# Using AI-Based Tools to Quantify the Technical Condition of Bridge Structural Components
## 3 Results  
As described in the Methods section, five models--XGBoost, SVR, DT, RF, and ANN--were developed and trained. These algorithms are favored because they strike a balance between performance and interpretability, and they deliver accurate results in classification and regression. They offer a robust toolkit combining efficiency, flexibility, and ease of use. Each has unique strengths, allowing one to select the most appropriate tool depending on the specific problem. The models predicted indicators \"Condition's Assessment\" as output value. All the AI models were employed to address a regression task, enabling the prediction of precise numerical values rather than discrete class labels (integer values), as seen in classification tasks. The regression approach allows for a highly accurate assessment of a component's condition, for example, with outputs precise to three decimal places. In contrast, relying on integer values for evaluation may fail to capture the true condition of the component and can significantly distort the representation of reality. Table 4 presents the performance of these models, evaluated based on MAPE and R\({}^{2}\) metrics, while Figure 2 illustrates the scatter of predicted versus target values. Figure 2: The scatter of predicted versus target values. (**a**)—XGBoost model, (**b**)—RF model, (**c**)—DT model, (**d**)—SVR model, (**e**)—ANN model. The results revealed that all models exhibited relatively low performance, with the ANN model showing a slight edge. Consequently, it was decided to further optimize the ANN models to enhance their productivity. Nine ANN models were designed with identical architectures but differing in activation functions and optimization algorithms. The optimal network structure consisted of an input layer with 23 neurons, a first hidden layer with 64 neurons, a second hidden layer with 32 neurons, and an output layer with a single neuron. Activation functions used included sigmoid, tanh, and ReLU, while the optimization algorithms applied were SGD, ADAM [46, 47, 48], and RMSprop. Table 5 outlines the characteristics and performance comparisons of the ANN models tested over 100 epochs. Notably, the ReLU activator function (used in ANN 4, ANN 5, and ANN 6) improved extensively over sigmoid and tanh activator functions. Based on MAPE and R\({}^{2}\) metrics, the ANN 5 model achieved the best results, with the lowest MAPE and highest R\({}^{2}\). This model utilized the ReLU activation function for hidden layers and the RMSprop optimizer, achieving optimal performance at 100 epochs (MAPE = 3.5%; R\({}^{2}\) = 0.994). The ANN 1 model achieved the worst results, with the highest MAPE = 13.3% and lowest R\({}^{2}\) = 0.895. Figures 3 and 4 depict MSE and MAE results for the ANN 5 model training and validation datasets. Figure 3 compares MSE results for training and validation datasets of the ANN 5 model. The maximum MSE values occurred during the first epoch--3.882 for the training set and 2.831 for the validation set. The minimum MSE values were observed in the 99 th epoch--0.030 for the training set and 0.051 for the validation set. Figure 4 illustrates MAE results for ANN 5. The maximum MAE occurred during the first epoch for both training (1.637) and validation datasets (1.326). At the same time, the minimum values for training datasets were observed in the 100 h epoch (0.047) and the 89 th epoch for validation (0.062). The following research stage was to evaluate ANN models on datasets assessing the condition of span structure and abutments. The procedure was applied to each of the remaining two bridge components. Table 6 shows the optimal R\({}^{2}\) and MAPE for three ANN models. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Models Metrics** & **XGBoost** & **SVR** & **DT** & **RF** & **ANN** \\ \hline MAPE, \% & 35.94 & 27.87 & 50.05 & 39.23 & 26.63 \\ \hline R\({}^{2}\) & 0.611 & 0.673 & 0.437 & 0.585 & 0.750 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of the ML models’ effectiveness.