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Modeling the Quantitative Assessment of the Condition of Bridge Components Made of Reinforced Concrete Using ANN

Author

Listed:
  • Roman Trach

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02-776 Warsaw, Poland
    Institute of Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine)

  • Victor Moshynskyi

    (Institute of Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine)

  • Denys Chernyshev

    (Department of Management in Construction, Kyiv National University of Construction and Architecture, 03037 Kyiv, Ukraine)

  • Oleksandr Borysyuk

    (Institute of Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine)

  • Yuliia Trach

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02-776 Warsaw, Poland
    Institute of Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine)

  • Pavlo Striletskyi

    (Institute of Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine)

  • Volodymyr Tyvoniuk

    (Institute of Civil Engineering, Warsaw University of Life Sciences, 02-776 Warsaw, Poland
    Institute of Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine)

Abstract

Bridges in Ukraine are one of the most important components of the infrastructure, requiring attention from government agencies and constant funding. The object of the study was the methodology for quantifying the condition of bridge components. The Artificial Neural Network-based (ANN) tool was developed to quantify the technical condition of bridge components. The literature analysis showed that in most cases the datasets were obtained during the inspection of bridges to solve the problems of assessing the current technical condition. The lack of such a database prompted the creation of a dataset on the basis of the Classification Tables of the Operating Conditions of the Bridge Components (CT). Based on CTs, five datasets were formed to assess the condition of the bridge components: bridge span, bridge deck, pier caps beam, piers and abutments, approaches. The next step of this study was creating, training, validating and testing ANN models. The network with ADAM loss function and softmax activation showed the best results. The optimal values of MAPE and R 2 were achieved at the 100th epoch with 64 neurons in the hidden layer and were equal to 0.1% and 0.99998, respectively. The practical application of the ANN models was carried out on the most common type of bridge in Ukraine, namely, a road beam bridge of small length, made of precast concrete. The novelty of this study consists of the development of a tool based on the use of ANN model, and the proposal to modify the methodology for quantifying the condition of bridge components. This will allow minimizing the uncertainties associated with the subjective judgments of experts, as well as increasing the accuracy of the assessment.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15779-:d:985708
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    References listed on IDEAS

    as
    1. 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.
    2. Samuel Y. O. Amakye & Samuel J. Abbey & Colin A. Booth & Jonathan Oti, 2022. "Performance of Sustainable Road Pavements Founded on Clay Subgrades Treated with Eco-Friendly Cementitious Materials," Sustainability, MDPI, vol. 14(19), pages 1-23, October.
    3. 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.
    4. 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.
    5. Grzegorz Wrzesiński & Anna Markiewicz, 2022. "Prediction of Permeability Coefficient k in Sandy Soils Using ANN," Sustainability, MDPI, vol. 14(11), pages 1-13, May.
    6. Youngjin Choi & Jinhyuk Lee & Jungsik Kong, 2020. "Performance Degradation Model for Concrete Deck of Bridge Using Pseudo-LSTM," Sustainability, MDPI, vol. 12(9), pages 1-19, May.
    7. 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.
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    Cited by:

    1. 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.

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