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Machine Cost-Effectiveness in Earthworks: Early Warning System and Status of the Previous Work Period

Author

Listed:
  • Martina Šopić

    (Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia)

  • Mladen Vukomanović

    (Faculty of Civil Engineering, University of Zagreb, 10000 Zagreb, Croatia)

  • Diana Car-Pušić

    (Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia)

Abstract

Estimating earthwork costs is challenging due to the use of high-cost construction machines, the performance of works in dynamic, changing, and uncertain conditions, and the issues of machine actual productivity. In earthworks, there is a constant need to track, control, and analyze the progress to reduce costs. The management of machines’ work on construction sites is complex due to an unknown or insufficiently accurate assessment of their actual productivity, and it relies heavily on the site manager’s (in)experience. The cost-effectiveness of the contracted price for the operation of the machines may be questionable. This paper proposes a model for machine cost-effectiveness in earthworks. The proposed model consists of an Early warning system and Status of the previous work period. The Early warning system can provide timely and reliable detection of cost-effectiveness and profitability thresholds for excavators and tipper trucks during the excavation and material removal. The Status of the previous work period is time-dependent and provides a final assessment of the cost-effectiveness of excavators and tipper trucks for the past month or a more extended time. Applying the proposed model at the construction site of the infrastructure project demonstrated its practicality and purpose.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7294-:d:1463471
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    References listed on IDEAS

    as
    1. Martina Šopić & Mladen Vukomanović & Diana Car-Pušić, 2023. "Protocol Proposal for Data Collection and Processing in Productivity Assessment of Earthworks Using Audio-Visual and Location-Sensing Technology," Sustainability, MDPI, vol. 15(16), pages 1-25, August.
    2. 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.
    3. Xueyuan Gao & Hua Feng, 2023. "AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity," Sustainability, MDPI, vol. 15(11), pages 1-21, June.
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