## 2 Research Methodology and Dataset
### Model Selection and Training  
Computations were performed on a desktop computer. The PC has a 64-bit operating system, 32 GB of memory, and a 3.80 GHz processor running an Intel (r) Core(tm) i7-9800X CPU. The programming software for the operations and command was executed using MATLAB R2022. Five models were developed from the UNET architecture. The encoder depth of the architecture was three (3), resulting in a total of 46 layers and 48 connections. At the onset of this study, the control model (CM) was first developed and trained with the base hyperparameters (Figure 1 and Table 3). These hyper-tune parameters were selected after several tuning to yield optimal training and validation loss (Figure 3). There were 44 iterations per epoch, and the model was set to a maximum of 30 epochs, yielding 1320 iterations in total. The model converged at 80 iterations, and the training progress finally reached a training accuracy of 73% and a loss of about 0.67. The computation time was 9 min. 43 sec. The training and validation loss \begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Datasets** & \multicolumn{2}{c}{**Image**} & \multicolumn{2}{c}{**RD**} & \multicolumn{2}{c}{**MD**} & \multicolumn{2}{c}{**MD**} \\ \cline{3-13}
**Bridge** & \multicolumn{2}{c}{**Resolution**} & & & & & & & & & & \\ \hline  & \multicolumn{2}{c}{**Images**} & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**Del.**} & \multicolumn{2}{c}{**Sound**} & \multicolumn{2}{c}{**Background**} & \multicolumn{2}{c}{**Del.**} & \multicolumn{2}{c}{**Sound**} & \multicolumn{2}{c}{**Images**} & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**Pixel**} & \multicolumn{2}{c}{**Sound**} \\ \hline FRNB* & 1952 \(\times\) 480 & 915 & 936,960 & 184,833 & 752,127 & 251,309 & 190,601 & 495,048 & 565 & 578,560 & 276,487 & 660,473 \\ \hline FRSB* & 1632 \(\times\) 416 & 663 & 678,912 & 135,967 & 543,315 & 183,487 & 139,790 & 355,633 & 411 & 420,864 & 202,864 & 476,048 \\ \hline FRMD* & 4352 \(\times\) 416 & 1768 & 1,819,432 & 354,409 & 1,456,023 & 496,993 & 366,114 & 947,324 & 1188 & 1,216,512 & 537,229 & 1,273,203 \\ \hline FRNB* & 3008 \(\times\) 384 & 1128 & 1,155,072 & 222,003 & 933,069 & 377,553 & 229,528 & 610,289 & 930 & 952,320 & 360,085 & 794,987 \\ \hline FRSB* & 4192 \(\times\) 480 & 1965 & 2,012,160 & 429,349 & 1,582,810 & 472,809 & 443,594 & 1,095,756 & 1036 & 1,060,084 & 593,603 & 1,418,557 \\ \hline \hline \end{tabular}
* Forest River North Bound (FRNB); Forest River South Bound (FRSB); Park River Median (PRMD); Park River North Bound (PRNB); Park River South Bound (FRSB).  
\end{table} 
Table 2: Annotated data types developed for training. \begin{table}
\begin{tabular}{l l} \hline \hline
**Optimizer** & **Stochastic Gradient Descent with Momentum (sgdm)** \\ \hline InitialLearnRate & \(1\times 10^{-3}\) \\ \hline Momentum & 0.8 \\ \hline MaxEpochs & 30 \\ \hline MiniBatchSize & 100 \\ \hline LearnRateSchedule & piecewise \\ \hline GradientThresholdMethod & l2 norm \\ \hline GradientThreshold & 0.05 \\ \hline ValidationData & valid \\ \hline ValidationFrequency & 3 \\ \hline VerboseFrequency & 3 \\ \hline Verbose & False \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Base learning hyperparameters for models. were seen to converge before 10 epochs for the CM model (Figure 3). To assess the effect of augmentation and balancing on the dataset, four (4) models in addition were proposed and developed, keeping the base hyperparameters the same for all models. The models developed are (i) raw data (RD), unprocessed dataset (ii). Augmented level 1 models (AM1): translation of the data prior to training of the model (iii). Augmented level 2 models (AM2): translation, rotation, scale, and shear augmentation on the data prior to training of the model (iv). Balanced dataset model (BM): the unbalanced size of data is balanced prior to training (v). Augmented-balanced model (ABM): augmentation and balancing are both applied to the data prior to training.