IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p15779-d985708.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/15779/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/23/15779/
    Download Restriction: no
    ---><---

    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. Grzegorz Wrzesiński & Anna Markiewicz, 2022. "Prediction of Permeability Coefficient k in Sandy Soils Using ANN," Sustainability, MDPI, vol. 14(11), pages 1-13, May.
    4. 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.
    5. 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.
    6. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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.
    2. 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.
    3. 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.
    4. 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.
    5. Xin Gao & Gengxin Duan & Chunguang Lan, 2021. "Bayesian Updates for an Extreme Value Distribution Model of Bridge Traffic Load Effect Based on SHM Data," Sustainability, MDPI, vol. 13(15), pages 1-15, August.
    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. 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.
    8. Binqiang Guo & Renzhi Wang & Chen Lu & Weijian Shi & Qingfei Gao, 2021. "Numerical Investigation on the Dynamic Performance of Steel–Concrete Composite Continuous Rigid Bridges Subjected to Moving Vehicles," Sustainability, MDPI, vol. 13(24), pages 1-16, December.
    9. Jan Kowalski & Marzena Lendo-Siwicka & Grzegorz Wrzesiński & Roman Trach, 2023. "Verification of Performance Standards for Construction Equipment in Terms of CO 2 Emissions," Sustainability, MDPI, vol. 15(21), pages 1-10, October.
    10. Youngjin Choi & Yangrok Choi & Jun-sang Cho & Dongwoo Kim & Jungsik Kong, 2023. "Utilization and Verification of Imaging Technology in Smart Bridge Inspection System: An Application Study," Sustainability, MDPI, vol. 15(2), pages 1-31, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15779-:d:985708. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.