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

Roman Trach (), Victor Moshynskyi, Denys Chernyshev, Oleksandr Borysyuk, Yuliia Trach, Pavlo Striletskyi and Volodymyr Tyvoniuk
Additional contact information
Roman Trach: Institute of Civil Engineering, Warsaw University of Life Sciences, 02-776 Warsaw, Poland
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
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

Sustainability, 2022, vol. 14, issue 23, 1-19

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.

Keywords: bridge components; quantitative assessment; artificial neural network; inspection; bridge management system; road beam bridge; condition (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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