Artificial Intelligence (AI) has a great impact on increasing productivity and economic growth in many sectors. However, in the construction industry, AI is still limited to research and few pilot projects. This study aims to depict the current rate of AI adoption in the industry and understand the obstacles that are hindering the required changes in the companies' business models. The data are collected through a tailored questionnaire sent to experts and practitioners in the field. The results show that labourskilled shortage, data quality, cost-benefit and lack of case studies and standards have been identified as major issues. The findings help to understand the needs of construction practitioners and propose possible solutions.
EU building sector consists mainly of outdated and inefficient properties with high energy consumption. Hence, building retrofit is being emphasized as a feasible alternative for addressing existing challenges, taking lots of time, effort, resources, and expertise in its traditional form. Conventional case-based retrofit scenarios fail to deliver quick and objective solutions for massive datasets. This research benefits from Artificial Intelligence, particularly clustering techniques, to enhance strategic decision-making for building retrofit and solve the shortcomings of conventional methods. It connects the dispersed Italian databases (CENED and TABULA) and determines desired building technology and retrofit strategy to obtain an optimum energy label.
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