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Using ANN to Predict the Impact of Communication Factors on the Rework Cost in Construction Projects

Author

Listed:
  • Roman Trach

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02787 Warsaw, Poland)

  • Yuliia Trach

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02787 Warsaw, Poland
    Water Disposal and Drilling Engineering, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine)

  • Marzena Lendo-Siwicka

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02787 Warsaw, Poland)

Abstract

The construction sector has a large impact on the environment and available resources. Natural resources and energy consumption occurs not only during the operation of the facility, but also during its construction. In addition, this situation often occurs when work already completed requires rework. In such cases, not only the reuse of resources and energy occurs but also generation of waste. Many studies support the relationship between communication and project efficiency, which is expressed in the cost of rework. At present there is no available tool to quantify the evaluation of this relationship. This study aims to fill this knowledge gap. The article purpose was to create ANNs (artificial neural networks) for assessing and predicting the impact of communication factors on rework costs in construction projects. During the data collection phase, 12 factors that influence communication were identified and assessed. The level of rework costs in 18 construction projects was also calculated. We used ANN, which is a two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer. The network input layer consists of 12 neurons while the hidden layer consists of 10 neurons and one output neuron. The optimal results of the mean square error and correlation were shown by the Levenberg–Marquardt algorithm. The proposed model can be used by project management as the integration decision support tool aimed at decreasing the number of reworks and reducing energy and resource consumption in construction projects.

Suggested Citation

  • Roman Trach & Yuliia Trach & Marzena Lendo-Siwicka, 2021. "Using ANN to Predict the Impact of Communication Factors on the Rework Cost in Construction Projects," Energies, MDPI, vol. 14(14), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4376-:d:597746
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    References listed on IDEAS

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    9. Yuting Qi & Queena K. Qian & Frits M. Meijer & Henk J. Visscher, 2019. "Identification of Quality Failures in Building Energy Renovation Projects in Northern China," Sustainability, MDPI, vol. 11(15), pages 1-23, August.
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    Cited by:

    1. Yuliia Trach & Victor Melnychuk & Oleksandr Stadnyk & Roman Trach & Filip Bujakowski & Agnieszka Kiersnowska & Gabriela Rutkowska & Leonid Skakun & Jacek Szer & Eugeniusz Koda, 2023. "The Possibility of Implementation of West Ukrainian Paleogene Glauconite–Quartz Sands in the Building Industry: A Case Study," Sustainability, MDPI, vol. 15(2), pages 1-22, January.
    2. Roman Trach & Yuliia Trach & Agnieszka Kiersnowska & Anna Markiewicz & Marzena Lendo-Siwicka & Konstantin Rusakov, 2022. "A Study of Assessment and Prediction of Water Quality Index Using Fuzzy Logic and ANN Models," Sustainability, MDPI, vol. 14(9), pages 1-19, May.
    3. Roman Trach & Oleksandr Khomenko & Yuliia Trach & Oleksii Kulikov & Maksym Druzhynin & Nataliia Kishchak & Galyna Ryzhakova & Hanna Petrenko & Dmytro Prykhodko & Olha Obodіanska, 2023. "Application of Fuzzy Logic and SNA Tools to Assessment of Communication Quality between Construction Project Participants," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    4. Jan Kowalski & Mieczysław Połoński & Marzena Lendo-Siwicka & Roman Trach & Grzegorz Wrzesiński, 2021. "Method of Assessing the Risk of Implementing Railway Investments in Terms of the Cost of Their Implementation," Sustainability, MDPI, vol. 13(23), pages 1-11, November.
    5. Roman Trach & Galyna Ryzhakova & Yuliia Trach & Andrii Shpakov & Volodymyr Tyvoniuk, 2023. "Modeling the Cause-and-Effect Relationships between the Causes of Damage and External Indicators of RC Elements Using ML Tools," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    6. Yuliia Trach & Roman Trach & Marek Kalenik & Eugeniusz Koda & Anna Podlasek, 2021. "A Study of Dispersed, Thermally Activated Limestone from Ukraine for the Safe Liming of Water Using ANN Models," Energies, MDPI, vol. 14(24), pages 1-14, December.
    7. Roman Trach & Victor Moshynskyi & Denys Chernyshev & Oleksandr Borysyuk & Yuliia Trach & Pavlo Striletskyi & Volodymyr Tyvoniuk, 2022. "Modeling the Quantitative Assessment of the Condition of Bridge Components Made of Reinforced Concrete Using ANN," Sustainability, MDPI, vol. 14(23), pages 1-19, November.

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