# Using AI-Based Tools to Quantify the Technical Condition of Bridge Structural Components
## 4 Discussion  
The assessment of the technical condition of reinforced concrete structural components is a critical task in civil engineering. Classical methods, such as empirical formulas, linear-elastic analyses, and finite element modeling (FEM), have long been the standard tools for this purpose. These techniques are grounded in well-established principles of material science and structural mechanics, offering reliable and interpretable results. However, with the rapid development of AI, modern approaches are increasingly explored and adopted, offering distinct advantages and challenges when compared to classical methods. At the same time, the use of various AI tools to solve the tasks of monitoring and assessing the condition of bridge elements is observed. [PERSON] [49] utilized an MLP model to predict the condition scores of bridge decks using bridge age as the sole input. Among 38 estimates, the network achieved a prediction accuracy of 79%. [PERSON] and [PERSON] [50] developed an asset management system to evaluate and predict bridge deck wear conditions. Their approach involved two machine learning models--ANN and k-nearest neighbors (KNN)--resulting in a model with a prediction accuracy of 91.44%. Similarly, [PERSON] and [PERSON] [24] employed an ANN model to predict bridge conditions based on assessments of structural components. Their findings demonstrated that the ANN model achieved a prediction accuracy of 73.6% for bridge deck condition evaluations. Classical methods rely on deterministic models and often involve large simplifications of the real-world behavior of materials and structures. These methods are computationally intensive and time-consuming, particularly when dealing with complex structures. Additionally, classical approaches may struggle to account for uncertainties in material degradation, environmental effects, and unexpected loading scenarios without extensive parametric studies. In contrast, modern AI-based methods, such as machine learning (ML) and deep learning (DL), provide a data-driven alternative for assessing structural conditions. These methods excel in handling large datasets, identifying patterns, and predicting outcomes without explicit programming of physical laws. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**x1** & **x2** & **x3** & **x4** & **x5** & **x6** & **x7** & **x8** & **x9** & **x10** & **x11** & **x12** & **y** & **y’** & **A y, \%** \\ \hline
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 15 & 35 & 0 & 0.052,8 & 5.28 \\ \hline
0 & 0 & 0 & 21 & 31 & 20 & 0 & 30 & 0 & 0 & 0 & 27 & 1 & 1.004,4 & 0.446 \\ \hline
32 & 0 & 22 & 25 & 14 & 9 & 34 & 9 & 33 & 9 & 0 & 10 & 2 & 2.002,1 & 0.106 \\ \hline
17 & 26 & 12 & 12 & 6 & 0 & 1 & 0 & 10 & 0 & 0 & 1 & 3 & 2.993,2 & 0.227 \\ \hline
1 & 1 & 2 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 4 & 4.015,6 & 0.389 \\ \hline
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 5 & 4.886,5 & 2.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: ANN model effectiveness confirmation. One great advantage of AI-based methods is their adaptability to nonlinear and complex behaviors that classical methods might oversimplify or overlook. Additionally, AI models can be trained to incorporate probabilistic elements, allowing for more robust assessments under uncertain conditions. However, the accuracy and reliability of AI methods heavily depend on the quality and quantity of training data. Inadequate or biased datasets can lead to erroneous predictions, limiting their applicability in critical safety assessments. To bridge the gap between classical and AI-based approaches, hybrid methods are gaining attention. These methods combine the strengths of deterministic models with the predictive power of AI, enabling more comprehensive assessments. It is necessary to note the limitations of this study. Summarizing the analysis of the quantitative assessment of the technical condition of bridges and their structural components in different countries, which was carried out in Section 2, the following can be noted:  
1.