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ANN Based Approach for Estimation of Construction Costs of Sports Fields

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  • Michał Juszczyk
  • Agnieszka Leśniak
  • Krzysztof Zima

Abstract

Cost estimates are essential for the success of construction projects. Neural networks, as the tools of artificial intelligence, offer a significant potential in this field. Applying neural networks, however, requires respective studies due to the specifics of different kinds of facilities. This paper presents the proposal of an approach to the estimation of construction costs of sports fields which is based on neural networks. The general applicability of artificial neural networks in the formulated problem with cost estimation is investigated. An applicability of multilayer perceptron networks is confirmed by the results of the initial training of a set of various artificial neural networks. Moreover, one network was tailored for mapping a relationship between the total cost of construction works and the selected cost predictors which are characteristic of sports fields. Its prediction quality and accuracy were assessed positively. The research results legitimatize the proposed approach.

Suggested Citation

  • Michał Juszczyk & Agnieszka Leśniak & Krzysztof Zima, 2018. "ANN Based Approach for Estimation of Construction Costs of Sports Fields," Complexity, Hindawi, vol. 2018, pages 1-11, March.
    • Handle: RePEc:hin:complx:7952434
    DOI: 10.1155/2018/7952434
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    References listed on IDEAS

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    1. Trefor Williams, 2002. "Predicting completed project cost using bidding data," Construction Management and Economics, Taylor & Francis Journals, vol. 20(3), pages 225-235.
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    Cited by:

    1. Zhengxun Jin & Jonghyeob Kim & Chang-taek Hyun & Sangwon Han, 2019. "Development of a Model for Predicting Probabilistic Life-Cycle Cost for the Early Stage of Public-Office Construction," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    2. Agnieszka Leśniak & Filip Janowiec, 2019. "Risk Assessment of Additional Works in Railway Construction Investments Using the Bayes Network," Sustainability, MDPI, vol. 11(19), pages 1-15, September.
    3. Edyta Plebankiewicz & Jakub Gracki, 2021. "Analysis of the Impact of Input Data on the Planned Costs of Building Maintenance," Sustainability, MDPI, vol. 13(21), pages 1-16, November.
    4. Maria Mrówczyńska & Marta Skiba & Anna Bazan-Krzywoszańska & Dorota Bazuń & Mariusz Kwiatkowski, 2018. "Social and Infrastructural Conditioning of Lowering Energy Costs and Improving the Energy Efficiency of Buildings in the Context of the Local Energy Policy," Energies, MDPI, vol. 11(9), pages 1-16, September.
    5. Maria Mrówczyńska & Małgorzata Sztubecka & Marta Skiba & Anna Bazan-Krzywoszańska & Przemysław Bejga, 2019. "The Use of Artificial Intelligence as a Tool Supporting Sustainable Development Local Policy," Sustainability, MDPI, vol. 11(15), pages 1-17, August.
    6. Edyta Plebankiewicz & Damian Wieczorek, 2020. "Prediction of Cost Overrun Risk in Construction Projects," Sustainability, MDPI, vol. 12(22), pages 1-15, November.

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