The recent shortage of young skilled laborers is one of the impending issues facing the global construction industry. To address these issues, the prefabricated external insulation system (PEIS) can be suggested as an alternative. However, before applying it to construction projects, a construction productivity analysis is difficult due to the complexity of simulation modeling and the absence of real data. Thus, this paper aims to develop a learning-based productivity prediction model for PEIS using machine learning. This describes a learning-based productivity prediction model for PEIS using machine learning and consists of three steps: (i) Establishment of data, (ii) Development of activity cycle diagram for PEIS, and (iii) Prediction model for productivity analysis. The prediction model has a precision rate of 99.09%. This paper contributes to the literature by developing the possibility of a quick analysis of construction productivity without real data through a machine learning approach.

近期年轻技术工人的短缺是全球建筑业面临的迫在眉睫的问题之一。为了解决这些问题，可以建议使用预制外保温系统 (PEIS) 作为替代方案。然而，在将其应用于建筑项目之前，由于模拟建模的复杂性和缺乏真实数据，建筑生产力分析很困难。因此，本文旨在使用机器学习开发一种基于学习的 PEIS 生产力预测模型。这描述了一种使用机器学习的基于学习的 PEIS 生产力预测模型，包括三个步骤：（i）数据的建立，（ii）PEIS 活动周期图的开发，以及（iii）生产力分析的预测模型。预测模型的准确率为99.09%。