ABSTRACT Machine learning techniques have been used for predicting facility-related costs but there is a lack of research on developing machine learning models for the complete life-cycle cost (LCC) analysis of facilities. This research aims to systematically investigate the feasibility of forecasting facilities' LCC by implementing machine learning on historical data. The authors propose a comprehensive and generalizable framework for developing facility LCC analysis machine learning models. This framework specifies the data requirements, methods, and expected results in each step of the model development process. First, a literature review and a questionnaire survey were conducted to determine the independent variables affecting facility LCC and to identify the potential data sources. The process of using raw data to derive LCC components is then discussed. Finally, a proof-of-concept case study was conducted on a university campus to demonstrate the application of the proposed framework. This research concludes that current building systems already contain the data for LCC analysis and that the proposed framework is effective in facility LCC prediction.

中文翻译：

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为设施生命周期成本分析开发机器学习模型的框架

摘要 机器学习技术已被用于预测与设施相关的成本，但缺乏开发用于设施完整生命周期成本 (LCC) 分析的机器学习模型的研究。本研究旨在通过对历史数据实施机器学习，系统地研究预测设施 LCC 的可行性。作者提出了一个全面且可推广的框架，用于开发设施 LCC 分析机器学习模型。该框架规定了模型开发过程每个步骤中的数据要求、方法和预期结果。首先，进行了文献回顾和问卷调查，以确定影响设施 LCC 的自变量并确定潜在的数据来源。然后讨论使用原始数据导出 LCC 组件的过程。最后，在大学校园内进行了概念验证案例研究，以展示所提议框架的应用。本研究得出的结论是，当前的建筑系统已经包含用于 LCC 分析的数据，并且提议的框架在设施 LCC 预测中是有效的。