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Construction of a Risk Assessment System for Green Transformation of Urban Buildings Based on K-means Clustering

In: Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026)

Author

Listed:
  • Lushan Shi

    (Xiamen Institute of Technology, School of Architecture and Civil Engineering)

Abstract

In the context of the new era, people’s yearning for a better life requires the construction field to provide a more comfortable and healthy living environment in a greener, environmentally friendly and efficient way. The green transformation of existing buildings often involves more stakeholders, and the risk factors associated with them are also more difficult to manage due to complex interest relationships, which greatly inhibits the enthusiasm of project sponsors to invest and residents to transform. In this paper, the k-means clustering algorithm is used to conduct risk network centrality analysis, risk network connection hub analysis, and risk factor influence analysis to study the role and influence of risk factors related to various stakeholders in the structure and dissemination of risk networks. It is found that there is a lack of effective daily operation feedback mechanism, uneven ability of management personnel, poor professional ability, lack of relevant professional and technical personnel, construction funds cannot be in place in time, and engineering changes. These risks occur most frequently in the green transformation of urban buildings. Risks with the characteristics of connecting hubs have a greater influence on the spread of risk networks and the closeness of risk relationships than risks with the characteristics of risk sources.

Suggested Citation

  • Lushan Shi, 2026. "Construction of a Risk Assessment System for Green Transformation of Urban Buildings Based on K-means Clustering," Advances in Economics, Business and Management Research, in: Ljiljana Trajkovic & José Alfredo F. Costa & Zaher Al Aghbari & Nor Azman Ismail & Dariusz Jacek Jak (ed.), Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026), pages 27-35, Springer.
  • Handle: RePEc:spr:advbcp:978-94-6239-689-0_4
    DOI: 10.2991/978-94-6239-689-0_4
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