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Construction equipment productivity estimation using artificial neural network model

Author

Listed:
  • Seung Ok
  • Sunil Sinha

Abstract

Estimating equipment production rates is both an art and a science. An accurate prediction of the productivity of earthmoving equipment is critical for accurate construction planning and project control. Owing to the unique work requirements and changeable environment of each construction project, the influences of job and management factors on operation productivity are often very complex. Hence, construction productivity estimation, even for an operation with well-known equipment and work methods, can be challenging. This study develops and compares two methods for estimating construction productivity of dozer operations (the transformed regression analysis, and a non-linear analysis using neural network model). It is the hypothesis of this study that the proposed neural networks model may improve productivity estimation models because of the neural network's inherent ability to capture non-linearity and the complexity of the changeable environment of each construction project. The comparison of results suggests that the non-linear artificial neural network (ANN) has the potential to improve the equipment productivity estimation model.

Suggested Citation

  • Seung Ok & Sunil Sinha, 2006. "Construction equipment productivity estimation using artificial neural network model," Construction Management and Economics, Taylor & Francis Journals, vol. 24(10), pages 1029-1044.
    • Handle: RePEc:taf:conmgt:v:24:y:2006:i:10:p:1029-1044
    DOI: 10.1080/01446190600851033
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    Citations

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    Cited by:

    1. Dixit Saurav, 2021. "Impact of management practices on construction productivity in Indian building construction projects: an empirical study," Organization, Technology and Management in Construction, Sciendo, vol. 13(1), pages 2383-2390, January.
    2. Hassanean S. H. Jassim & Weizhuo Lu & Thomas Olofsson, 2017. "Predicting Energy Consumption and CO 2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model," Sustainability, MDPI, vol. 9(7), pages 1-25, July.
    3. Mohd. Ahmed & Saeed AlQadhi & Javed Mallick & Nabil Ben Kahla & Hoang Anh Le & Chander Kumar Singh & Hoang Thi Hang, 2022. "Artificial Neural Networks for Sustainable Development of the Construction Industry," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    4. Roy, Adrien & McCabe, Brenda Y. & Saxe, Shoshanna & Posen, I. Daniel, 2024. "Review of factors affecting earthworks greenhouse gas emissions and fuel use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 194(C).
    5. Martina Šopić & Mladen Vukomanović & Diana Car-Pušić, 2024. "Machine Cost-Effectiveness in Earthworks: Early Warning System and Status of the Previous Work Period," Sustainability, MDPI, vol. 16(17), pages 1-19, August.

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