## 5 Results and Utilization Plan  
Classification performance indicators were used to evaluate whether the labels classified based on the trained data accurately matched the actual labels. The most commonly used accuracy, precision, recall, and F-1 score were used among the various performance indicators. Precision is calculated as the number of true positives (TP) divided by the sum of true positives and false positives (FP): Precision = TP/(TP + FP). Similarly, recall is calculated as the number of true positives divided by the sum of true positives and false negatives (FN): Recall = TP/(TP + FN). For instance, if the classifier correctly identifies 80 true positives, 20 false positives, and 10 false negatives, the precision would be \(80/(80+20)=0.8\) and recall would be \(80/(80+10)=0.89\). Although accuracy is a ratio of accurately predicting positive/false values and is often used to judge intuitive accuracy, it is difficult to be evaluated properly if data are unbalanced or biased to a specific class. Precision is the probability of the actual values being positive among the positive results classified by the model. It is also called positive predictive value (PPV) as an indicator of how well the positives were predicted. Recall (recall rate) is the ratio of the true values predicted by the model among those that are actually true, which means the same as sensitivity. The criteria for true positives are different from precision. Precision and recall can be used in a complementary manner; when the results of both indicators are high, they can be considered better indicators. Although precision is important in some cases, the recall value is also important in certain other cases depending on the analysis data. The F-1 score is an indicator that reveals the effectiveness of the model. It is a harmonic average of precision and recall, which combines two values and converts them into one statistical value. It was found that the data ratio of the label was extremely different for each class, as the pouring noise was primarily collected in the beginning. Although it was determined that there was no problem in training even though the data ratio was different as the purpose was to train and determine the pouring noise, the imbalance of the dataset exhibited many errors and biases in the construction of the classification model. Therefore, data preprocessing was performed by adding white noise to the existing data or by including the official data (urban 8 k) to balance the existing data. The precision  
Figure 11: Image of sound data at a construction site pre-processed for RNN model training. Figure 10: Recurrent neural networks (RNNs). and recall ratio of all classes were improved as a result of calculating and expressing the precision and recall values for each class, as shown in both Figure 12 and Table 1.  
A total of approximately 9000 data were collected, with 80% (7200 data) used for training and 20% (1800 data) used for testing the CNN model by setting an optimized interval. As a result of performing 100 iterations, the accuracy for determining the performance of the classification model continued to increase as the number of iterations increased, and then converged after the 78 th iteration, resulting in an accuracy of approximately 94.38%. In addition, it was trained by inputting them into the RNN model, and as a result of performing iteration learning 100 times as in the CNN model, the accuracy increased as the number of iterations increased, and then converged after the 69 th iteration, resulting in an accuracy of approximately 93.26% (Figure 13). As a result of training on the CNN and RNN models with the following parameters (e.g., number of layers, neurons, activation functions, and training epochs), both showed extremely high accuracy at 94.38% and 93.26%, respectively. In classification model development, both models are expected to exhibit high performance in classifying sound data at the site.