## 5 Results
### Results of ANN Prediction and Model Assessment  
The collected data were divided into a training dataset and validation dataset. The training dataset was used to compute the gradient and update connection weights, and the validation dataset was used to assess errors. Training was completed when the errors for the training dataset reduced and the errors for the validation dataset increased, during which the model had the minimum error for the validation dataset. To achieve high prediction accuracy, model training was performed using the training dataset. In each iteration, the batch size of updates was 30 samples. For an ANN model, the number of epochs refers to the number of times the model processes the entire training dataset. A higher number of epochs generally results in a higher number of iterations and better model performance. The model training process involves the processing of multiple epochs of data until the model can correctly classify or predict the training data. In this study, the number of epochs was set as 100. After 100 epochs, the ANN model achieved prediction accuracies of 85.5% for the training datasets (Figure 5). Finally, the prediction accuracy of the proposed testing set was 85%. Similar to other supervised learning models, ANN models require large quantities of labeled data for training. In particular, ANNs must repeatedly perform training by using the same sample to determine the optimal weights. The training process is repeated until  
Figure 5: Prediction accuracy of the training model. the minimum error is achieved (i.e., error between the labeled and predicted values) and an accurate classification is obtained. In this study, the categorical cross-entropy function was used to compute the training loss (learning errors) for 100 epochs. Initially, the training loss decreased rapidly, and it then stabilized by the 20 th epoch. The final training loss was determined to be \(-1.5\) in relation to the optimal value (Figure 6). Managers can use the proposed AHP-ANN model for accurately and rapidly extracting valuable information from big data. Thus, this model can effectively support managers in decision making. Given that machine learning is an exploratory method, the analysis direction must be determined before conducting data mining. The results of data mining might be unpredictable. However, novel and useful knowledge can be obtained through machine-learning-based training and testing, and this knowledge can be used to construct a decision-making model for construction management. The AHP-ANN method provides several advantages; it addresses various weaknesses in several dimensions and simplifies the dimensions of a considerable number of risk factors to identify the major risk factors and estimate their influence. In addition, it can decompose complex problems one by one and establish a hierarchical structure comprising five risk dimensions, enabling managers to understand the attributes of the factors that affect quality and to effectively manage the major factors. When an empirical model is derived from a large volume of data and the mathematical framework of a system is unclear such that conventional statistical methods based on appropriate assumptions cannot be applied, an ANN-based prediction model becomes useful. However, because the training and model derivation process of an ANN is a black box, the ANN is at a disadvantage because it has difficulty explaining the logical reasoning and meaning of a model in accordance with the applied parameters. In machine learning, a large volume of historical data are used to predict future actions or outcomes. In this context, prediction involves inputting the features of known variables or factors (e.g., importance) and classifying or regressing the output results. When an unknown set of variables is input into a machine learning model, the model can calculate the probability value on the basis of past experiences and further classify its results.