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Modeling the Cause-and-Effect Relationships between the Causes of Damage and External Indicators of RC Elements Using ML Tools

Author

Listed:
  • Roman Trach

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

  • Galyna Ryzhakova

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

  • Yuliia Trach

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

  • Andrii Shpakov

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

  • Volodymyr Tyvoniuk

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

Abstract

Reinforced concrete (RC) structures are used in a wide range of applications, including high-rise buildings, nuclear power plants, oil and gas platforms, bridges, and other infrastructure. However, over time, RC structures can be subject to deterioration and damage, particularly from exposure to weather and environmental conditions, heavy traffic loads, and other factors. Regular inspections, diagnosing the condition, maintenance, and repair can help to mitigate the effects of degradation and extend the life of the structure. The task of this study was to determine the possible causes of the defects of the RC elements based on the identification of external indicators using the ML tools. This study created and compared the performance of four ML models, namely, Support Vector Regression (SVR), decision trees (DTs), random forest (RF), and Artificial Neural Networks (ANNs). The first comparison showed a rather low performance of all models, with a slight advantage of the ANN model. Later, six ANN models were optimized to obtain a higher level of performance. The next step of this study was the training, validation, and testing of ANN models. Analysis of MAPE and R2 metrics showed that the ANN model with an Adaptative Moment (ADAM) loss function and sigmoid activation had the best results (MAPE 3.38%; R 2 0.969). The novelty of the study consisted of the development of the ML model, which is based on the use of ANNs, and allows for the establishment of cause-and-effect relationships in the diagnosis of the technical condition of the RC elements. The advantage of using ANN to solve this problem is the possibility to obtain a forecast in the form of continuous values. Moreover, the model can be used further without retraining, and it can make predictions on datasets it has not yet “seen”. The practical use of such a model will allow for the diagnosis of some causes of defects during a visual inspection of structures.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5250-:d:1098554
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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. 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.
    3. Kennedy C. Onyelowe & Ahmed M. Ebid & Ariel Riofrio & Haci Baykara & Atefeh Soleymani & Hisham A. Mahdi & Hashem Jahangir & Kizito Ibe, 2022. "Multi-Objective Prediction of the Mechanical Properties and Environmental Impact Appraisals of Self-Healing Concrete for Sustainable Structures," Sustainability, MDPI, vol. 14(15), pages 1-28, August.
    4. 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.
    5. 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.
    6. 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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