2018. The present study also adopts various tools for duration prediction such as artificial neural networks (ANNs), smoothing techniques, time series analysis, and Bromilow’s time–cost (BTC) model. The results of the study recommend smoothing techniques with a constant value of 0.3, which gave the remarkable very small error of 1.2%, and its outcomes become even better when compared to other techniques.

Suggested Citation

  • P. Velumani & N. V. N. Nampoothiri & M. Urbański, 2021. "A Comparative Study of Models for the Construction Duration Prediction in Highway Road Projects of India," Sustainability, MDPI, vol. 13(8), pages 1-13, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4552-:d:539376
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    References listed on IDEAS

    as
    1. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
    2. Daniel Chan & Mohan Kumaraswamy, 1999. "Modelling and predicting construction durations in Hong Kong public housing," Construction Management and Economics, Taylor & Francis Journals, vol. 17(3), pages 351-362.
    3. Mario Vanhoucke, 2013. "Project Management with Dynamic Scheduling," Springer Books, Springer, edition 2, number 978-3-642-40438-2, March.
    4. Martin Skitmore & NG Thomas, 2001. "Australian project time-cost analysis: Statistical analysis of intertemporal trends," Construction Management and Economics, Taylor & Francis Journals, vol. 19(5), pages 455-458.
    5. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt’s exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759.
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    Cited by:

    1. Kristina Galjanić & Ivan Marović & Tomaš Hanak, 2025. "Neural Network-Based Approaches for Predicting Construction Overruns with Sustainability Considerations," Sustainability, MDPI, vol. 17(16), pages 1-17, August.
    2. Qiao, Yu & Alinizzi, Majed & Pourgholamali, Mohammadhosein & Fricker, Jon D. & Labi, Samuel, 2026. "Transportation project bundling policy impacts on construction time delay: some empirical evidence," Transportation Research Part A: Policy and Practice, Elsevier, vol. 203(C).
    3. Junlong Peng & Chao Peng & Mengyao Wang & Ke Hu & Dubin Wu, 2022. "Research on the factors of extremely short construction period under the sufficient resources based on Grey-DEMATEL-ISM," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-21, March.

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