## 2 Related Works  
Recently, research on predicting construction costs using the ANN method has been steadily increasing. Most of these studies focus on predicting the total construction cost according to the ANN model and influencing factors.  
[PERSON] et al. [6] found that the ensemble model can be utilized in ANN-based conceptual cost estimation models to improve prediction accuracy and stability when the amount of project data is not large. [PERSON] et al. [7] attempted to develop a fast and accurate machine learning (ML) and optimization framework. They reviewed 13 ML regression algorithms and suggested that using artificial neural networks (ANNs), gradient boosting, and XGBoost models, construction costs can be predicted with the best accuracy with minimal time.  
[PERSON] et al. [8] developed a genetic algorithm-backpropagation neural network (GA-BPNN) model to quantitatively predict project portfolio benefit (PPB). The model developed in the study showed higher prediction accuracy than conventional BPNNs. [PERSON] et al. [9], proposed a hybrid ARIMA-ANN model to predict construction costs and city-level construction cost indicators (CCIs), which could provide more accurate predictions than ARIMA or ANN.  
[PERSON] et al. [10] stated that the ANN method was inspired by the process in which the human brain works. [PERSON] [11] said that ANN finds the correlation weight of the hidden layer in the relationship between the input layer and the output layer and predicts the result value through the calculated weight. ANN algorithms are of great help in solving problems involving variables with dynamic properties. Through the learning process, the pattern of variables can be found, and this can also be confirmed in the form of a learning curve.  
An approximate estimate is to predict construction costs for uncompleted plans. Construction cost is calculated through the experience of the estimator, and is calculated based on data from previously completed projects. The error range of the approximate estimate is approximately 10 to 25% [11]. Although there are various methods for cost estimation, ANN is an accurate and widely known method for cost estimation. Previously, various studies have been conducted to solve the cost estimation problem, and these studies confirm that ANN's performance is better than that of a simple regression method and has a great effect on accuracy depending on the composition of neural networks in cost estimation [1].  
In a study using ANN for the scope of cost estimation or cost analysis, the trend of highway construction costs was analyzed in Louisiana, USA, and future cost trends were predicted [12]. In Australia, the total life cycle cost of construction work was calculated using the concept of major cost factors [13]. In Egypt, on-site indirect costs were predicted for a dam project [14], and in Turkey, ANN was applied to the cost estimation of a building structure system [15].  
[PERSON] [16] proposed an automated cost estimation system for low-rise steel structures based on ANN, where design variables such as area, perimeter length, height, and load were mainly used. The mean absolute error cost estimation using these variables in ANN and regression were 11% and 15%, respectively. As can be seen in various research cases, ANN presents fairly good results in predicting construction costs.  
Most of the existing research cases involved various ANN analysis methods that could more accurately predict total construction costs. Research on how to predict multiple construction costs at the same time, such as subconstruction costs, is still lacking. The method of predicting multiple construction costs at the same time can be used in various cases for predicting detailed construction cost factors that cannot be directly calculated, in addition to predicting multiple sub-construction costs.