## 1 Introduction  
The accurate cost estimation of a construction project plays a very important role in successfully completing the project. Any construction cost estimation is developed based on specific parameters related to construction cost. However, in the early stage, it is not easy to accurately estimate the construction cost because the information necessary to predict the construction cost has not yet been determined. Therefore, research on the prediction of construction cost in the early stage has attracted the attention of many researchers [1, 2]. [PERSON] et al. [1] has studied 92 papers published from 1985 to 2020 dealing with construction cost estimation. Most of the proposed estimation techniques tried to present a construction cost prediction model that can be used in the pre-bidding stage and were expected to be used as a means to support the decision making of managers. These models use similar past project information to estimate construction costs. The most popular machine learning (ML) techniques that were used in the reviewed papers are the artificial neural networks (ANN) and regression analysis (RA), respectively. In the construction management, the artificial neural networks (ANN) and the support vector machine (SVM) were the most common machine learning techniques [1]. These ML techniques, such as ANN and SVM, use a historical database to predict the construction cost. Most of these papers used only a single model or a hybrid model that improved a single model. Each model has its own advantages and disadvantages. For example, multi-layer perceptron (MLP) is good at handling noisy data but has the problems of overfitting and becoming trapped at the local minimum, and SVM selects the best hyperplane that is less prone to overfitting [3]. Therefore, it may be possible to produce better results by combining several models rather than using one model. Ensemble learning that typically generates several models that are combined to make a prediction has recently attracted more attention in the field of ML. The use of ensemble methods generally has the advantage of increasing accuracy and robustness compared to using a single model [4, 5, 6]. The conventional ensemble methods include bagging-, boosting- and stacking-based methods [5]. Recently, the ensemble technique has been applied to various construction-related fields, including real estate appraisal [7], housing price prediction [8], energy consumption forecasting [9; 10; 11], energy performance prediction [12], the prediction of high-performance concrete compressive strength [13], etc. Stacking [14], also called meta ensembling or stacked generalization, is another well-known method used to increase diversity and generalization. Stacking combines individual models as a base learner and trains a meta learner with the output of the base learners. Although it is applied to the problem of classification, the results [15] of comparing the performance of many ML models show that different stacking approaches give a relatively better performance. In the context of ensemble learning regression, there has been work that proved that, if the stacking model is properly combined, the squared error of the prediction can be smaller than the average squared error of the base learners [5]. In terms of the performance on network intrusion detection systems, stacking is the only method that was able to reduce the false positive rate by a relatively high amount [16]. Although the ML ensemble algorithm is being actively introduced in many fields, there is a lack of applications of the ML ensemble algorithm to the construction estimation domain. [PERSON] et al. [17] have suggested a hybrid boosting-based model--natural gradient boosting and light gradient boosting--to improve the accuracy and reliability of the construction cost estimates during the VE phase. [PERSON] et al. [18] have applied a stacking ensemble ML algorithm for predicting the cost of highway construction projects.